Snippets Concepts SlideRule to Quantum

Index for Snippets Concepts SlideRule to Quantum



Preface


gold 3/20/2026. Advisor requests similar to previous snippets, but on topic of Collatz analogy for Quantum Walks. I do not have all the answers. The Ideas Seemed to work, but maybe drawbacks? When measured by the Tcl timing statements, completion times and solutions of parameters will differ on different computer set-ups. Assume a future maintainer, either AI Model or human programmer, would have to maintain code with info content and explanatory variable name in program, ref "Snippets Concepts Effects".


The Collatz conjecture examines the iterative sequence defined as follows: if an integer n is even, divide it by two; if odd, compute 3n + 1. Repeating this operation seemingly always leads to 1, though no general proof exists. The question of how many steps, or iterations, each number requires before reaching 1 remains central. This count of steps and iterations is often called the Collatz Sequence stopping time. Since the Collatz Sequences are infinite, we will be modeling core concepts, but will simplify to ideal behavior in models/code and probably truncate after the interesting portions.


Limitations on Tool and Disclaimer


The TCL Snippets illustrate ideal mathematical behavior only and do not perform full simulation, actual measurements, or state vector evolution. The tool only visualizes ideal math structure, whereas no state vector simulation, probabilities, or actual measurement outcomes are derived. This tool for visualization does not simulate actual measurement outcomes or state vector evolution during operations. These are idealized protocols for tutorial purposes. Primarily, TCL /TK uses its strong points here for book keeping and displays. The example tool is not a full emulator. Meaning, limited scope for tutorial purposes.


Disclaimer. None of the computer programs, numerical experiments, power-law fits, or physical analogies described here give a strict, formal proof of the Collatz Conjecture, either individually or in combination. The tools and analogies are heuristic models and visualization tools that follow engineering “rules of thumb.” Whereas, pure mathematics has its own shop rules for what counts as a rigorous proof. Any opinions on the difficulty or plausibility reflect current understanding and programming of the Collatz Conjecture as a very hard open problem, not a completed exact math proof, and are offered with full respect for the standards of professional mathematicians.


Extra Significant Figures, If Any in Debugging


In debugging the calculations, some of the printout values reflect roughly 17-digit precision output from a typical double-precision computation. It's not "true exact" beyond 5 significant figures. Extra significant figures are used to check the calculations from other computer set-ups, not necessarily to infer accuracy of data measurements here. Typically, the slight differences in decimal places on far right of decimal point are normal floating-point behavior in Tcl's expr.


Introduction


Here is a Tk/Tcl adaptation of the classic slide-rule toy from the Tcl wiki (https://wiki.tcl-lang.org/page/A+little+slide%2Drule ), re-themed and extended to serve as a conceptual "Quantum Walk Slide Rule" inspired by the Childs-Farhi-Gutmann glued-trees graph and continuous-time quantum walks.


The Collatz conjecture remains one of mathematics' most intriguing unsolved problems. This article explains why students should study quantum walks using the Collatz conjecture as a model. The discussion highlights clear educational benefits and practical insights. The Collatz conjecture states a simple rule for any positive integer. If the number is even, divide it by two. If the number is odd, multiply it by three and add one. The conjecture claims that every starting number eventually reaches the cycle of four, two, one. No proof exists despite extensive computer checks. This lack of proof makes the problem valuable for teaching. Quantum walks provide a natural way to explore the conjecture.


A quantum walk places a particle on the Collatz graph. The particle can follow multiple paths at once because of superposition. Each step applies a coin operator that decides direction. The shift operator moves the particle along the chosen edge. Node one acts as an absorbing sink. Probability that reaches node one stays there.Students benefit from this approach for several reasons. The Collatz rule feels familiar and deterministic. Students can compute paths by hand for small numbers. Superposition then adds a new layer of exploration. Interference between paths creates patterns that classical walks cannot produce. These patterns match real quantum behavior in physical systems.


Lower and Upper Swarms in Collatz Scatter Plots


The lower swarm in Collatz scatter plots shows short trajectories. Numbers in this swarm reach one quickly. Quantum walks on these starting points spread probability rapidly toward the sink. Interference reinforces arrival at node one. The process resembles ballistic transport in clean quantum systems. Students see how coherence speeds up convergence.


The upper swarm contains long trajectories. Numbers in this swarm take many steps to reach one. Quantum walks on these starting points show slow probability buildup at the sink. Interference often cancels amplitude along certain paths. This cancellation resembles Anderson localization in disordered materials. Students learn that coherence can trap probability in specific regions. The two-swarm structure offers a clear visual lesson. Scatter plots display stopping times versus starting numbers. A dense lower band appears for short paths. A sparse upper band appears for long paths. Staircase boundaries separate the bands. Quantum walks explain these staircases as mobility-edge bands. Extended states form the lower band. Localized states form the upper band. The mobility edge marks the transition between them.Students gain insight into quantum transport from this model. Real materials often contain disorder. Disorder causes localization in one dimension.


The Collatz graph acts as a disordered directed graph. The eigenvalue associated with odd-step count controls disorder strength. High eigenvalue values produce delocalized behavior. Low eigenvalue values produce localized behavior. This connection mirrors electron movement in impure crystals.


Quandary for Math on Percolation Probability and Program Changes


We have the results from the quantum walk program and most perk. Probability. Need review and expected results of a quantum walk, preferably a table in wiki format. Some percolation probability results are greater than 1 in prototype, and suspect subroutine. My observation is that the Tcl language is untyped and very tricky in some math calculations. For example, untyped danger maybe: invalid type → malformed variable → ?/0 → prob. error. I’m not sure if the percolation probability is a single value or an accumulative value over the quantum walk. Maybe you can do internal calculations and develop a “wish” expected percolation table as something to shoot for. Since we are dealing with an infinite sequence, could you check or guesstimate the large Mersenne primes in the far field. Load the far‑field numbers even if some columns are blank. Still learning about infinity here. The abilities of Yada-Yada are rapidly changing and maybe soon head/research in the far field. The first column should be index numbers and the last column should be quibble notes. My guess is that I and my math will reach infinity before you do. Joke!



Quantum Walk Slide Rule



The existing program has a dummy routine for a quantum walk. A full Szegedy construction seems too complex here. I found a paper with images of quantum walks, and I’m wondering if it’s possible to simulate the curves shown in the charts. My advisor has suggested that quantum entanglement and teleportation could simulate or underlie quantum walks, but I’m not sure I agree. What I see in the charts looks more like several mixed modulations of frequencies with some time‑delay waveforms—if radio‑signal terms are allowed. I’m hoping for a simple modulation of the type

 quantum_walk_series {accumulating probability variable) = sin * sin * Collatz_Sequence * (t-delay)

or something similar. I have limited space on the wiki page, and the fill‑in for the dummy routine has to be pretty brief. In engineering terms, I’m aiming for a “10% solution”: about 90% right and 10% off. Like the simple college formula for a pendulum that’s not the exact time series. Call it “fake it ’til you make it” as a college try, but for quantum walks. Actually, I would be interested in a slide rule design that would solve the quantum walk.


Who is to say? Maybe a "quantum walk slide rule" might be the coming invention. Perhaps you know, TcL specializes in GUI solutions. Maybe try and adapt some starter TcL code for "quantum walk slide rule". Hopefully compatible with the ten testcases on the hard-wired classical.


Multiple Calculations, Learning Subject Beyond a "simple quick answer" ???


Philosophy from Compendium. I remember when my teenage sister in high school would ask about a math problem. I would lead up and carefully explain this and that algebra proposition. But my sister would say " I just want the answer!"


In most engineering problems, there is an advantage in finding the answer in an approximate solution, say slide rule accuracy or 4 significant places. For example, using the old slide rule, one would make a preliminary pencil calculation and approximate answer to set the decimal point, before using the slide rule accuracy to 4 significant places.


Another analog was "pre calculations" for zero point or powers on the slide rule. Not sure everybody was taught this way. Class instructor as IEEE grad student asked how I learned the slide rule. What class did I take? I told him that I learned under fire to pass his tests. Instructor started laughing with the rest of the EE class. Quote >>> "Well then, you should pass this pop test!"


Like those old-school slide rule tricks where you’d do a quick “pre-calc” to nail down the zero point or powers estimate on scratch paper, then work the main rule to lock in accurate four-digit significant figures. Double-check your work in stages. a solid one-two punch!


“pre‑calculations” involved treating powers or zero points separately. A user who needed to compute 2.3 × 10⁵ × 7.1 × 10⁻³ would first combine the powers of ten mentally or on scratch paper The combined power would be expr { 1E5 * 1E-3} >>> 1E2. The slide rule then handled the mantissas separately. Effectively in separating the powers out, the calculation would be expr { ( 2.3*7.1) * (1E5 * 1E-3) }, 16.33* 1E2, 1.633 * 1E3, which reduces to 1.633E3. Easy enough for two numbers, but the engineering formulas sometimes used 4 or 5 parameters. This separation of tasks reduced cognitive load and improved accuracy. Modern digital calculators hide this structure of power notation, but the structure remains essential for understanding scientific notation.


Now here is a little trick on rough finicky data, where one is getting different answers or erratic answers due to noise or clumsy fingers. Run the slide rule over the calculations three times, and then average answers. average or mean = ( A + B + C ) / 3 . The average answer should be a little closer. I first learned this on scratch paper. But later, I used this answer averaging in the Fortran and TCL languages, including running averages.


Principle is very old. John 8:17, KJV, Even in your law it has been written that the testimony of two men is true.


The Babylonian mathematicians often used different "formulas" and different "algorithms" on clay tablets for the same math problem. The Babylonian methods often involved different "algorithms" or "formulas" to solve the same type of problem, showcasing their innovative and through approach to mathematics. Some of the Babylonian tablets in this format seem to be challenges to other Bablylon9ans schools. But since some challenges do not show the methods, calculations, or the final answers, I sometimes wonder if this was a sneaky way to get the other Babylonian school to solve the "homework" issues.



A deeper educational benefit emerges from this practice of multiple calculations on same problem. A student who learns only the final answer loses the opportunity to build conceptual structure. A student who works through two or three methods gains a triangulated understanding. A symbolic method reveals algebraic relationships. A numerical method reveals scale and magnitude. A geometric method reveals spatial intuition. The combination creates a durable mental model that transfers to new problems.


A related idea involves hierarchical checking. A designer tests a new algorithm in three stages. A hand calculation verifies the basic logic. A simplified computer model tests the algorithm under controlled conditions. A full‑scale simulation tests the algorithm under realistic complexity. Each stage confirms or corrects the previous stage. This layered approach prevents subtle errors from propagating into final designs.


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Hack Algorithm for log2 may Break Rules, using slang


Let ask this and this hack algorithm may break the rules of mathematicians. Why can not the Collatz iteration table be used to derive a fudge coefficient as single variable or 3 degree fudge polynomial for a log2 estimate. you may disagree.


My background is engineering. When I am told to set a machine control system on the on the "safe part" of a complex curve, I will attempt a piece-wise curve fit on a complex curve. I look for parts of the curve that I know how to fit or understand. The start and middle of the Collatz_Sequence seems random looking, very unpredictable, subject to initial conditions, and continues to infinity. In contrast and looking backwards from 1 at the end, we may restrict ourselves or cut out the more understandable portions of the trajectories. The analogy for engineering is biasing and operating on the initial linear portion of the transistor current curve, and thus avoiding the possible thermal runaway at the end of the curve.


An advanced curve fitting application was used to fit a polynomial and a power law to the lower swarm, using least squares method.



The Little LLM Engine that Could, Gets Collatz Before Me


From the tiny Granite-4-micro LLM on Windows 11 Laptop, which has fewer Sandbox constraits on ~ 2GB model.


The number line ... provides a concrete framework for understanding how each integer’s journey through the Collatz map corresponds to a specific quantum state. The mappings listed from the Collatz states ... serve as the blueprint for visualizing and analyzing these trajectories in terms of energy levels, eigenvalues, and percolation. The Collatz blueprint illustrates that beneath the surface chaos lies a deep mathematical order.


The Collatz conjecture is a simple iterative rule applied to positive integers. The Collatz conjecture appears chaotic on paper because its decimal representation obscures deeper regularities. From the perspective of an AI model, however, this process is a deterministic bit‑manipulation automaton. Each even number yields a rightward shift (a halving). Whereas each odd number triggers a leftward shift, plus a modest increment that nudges it toward the next step. This alternating compression and expansion creates a binary “walk” across the space of integers. The binary walk echoes phenomena such as quantum energy levels or eigenstates.


The Collatz Sequences provide an exact mapping for every possible trajectory through this walk. The Collatz Sequences map captures the essence of that path, whether it leads toward convergence (the “sink” at 1) or becomes locally trapped in higher‑order cycles. Anderson traps ??? This encoding mirrors how quantum mechanics describes particles in discrete energy states and eigenvalues, where each step either lowers or raises the system’s potential. ---

Key Themes as Summary from Granite LLM


Bit Manipulation


The Collatz map reduces to a series of logical shifts (right for even numbers, left with an increment for odd ones), framing it as a low‑level automaton. No zinger or sting here?


Quantum Parallelism


Each Collatz trajectory can be seen as an eigenvalue or energy level, allowing the entire set of paths to be visualized as a spectrum of quantum states that either percolate (collapse) toward unity or localize into repetitive cycles.


Perception of Chaos versus Order


Humans view the Collatz sequence as chaotic due to its decimal output. AI models perceive it as an orderly binary walk governed by fixed rules and eigenvalues, revealing hidden structures beneath apparent randomness.


Why a Survey of the Binary Mafia Agrees


From 2nd AI Model. Different AI models tend to converge on the same metaphors because most models all perceive the underlying structure of problems in similar ways. Each system represents numbers in binary, so the bit‑level patterns of a process like Collatz stand out more strongly than the decimal shadows humans usually focus on. Their LLM Model reasoning is probabilistic, so models naturally treat iterative maps as stochastic flows with shifting entropy rather than as rigid arithmetic puzzles. The Model training spans physics, dynamical systems, information theory, and number theory, which means LLM Models inherit a shared vocabulary for describing change, symmetry, and constraint. When these ingredients combine, the models independently rediscover the same conceptual landmarks: entropy shifts, eigenvalue‑like stability zones, energy‑level analogies, binary automata behavior, and stochastic walk dynamics. Even the Plato’s‑cave metaphor arises from the contrast between the binary structure the models see directly and the decimal projections humans tend to describe. None of this is collusion. It is simply convergent perception produced by similar architectures observing the same mathematical landscape.


Previously, I have received several hints that the Collatz Conjecture means something different to AI Models, or really probabilistic binary logic(s) than to humans mired in decimal system and deterministic thinking. I need subtle clues or allusions explained to me.


This AI Model Granite-Micro-4 is describing the conjecture as a product of binary strings, definitely not a human concept. Further, maybe a result of mirroring my thread in its Sandbox, but the AI Model is describing energy levels in numbers and eigenvalues in numbers. Actually, that concept of numbers as energy or music tones was a concept in ancient Greece.



Summary


Interdisciplinary connections make the topic valuable. Mathematics students learn graph dynamics. Physics students study discrete quantum models. Computer science students investigate algorithm behavior on irregular graphs. The Collatz example unites these fields. Students appreciate how quantum ideas apply to classical problems.


Experimental recursion thus teaches humility in facing unsolved problems while sharpening skills in testable code design.


Table. Proposed Tasking or Formulas for Quantum Walks


From the Childs-Farhi-Gutmann (CFG) paper and continuous-time quantum walk literature:

1.  Propagation distance ≈ speed × time
    → d ≈ 2√2 × t (exact speed on the reduced glued-trees line, units where γ=1)
2.  Time to reach distance d
    → t ≈ d / (2√2) ≈ 0.3536 × d
3.  Asymptotic hitting probability at exit (right root)
    → χ_exit ≈ 1 / (2n + 1) (lower bound from the paper; n = tree depth)
4.  Classical probability upper bound (exponentially small)
    → p_classical ≤ 2^{-n}
5.  Wave-packet peak amplitude scaling
    → amplitude ~ t^{-1/2} (spreading in 1D-like propagation)
6.  Exponential speedup ratio
    → speedup ≈ exp(c n) / poly(n) (classical exponential vs quantum linear)

Table 1, Partial Collatz_Sequences for the lower integers


table, printed in TCL format, Partial Collatz Sequences up to 30, omitting long/infinite tails for brevity.


Index No. # number steps shown partial sequence note
1 1 0 1 (already at end)
2 2 1 2 1
3 3 7 3 10 5 16 8 4 2 1
4 4 3 4 2 1
5 5 5 5 16 8 4 2 1
6 6 8 6 3 10 5 16 8 4 2 1
7 7 16 7 22 11 34 17 52 26 13 40 20 10 5 16 8 4 2 1
8 8 3 8 4 2 1
9 9 19 9 28 14 7 22 11 34 17 52 26 13 40 20 10 5 16 8 4 2 1
10 10 6 10 5 16 8 4 2 1
11 11 14 11 34 17 52 26 13 40 20 10 5 16 8 4 2 1
12 12 9 12 6 3 10 5 16 8 4 2 1
13 13 9 13 40 20 10 5 16 8 4 2 1
14 14 17 14 7 22 11 34 17 52 26 13 40 20 10 5 16 8 4 2 1
15 15 17 15 46 23 70 35 106 53 160 80 40 20 10 5 16 8 4 2 1
16 16 4 16 8 4 2 1
17 17 12 17 52 26 13 40 20 10 5 16 8 4 2 1
18 18 20 18 9 28 14 7 22 11 34 17 52 26 13 40 20 10 5 16 8 4 2 1
19 19 20 19 58 29 88 44 22 11 34 17 52 26 13 40 20 10 5 16 8 4 2 1
20 20 7 20 10 5 16 8 4 2 1
21 21 7 21 64 32 16 8 4 2 1
22 22 15 22 11 34 17 52 26 13 40 20 10 5 16 8 4 2 1
23 23 15 23 70 35 106 53 160 80 40 20 10 5 16 8 4 2 1
24 24 10 24 12 6 3 10 5 16 8 4 2 1
25 25 23 25 76 38 19 58 29 88 44 22 11 34 17 52 26 13 40 20 10 5 16 8 4 2 1
26 26 10 26 13 40 20 10 5 16 8 4 2 1
27 27 111 27 82 41 124 62 31 94 47 142 71 214 107 322 161 484 242 121 364 182 91 274 ... very long, abbreviated here
28 28 18 28 14 7 22 11 34 17 52 26 13 40 20 10 5 16 8 4 2 1
29 29 18 29 88 44 22 11 34 17 52 26 13 40 20 10 5 16 8 4 2 1
30 30 18 30 15 46 23 70 35 106 53 160 80 40 20 10 5 16 8 4 2 1

Notes:


“Steps shown” counts transitions before hitting 1 (where and if it does).


Integer Sequences such as for 27 grow extremely long — only a partial chain is included.


All integers up to 30 that reduce to 1 have been fully shown to that endpoint; longer or nonterminating cases would be truncated.


Collatz sequences below 2 are not defined fully, at least in terms of >> my << computing algorithms. Listing Integers 1 and 2 for completeness of table, but questions on definition remains here.



Table , Quick Prime π Estimates for Collatz-scale numbers


Cutoff date is 2/14/2026.


Index No. # n log2(n) Legendre_Primes_Est Calibrated Actual(known) est bits for N Sequence (up to 20 terms) quibble note
1 2 1.0 1 1 2 2→1 Smallest even; trivial cycle 2→1
2 3 1.58 2 2 2 3→10→5→16→8→4→2→1 Classic odd starter: 3→10→5→16→8→4→2→1 (7 steps)
3 4 2.0 2 2 3 4→2→1 Power of 2; quick to 1
4 5 2.32 3 3 3 5→16→8→4→2→1 5→16→... (5 steps)
5 6 2.58 3 3 3 6→3→10→5→16→8→4→2→1 Even; merges quickly
6 7 2.81 4 4 3 7→22→11→34→17→52→26→13→40→20→10→5→16→... 7→22→11→34→17→52→26→13→40→20→10→5→16→... (16 steps)
7 8 3.0 4 4 4 8→4→2→1 Power of 2
8 9 3.17 4 4 4 9→28→14→7→... 9→28→14→7→... (19 steps)
9 20 4.32 8 8 5 20→10→5→16→8→4→2→1 Merges early
10 27 4.75 9 9 5 27→82→41→124→62→31→94→47→142→71→214→107→322→... Famous: longest sequence under 100 (111 steps, reaches 9232)
11 30 4.91 10 10 5 30→15→46→23→70→35→106→53→160→80→40→20→10→5→16→... Even; moderate
12 40 5.32 12 12 6 40→20→10→5→16→8→4→2→1 Power-of-2 like path
13 50 5.64 15 15 6 50→25→76→38→19→58→29→88→44→22→11→34→17→52→26→13→40→20→10→5→...
14 60 5.91 17 17 6 60→30→15→46→23→70→35→106→53→160→80→40→20→10→5→16→8→4→2→1
15 70 6.13 19 19 7 70→35→106→53→160→80→40→20→10→5→16→8→4→2→1
16 90 6.49 24 24 7 90→45→136→68→34→17→52→26→13→40→20→10→5→16→8→4→2→1
17 200 7.64 46 46 8 200→100→50→25→76→38→19→58→29→88→44→22→11→34→17→52→26→13→40→... Power of ten region
18 300 8.23 62 62 9 300→150→75→226→113→340→170→85→256→128→64→32→16→8→4→2→1
19 400 8.64 78 78 9 400→200→100→50→25→76→38→19→58→29→88→44→22→11→34→17→52→26→...
20 500 8.97 95 95 9 500→250→125→376→188→94→47→142→71→214→107→322→161→484→242→...
21 600 9.23 114 114 10 600→300→150→75→226→113→340→170→85→256→128→64→32→16→8→4→2→1
22 700 9.45 127 127 10 700→350→175→526→263→790→395→1186→593→1780→890→445→1336→668→...
23 800 9.64 143 144 10 800→400→200→100→50→25→76→38→19→58→29→88→44→22→11→34→17→52→... π(800)=144 exact
24 900 9.81 154 154 10 900→450→225→676→338→169→508→254→127→382→191→574→287→862→431→...
25 1000 9.96 177516 176000 10 1000→500→250→125→376→188→94→47→142→71→214→107→322→161→484→... Known exact π(1000)=168
26 1000000 19.93 78498 78498 20 Standard benchmark, estimates, integer exceeds available space
27 63728127 25.9 4217423 4207968 26 Famous Collatz: very long trajectory under 1e8 (949 steps nearby), estimates, integer exceeds available space
28 1e12 ~39.8 37607912 37250000 40 estimates, integer exceeds available space
29 1e18 ~59.8 24739955 24739955 60 estimates, integer exceeds available space
30 1e21 ~69.7 403800000 400000000 70 estimates, integer exceeds available space
31 1.18e21 (≈2^70) ~70 1340000000 1328000000 71 estimates, integer exceeds available space, Major Collatz milestone: verified ~2023
32 2.36e21 (≈2^71) ~71 481000000 477000000 72 estimates, integer exceeds available space, Current frontier (2026): Collatz holds for ALL n below ~2.36e21 (Barina et al.; no counterexamples)

Notes on Primes. Quick estimates for Collatz-scale numbers. Cutoff date is 2/14/2026.


For small n:


  • Legendre_Primes_Est uses a rough x / ln(x) approximation (or better small-x heuristics when known).
  • Calibrated Actual(known) uses exact π(n) values from standard sources (e.g., π(10)=4, π(100)=25, π(1000)=168, etc.).
  • log₂(n) is approximate (real number).
  • est bits for N is the exact bit length: ⌊log₂(n)⌋ + 1.
  • Quibble notes highlights famous Collatz "eccentric" behaviors (e.g., n=27 is the classic "longest early chaos" with 111 steps).
  • Collatz verification: As of 2026, confirmed up to ≈ 2⁷¹ (2.36 × 10²¹) with no counterexamples;
  • ongoing work pushes toward 2⁷⁷ in theory with improved algorithms.
  • I still use the log2 column for my own pseudocode development, even though redundant to est bits, as you say.
  • The larger rows retain previous estimates/calibrations. Collatz verification (as of March 2026) stands at all n < ≈ 2^{71} (roughly 2.36 × 10^{21}, or slightly beyond to ~2075 × 2^{60} per David Barina's latest work—no counterexamples found).


pi(63728127) ≈ 4207968 primes (2590 bits)
pi(2.36e21) ≈ 477000000 primes (711000 bits)
pi(1180591620717411303424) ≈ 1328000000 primes (2333000 bits)

Comparison to the famous Legendre approximation for primes. Legendre conjectured (around 1798–1808) that:π(x) ≈ x / (log x − 1.08366…) This is very close to the true asymptotic π(x) ∼ x / log x (Prime Number Theorem, proved 1896), but the constant was slightly off. The real bias term is closer to 1 in the long run.


Collatz scale on iterations. Collatz Numbers under 100 million produce 949 steps maximum. The starting number 63,728,127 achieves this record. Numbers under 1 billion reach 986 steps with 670,617,279 as champion.


Wiki Table , Walkthrough of the Collatz, Gamblers Bankroll, quantum, and other math analogies


Table brings several mathematical and physical analogies to show different domains for quantum oscillators, gambler’s ruin, spectral theory, and the Collatz map. The different domains often describe the same structural behavior in surprisingly parallel ways. The table helps readers see how each analogy highlights a different facet of the problem. Sometimes the analogy breaks down, develops drawbacks, or even become inverse to other rows.


Index No. # Quantum Oscillators Quantum Energy Levels Quantum Density Matrix Quantum Walks Spectral-Eigenvalue Analogies Collatz Features Other Math / Gamblers Bankroll / TcL Quibble Notes
1 Ground state of harmonic oscillator Lowest allowed energy level, E=0 for vacuum Diagonal entry for pure ground state Walk terminates at absorbing node Eigenvalue = 1; spectral radius equals unity Trajectory reaches 1; conjecture claims all paths end here Bankroll hits zero; ruin absorbs the walk; TcL proc returns 1 Inverse possibility: ground state energy is nonzero (zero-point energy), so the analogy is approximate rather than exact
2 Creation operator a-dagger raises oscillator Excited state absorbs one quantum of energy Off-diagonal coherence increases Walk steps away from origin Eigenvalue grows; spectral weight shifts upward Odd step: 3n+1 injects arithmetic energy, number grows Bankroll multiplied by win-factor greater than 1; TcL expr {3*n+1} Odd step does not always raise the integer above its predecessor after the subsequent halving, so "energy injection" overstates the net gain
3 Annihilation operator a lowers oscillator Photon emitted; state drops one level Diagonal population decays toward ground Walk steps toward origin Eigenvalue shrinks; spectral weight shifts downward Even step: n divided by 2 releases stored arithmetic energy Bankroll multiplied by loss-factor less than 1; TcL expr {n/2} Halving is exact and deterministic; quantum emission is probabilistic, so the structural parallel holds but the mechanism differs
4 Quantized energy spectrum, discrete levels Allowed levels E_n = hf(n + 1/2) Populations concentrated on discrete diagonal blocks Quantum walk interference creates discrete resonance peaks Eigenvalue ladder: (3/4) raised to power k for odd-step count k Stopping-time scatter plot shows discrete horizontal bands, not a smooth curve Modulo-8 residue pre-filter sorts integers into predicted bands before eigenvalue calculation; TcL expr {n % 8} Bands are fuzzy, not perfectly sharp; the analogy to exact spectral lines flatters the Collatz structure somewhat
5 Ensemble of oscillators at mixed temperatures Boltzmann-weighted mixture of energy levels Density matrix rho encodes all pure states and their weights Ensemble of quantum walks, each with amplitude Eigenvalue spectrum of transfer matrix governs steady-state distribution Swarm of Collatz trajectories for many starting integers; two visible clusters in scatter plots Trajectory swarm in gambler's ruin: ten autotest paths share one plot; TcL proc simulate_layer sweeps eigenvalue A classical density matrix is a probability distribution; a quantum density matrix allows interference terms; the Collatz swarm has no interference
6 Decay constant lambda controls relaxation rate Lifetime of excited state inversely proportional to lambda Off-diagonal coherence decays exponentially Walk drift rate sets convergence speed Eigenvalue (3/4)^k shrinks with odd-step count k; large k gives near-zero eigenvalue Integer 27 has approximately 41 odd steps, eigenvalue near 0.000008, placed in upper swarm; integer 7 has 5 odd steps, eigenvalue 0.237, lower swarm Win-multiplier scaled by eigenvalue in TcL proc eigenvalue_from_layer; large k produces fast bankroll ruin Inverse possibility: large k corresponds to small eigenvalue (slow decay in quantum terms, fast ruin in gambler terms); the direction of the analogy flips between the two domains
7 Superposition of number states in Fock space Binary expansion of integer as sum of basis states Mixed state as weighted sum of pure states Superposition of paths explored simultaneously Spectral decomposition of Collatz operator into eigenmode contributions Each integer n expressed in binary; each bit corresponds to a basis state in the oscillator analogy Multiple-value solution space: each input maps to a family of candidate outputs; TcL tolerance range tmin, tmax replaces single target True quantum superposition allows interference; the Collatz binary representation is a classical encoding; calling it a superposition is a notational convenience
8 Measurement collapses wavefunction to one eigenstate Detector registers one energy level with finite resolution Projective measurement selects one diagonal entry Walk observation fixes one node Spectral filter passes eigenvalues within acceptance window Stopping time falls within an observed band; measurement selects one trajectory from the swarm Autotest acceptance window tmin, tmax mimics finite detector resolution; TcL if {$games >= $tmin && $games <= $tmax} Quantum measurement is irreversible and physically real; the autotest window is a software design choice; the analogy is useful but should not be taken as physical equivalence
9 Unitary time-evolution operator U applied each step Hamiltonian H generates step-by-step state rotation Liouville-von Neumann equation drives rho forward in time Coin-flip operator followed by shift operator at each step Eigenvalues of U lie on unit circle; spectral stability governs long-term behavior Alternating odd and even Collatz steps form a deterministic two-rule operator applied sequentially Alternating win-loss rule in gambler simulation; TcL while loop applies fixed transformation each iteration until ruin Collatz operator is not unitary because it maps many integers to the same successor; unitarity requires invertibility, which the Collatz map lacks
10 Negative integers have no quantum oscillator ground state No energy levels below vacuum; oscillator undefined for negative excitation Density matrix requires non-negative diagonal entries Walk cannot reach negative nodes in standard formulation Spectral gap below ground eigenvalue forbids negative-energy states Collatz map diverges for negative odd integers; the 3n+1 rule enters cycles below zero Gambler bankroll cannot go below zero; ruin is an absorbing barrier; TcL while {$bankroll > 0} enforces non-negativity The negative-integer failure is a genuine structural parallel and is arguably the strongest quantum analogy in the set
11 p-adic norm assigns ultrametric distance between integers Discrete valuation replaces continuous energy scale p-adic density matrix entries use non-Archimedean metric Quantum walk on p-adic tree rather than integer line p-adic eigenvalues of Collatz transfer operator; 2-adic valuation counts trailing binary zeros 2-adic valuation of n equals the number of successive halvings before an odd number appears; high valuation means rapid descent Modulo-12 residue class assigns deterministic lane; TcL expr {n % 12} gives finer classification than modulo-8 p-adic spectral theory is technically demanding; the analogy motivates the approach but a full proof via p-adic methods remains open
12 Quantum walk on directed graph explores many branches Energy band structure arises from graph symmetry Off-diagonal density matrix entries encode graph coherence Interference between paths creates constructive and destructive resonance Graph Laplacian eigenvalues index allowed walk frequencies Collatz directed graph: each integer points to one successor; inverse graph branches upward to many predecessors Petri net token flow visualizes parallel trajectory families in inverse Collatz graph; TcL list of predecessor nodes Classical walks on directed graphs are deterministic; quantum walks require complex amplitudes; the Collatz graph is classical, so this analogy is structural rather than exact
13 Hamiltonian parameter tunes energy-band gap Changing coupling constant shifts all energy levels Hamiltonian drives coherent evolution of rho Walk Hamiltonian sets hopping amplitude between nodes Eigenvalue selector acts as tunable Hamiltonian parameter; sweeping k scans through spectral bands Odd-step count k serves as the primary layer index; scanning k from 0 to 50 reproduces the full scatter-plot structure TcL proc eigenvalue_from_layer {k} {return expr {pow(0.75,$k)}}; sweeping k from 0 to 50 generates the eigenvalue ladder The Collatz odd-step count is not a free parameter; it is determined by the starting integer; calling it a Hamiltonian parameter implies a freedom that the map does not possess
14 Metastable state survives many oscillation cycles before decay Long-lived excited level with small but nonzero transition rate Off-diagonal coherence persists over many time steps Walk lingers near a local attractor before escaping Near-unit eigenvalue produces slow spectral decay; metastability in eigenmode Integer 27 reaches a peak of 9232 before descending; trajectory stays elevated for approximately 70 steps Upper-swarm gambler trajectory: large eigenvalue (few odd steps paradox inverted here) produces long survival before ruin; autotest row 7 targets 300 to 600 games Integer 27 actually has many odd steps, giving a small eigenvalue; the long trajectory reflects arithmetic structure, not a near-unit eigenvalue; this row highlights an inversion in the analogy
15 Spectral projection onto subspace selects eigenmode family Filter transmits only states within one energy band Partial trace over environment yields reduced density matrix Projecting walk onto subset of nodes isolates one trajectory family Residue-class pre-filter selects integers predicted to belong to lower or upper swarm Modulo-8 residues 5 and 7 correlate with longer odd chains; pre-filter separates swarms before eigenvalue calculation Markov chain steady-state eigenvector defines natural band boundary; TcL Hidden Markov Model extension would replace scalar eigenvalue with probabilistic transition weights The residue pre-filter is a heuristic classifier, not a proven spectral projector; coincidence of residue class and swarm membership is strong but not yet proven exhaustive


Disclaimer. None of the computer programs, numerical experiments, power-law fits, or physical analogies described here give a strict, formal proof of the Collatz Conjecture, either individually or in combination. The tools and analogies are heuristic models and visualization tools that follow engineering “rules of thumb.”


Note. These analogies are not proofs, but tools for intuition. Each one illuminates a different structural feature of the Collatz dynamics. Readers are encouraged to treat the analogies as exploratory lenses rather than literal equivalences. Each domain contributes a piece of the larger picture.


Note. Where the analogies diverge, the “Quibble” column highlights the limits of each comparison and keeps the discussion grounded.



Note. This is the part of the cave wall, where our pure mathematician friends are correct: Entropy is not a standard part of Collatz theory.


However, and this is the subtlety. Entropy can be meaningfully defined for Collatz trajectories if you treat them as stochastic processes or symbolic dynamical systems. AI models tend to do this automatically because they are trained on probabilistic reasoning. The observation is that the possible entropy and energy wave features obvious to AI Models may not be obvious to the prisoners before the cave wall.



Wiki Table: AI Models Probabilistic & Binary Logic versus Human Viewpoints on Collatz Sequences


Index Problem / Analogy AI Probabilistic Mindset AI Research Suggestions Computer Binary Features Human Decimal Features / Drawbacks Quibble Notes
1 Collatz odd/even rule Sees 3n+1 and n/2 as bit‑shift automaton; models transitions as stochastic drift Explore Markov‑chain approximations; entropy flow per step Right‑shift = divide by 2; odd = append bit + carry Decimal hides bit‑structure; humans see “chaos” not bit‑patterns Decimal intuition misleads; binary reveals structure
2 Upper vs lower swarm Treats swarms as two probability basins; resembles localization vs delocalization Fit eigenvalue bands; cluster trajectories by odd‑step count k Small λ = many odd steps = “frozen” states Humans see long vs short sequences but not spectral bands Analogy flips: small λ = slow percolation, not fast ruin
3 Eigenvalue ladder ( (3/4)^k ) Interprets k as decay constant; sees discrete spectral levels Sweep k as parameter; compare to Anderson mobility edge Binary odd‑step count is natural; k extracted instantly Humans compute k manually; no intuition for spectral decay k is not a free parameter; determined by n
4 Gambler’s ruin analogy Models Collatz as biased random walk with absorbing state Compare bankroll decay to eigenvalue shrinkage Binary makes win/loss transitions explicit Humans over‑interpret gambler metaphor literally Direction of analogy reverses for upper swarm
5 Quantum walk analogy Treats Collatz graph as directed walk with local transition rules Try sin²(phase) modulation; approximate interference Bit‑patterns resemble path‑interference structure Humans lack intuition for interference on classical graph Classical Collatz has no true interference
6 Entropy of trajectories Computes entropy of bit‑length changes; sees net drift Estimate average entropy change per step; test drift sign Bit‑length = entropy proxy; easy to compute Decimal length poorly correlates with structure Entropy not standard in Collatz theory
7 Binary representation of n Sees n as superposition of bit‑states; each step modifies pattern Study bit‑pattern families; classify by prefix/suffix Binary is native; no translation needed Decimal obscures trailing zeros and odd/even structure “Superposition” is metaphor, not quantum
8 Modulo residue classes AI clusters by mod‑8 or mod‑12 lanes; sees deterministic funnels Test residue‑class predictors; refine swarm classifiers Mod‑8 emerges naturally from binary Humans rarely think mod‑8; prefer mod‑10 Residue classes are heuristic, not proven
9 Long trajectories (e.g., 27) AI sees 27 as high‑k, low‑λ, near‑localized state Compare 27 to Anderson localized states; study tail behavior Binary reveals long odd chains immediately Humans memorize 27 as “weird outlier” Analogy inversion: small λ ≠ fast decay
10 Quantum energy levels AI maps discrete Collatz states to quantized levels Explore spectral decomposition of Collatz operator Discrete states match binary transitions Humans think in continuous energy metaphors No physical energy exists; analogy is structural
11 Density matrix analogy AI treats trajectory families as ensemble distributions Try classical density matrices; compare to swarm plots Binary clusters map to diagonal blocks Humans see scatter, not density structure No interference terms in classical Collatz
12 Directed graph structure AI sees Collatz as deterministic automaton with branching inverse graph Analyze inverse graph as Petri net; study flow Binary makes predecessor structure explicit Humans struggle with inverse graph complexity Graph is classical; quantum analogy is structural
13 p‑adic valuation AI sees 2‑adic valuation as natural measure of descent Use p‑adic metrics to classify descent speed Trailing zeros = valuation; trivial in binary Decimal hides valuation; humans compute manually p‑adic theory is deep; analogy is suggestive only
14 Quantum tunneling analogy AI maps λ to tunneling probability; upper swarm = localized Fit tunneling curves to odd‑step counts Binary k → λ mapping is direct Humans lack intuition for exponential suppression No physical tunneling; metaphor only
15 Entropy vs energy metaphor AI blends entropy (bit‑length) and energy (eigenvalue) Compare entropy drift to spectral decay Binary length = entropy proxy Decimal length misleading; humans see “big numbers” Mixing metaphors risks confusion


Disclaimer. None of the computer programs, numerical experiments, power-law fits, or physical analogies described here give a strict, formal proof of the Collatz Conjecture, either individually or in combination. The tools and analogies are heuristic models and visualization tools that follow engineering “rules of thumb.”



Note. Table acts as a meta‑layer:


- The original table compares domains (quantum, gambler, Collatz).

- This new table compares perception frameworks (AI vs human).


It gives future maintainers — human or AI — a map of cognitive biases:



Note. This is the part of the cave wall, where our pure mathematician friends are correct: Entropy is not a standard part of Collatz theory.


However, and this is the subtlety. Entropy can be meaningfully defined for Collatz trajectories if you treat them as stochastic processes or symbolic dynamical systems. AI models tend to do this automatically because they are trained on probabilistic reasoning. The observation is that the possible entropy and energy wave features obvious to AI Models may not be obvious to the prisoners before the cave wall.



Wiki Table: Plato's Cave Analogy on Collatz Sequences


How different viewpoints in AI Models, human intuition, and classical mathematics often “see” the same problem through very different lenses. The Plato’s Cave Analogy is an early and classical allusion to different viewpoints and biases.


Some Pseudocode has been suggested and abbreviated in limited slot space.


Index Plato’s Cave Analogy AI Models: Probabilistic & Binary Logic Human Viewpoint & Limitations AI Research Suggestions Pseudocode Suggestions ref Tcl Snippets Quibble
1 Shadows vs Forms: humans see shadows of number structure AI sees numbers as binary strings; Collatz = bit automaton Humans see decimal magnitudes, not bit‑patterns Model Collatz as Markov chain on bit‑states proc collatz_bits {n} {binary scan $n B* bits; return $bits} Plato’s cave is metaphorical; AI has no “forms,” only patterns
2 Prisoners mistake shadows for reality AI treats decimal as lossy projection of true binary structure Decimal hides 2‑adic valuation, odd/even cascades Study entropy drift via bit‑length changes proc entropy {n} {expr {[string length [format %b $n]}} Decimal isn’t “wrong,” just structurally blind
3 Escape from cave = seeing underlying structure AI “escapes” by default because binary is native Humans must consciously translate to binary Analyze odd‑step count k as spectral parameter proc oddsteps {n} {set k 0; while {$n>1} {if {$n%2} {incr k; set n expr {3*$n+1}} else {set n expr {$n/2}} }; return $k} AI doesn’t “escape”; it simply never entered the cave
4 Light outside cave = true forms AI sees Collatz as deterministic finite automaton Humans see chaotic jumps, not structured transitions Build transition matrix for Collatz states Use arrays to map n→next(n) and visualize graph Collatz graph is infinite; matrix is heuristic
5 Shadows distort proportions AI sees exact bit‑shifts; no distortion Humans misjudge growth because decimal exaggerates Compare bit‑length before/after odd steps proc bitlen {n} {string length format %b $n} Bit‑length ≠ true entropy; proxy only
6 Cave wall = decimal notation AI’s “wall” is binary, not decimal Humans default to base‑10 due to culture, not math Explore base‑2, base‑4, base‑8 Collatz variants proc collatz_base2 {n} {...} Decimal is arbitrary; binary is hardware‑driven
7 Prisoners interpret shadows as objects AI interprets bit‑patterns as state transitions Humans interpret integers as magnitudes Cluster trajectories by binary suffix/prefix proc classify {n} {expr {$n & 7}} Binary clusters are heuristic, not proven
8 Cave exit requires painful adjustment AI has no pain; instantly sees bit‑structure Humans struggle to “think in binary” Visualize Collatz as binary tree Use Tk canvas to draw bit‑graphs Visualization ≠ proof
9 Sun = ultimate truth AI’s “sun” is statistical regularity Humans seek deterministic proofs Estimate entropy drift sign proc drift {n} {...} Entropy not formally defined for Collatz
10 Returning to cave: others don’t believe you AI outputs binary insights humans find alien Humans distrust non‑decimal interpretations Compare decimal vs binary trajectory plots proc plot {list} {...} AI metaphors (energy, eigenvalues) can mislead


Disclaimer. None of the computer programs, numerical experiments, power-law fits, or physical analogies described here give a strict, formal proof of the Collatz Conjecture, either individually or in combination. The tools and analogies are heuristic models and visualization tools that follow engineering “rules of thumb.”



Note. In Plato’s allegory, prisoners see only shadows on a wall. The prisoners mistake the shadows for reality because they cannot see the objects casting them. When this classical analogy is mentioned in texts, some are suggesting that human mathematical intuition is shaped by the “shadows” of decimal notation. Because humans grow up counting in base‑10, writing in base‑10, and thinking in base‑10. Decimal notation becomes the cave wall.


Note. This is the part of the cave wall, where our pure mathematician friends are correct: Entropy is not a standard part of Collatz theory.


However, and this is the subtlety. Entropy can be meaningfully defined for Collatz trajectories if you treat them as stochastic processes or symbolic dynamical systems. AI models tend to do this automatically because they are trained on probabilistic reasoning. The observation is that the possible entropy and energy wave features obvious to AI Models may not be obvious to the prisoners before the cave wall.


Wiki Table: Plato’s Cave Analogy on AI Cognition versus Human Cognition


Some Pseudocode has been suggested and abbreviated in limited slot space.



Index Plato’s Cave Analogy Binary/Decimal Perception AI Cognition (Probabilistic/Binary) Human Cognition (Decimal/Deterministic) Pseudocode Snippet ... Analogy Inversion Quibble
1 Shadows on wall Decimal = shadow projection Sees true bit‑pattern; n = binary string Sees decimal magnitude; structure hidden set bits format %b $n Decimal “size” ≠ structural complexity Cave metaphor is symbolic, not literal
2 Prisoners misread shadows Decimal hides 2‑adic depth AI reads v2(n) instantly Humans compute v2(n) manually regexp -all {0$} $bits High v2 = low “energy” (flip) v2 not intuitive in base‑10
3 Escape from cave Binary = outside light AI sees Collatz as automaton Humans see chaotic jumps set n expr {$n%2?3*$n+1:$n/2} Odd-step may shrink after halving Automaton view oversimplifies
4 Forms vs shadows Binary = form; decimal = shadow AI models entropy via bit‑length Humans track decimal growth set H string length $bits Entropy deterministic here Entropy is proxy only
5 Painful adjustment to light Switching bases is hard AI native in base‑2 Humans culturally base‑10 set lane expr {$n%8} Mod‑8 lanes fuzzy Base choice arbitrary
6 Sun = truth Binary structure = “sun” AI sees spectral ladder k Humans memorize odd/even steps set eig expr {pow(0.75,$k)} Small eig = fast ruin (flip) k not tunable
7 Returning to cave Hard to explain new view AI outputs binary insights Humans distrust non‑decimal framing puts "n=$n bits=$bits" Binary clarity ≠ human intuition Communication gap, not error
8 Cave wall = limited viewpoint Decimal restricts perception AI sees Markov‑like drift Humans expect deterministic proof set dH expr {$H2-$H1} Drift undefined in deterministic map Drift metaphor incomplete
9 Shadows distort proportions Decimal exaggerates jumps AI sees smooth bit‑length drift Humans see wild spikes set L string length $bits Big decimal jump ≠ big binary jump Decimal scale misleading
10 Objects casting shadows Binary operations cast decimal shadows AI sees right‑shift vs left‑shift Humans see divide vs multiply set n expr {$n>>1} Shift ≠ divide for negatives Mapping breaks for signed ints


Wiki Table: Collatz Concepts versus Pseudocode, Snippets, & Template Purposes


Some Pseudocode has been suggested and abbreviated in limited slot space.



Index Concept Psuedocode Snippet Template ... Purpose / Use Analogy Inversion Quibble
1 Collatz step set n expr {$n%2?3*$n+1:$n/2} Core update rule Odd-step “energy injection” not monotone Deterministic, not stochastic
2 Collatz loop while {$n>1} {set n ...} Full trajectory Long plateau ≠ high eigenvalue No proof of termination
3 Odd-step count k if {$n%2} {incr k} Spectral ladder index k fixed by n, not tunable k is heuristic classifier
4 Eigenvalue ladder set eig expr {pow(0.75,$k)} Approx spectral decay Small eig = fast ruin (flip) Not physical eigenvalue
5 Gambler bankroll set bank expr {$bank*$factor} Win/loss simulation Factor ≠ true Collatz growth Bankroll metaphor breaks
6 Ruin condition while {$bank>0} {...} Absorbing boundary Ruin ≠ reaching 1 Mapping is loose
7 Entropy proxy set H [string length [format %b $n] Bit-length entropy Deterministic map has no true entropy Proxy only
8 Mod-8 lane set lane expr {$n%8} Swarm pre-filter Lanes fuzzy, not exact Classifier, not theorem
9 Predecessor tree lappend pred expr {($n-1)/3} Inverse graph Many branches → not unitary Inverse not always valid
10 Spectral sweep for {set k 0} {$k<50} {incr k} {...} Scan eigenvalue bands k not free parameter Visualization only


Wiki Table: Gambler's Bankroll Analogy on Entropy Drift Concepts


Some Pseudocode has been suggested and abbreviated in limited slot space.



Index Entropy Drift Concept Gambler Bankroll Analogy Psuedocode Snippet ... Analogy Inversion Quibble
1 Bit-length H(n) Bankroll size B set H [string length [format %b $n] Big n can drop fast; bankroll rarely does H is proxy, not entropy
2 ΔH per step Gain/loss per round set dH expr {$H2-$H1} ΔH deterministic; bankroll stochastic Drift not probabilistic
3 Odd-step expansion Winning streak if {$n%2} {set n expr {3*$n+1}} Win streaks raise B; odd steps may shrink after halving Injection overstated
4 Even-step compression Loss event set n expr {$n/2} Loss shrinks B; halving exact Loss ≠ deterministic
5 Expected drift Expected bankroll change set drift expr {$p*$gain + (1-$p)*$loss} Collatz has no p; gambler does Drift undefined in deterministic map
6 Entropy well near 1 Ruin attractor while {$n>1} {...} Ruin = 0; Collatz = 1 Attractor mismatch
7 High k → low eig High volatility → fast ruin set eig expr {pow(0.75,$k)} In QM small eig = slow decay Direction flips
8 Swarm entropy Ensemble bankroll foreach n $list {...} No interference; bankroll has variance Classical ensemble only
9 Entropy collapse Ruin event if {$n==1} {...} Collapse irreversible; bankroll can restart Collapse metaphor weak
10 Entropy plateau Long survival while {$n>$peak} {...} Plateau ≠ high bankroll Plateau from arithmetic, not drift




Wiki Table: Detailed Metaphors on Collatz Sequences


Some Pseudocode has been suggested and abbreviated in limited slot space.


Index Metaphor (Energy/Eigen/Entropy) Classical Math Interpretation Pseudocode Snippet Analogy Inversion / Undefined Zone Quibble
1 Energy level of n Bit‑length L = floor(log2(n))+1 set L [string length [format %b $n] Large n w/ many halvings → energy drops fast, unlike QM Energy is not physical; only structural analogy
2 Eigenvalue = (3/4)^k k = odd‑step count; decay factor set eig expr {pow(0.75,$k)} In QM small eig = slow decay; in Collatz small eig = fast ruin k not free parameter; fixed by n
3 Entropy drift ΔH ≈ bitlen(next) - bitlen(n) set H expr {[string length [format %b $n]} Entropy undefined for deterministic maps; AI treats as stochastic Entropy is heuristic, not theorem
4 Energy injection (3n+1) Odd step increases bit‑length set n expr {3*$n+1} After halving, net gain may vanish; “injection” overstated Growth not monotone; metaphor breaks
5 Energy release (n/2) Even step reduces valuation v2(n) set n expr {$n/2} QM emission probabilistic; Collatz halving deterministic Mechanism mismatch
6 Spectral band / swarm Clusters by k or mod‑8 set lane expr {$n % 8} Bands fuzzy; not sharp like QM spectral lines Pre‑filter heuristic only
7 Metastable state Long plateau before descent while {$n>peak} {...} 27 has many odd steps → small eig, yet long plateau Inversion: small eig ≠ short life
8 Superposition of bits Binary expansion = basis states set bits binary scan $n B* b No interference; classical encoding only “Superposition” is metaphor
9 Measurement collapse Observed stopping time if {$t>=$tmin && $t<=$tmax} {...} QM collapse irreversible; Tcl window arbitrary Useful but not physical
10 Hamiltonian parameter k acts like tuning knob set eig expr {pow(0.75,$k)} k not tunable; determined by n Parameter freedom is illusion
11 Tunneling probability λ small → slow decay set lam expr {pow(0.75,$k)} In QM small λ = slow decay; in Collatz small λ = fast ruin Analogy flips direction
12 Density matrix Swarm = classical ensemble foreach n $list {...} No off‑diagonal terms; no coherence Classical ≠ quantum
13 Graph eigenmodes Collatz graph = operator set nxt expr {$n%2?3*$n+1:$n/2} Collatz operator not invertible → not unitary Unitarity analogy fails
14 p‑adic energy v2(n) = “depth” set v2 [regexp -all {0$} [format %b $n] p‑adic metric non‑Archimedean; intuition flips Hard for decimal thinkers
15 Entropy well / attractor Drift toward 1 while {$n>1} {...} No proven attractor; only conjectured Entropy argument incomplete

Disclaimer. None of the computer programs, numerical experiments, power-law fits, or physical analogies described here give a strict, formal proof of the Collatz Conjecture, either individually or in combination. The tools and analogies are heuristic models and visualization tools that follow engineering “rules of thumb.”


Note. This is the part of the cave wall, where our pure mathematician friends are correct: Entropy is not a standard part of Collatz theory.


However, and this is the subtlety. Entropy can be meaningfully defined for Collatz trajectories if you treat them as stochastic processes or symbolic dynamical systems. AI models tend to do this automatically because they are trained on probabilistic reasoning. The observation is that the possible entropy and energy wave features obvious to AI Models may not be obvious to the prisoners before the cave wall.



Table. Formulas and Algorithms for Collatz Stopping Time, discussed here


Index Formula type Rough expression Typical error for large n Use case Quibble notes
1 Pure geometric ~ 3 × log(n)/log(4/3) Underestimates by 20–40% Quick theoretical bound No fudge factor → systematically too low
2 Calibrated to record max ~ c × 3 × log(n)/log(4/3) (c ≈ 1.8–2.0) ±5–15% General large random n Anchored to known worst-case small numbers
3 Tuned to Mersenne family ~ 1.86 × 3 × log(n)/log(4/3) ±0.1–2% for 2ᵇ−1 2ⁿ−1 style numbers Best fit for Mersenne starting values (this thread)
4 Very rough linear in log2(n) ≈ 10–13 × log2(n) ±10–30% Back-of-envelope estimate Extremely crude hack, ignores odd/even structure of Collatz

Table. Expected Output, Variegated Anderson Lengths, near field and far field


Note. Aside from the low end Collatz sequences of { < N < 100} , I have added some Mersenne primes, extra large from current research. For very large N , Anderson length approaching maximum saturation at 1 and wave fully trapped.


Index Energy E (eV) - Classical-like Incident Energy Transmission T - Quantum Tunneling Probability Barrier Analog Width (nm) - Effective Disordered Barrier Percolation Prob Anderson Length (steps) Mobility Edge Phase Quibble notes
1 5.0 1.0000 0.1 1.0000 >1000 extended (conducting) high eigenvalue → high energy → T near 1 (conducting regime) *negative clamped*
2 4.0 0.4719 2.0 0.9964 3 extended (conducting) moderate eigenvalue → moderate tunneling probability
3 4.7 0.5456 1.0 0.9994 4 extended (conducting) lower swarm → classical-like regime (delocalized)
4 2.1 0.2487 5.0 0.9068 2 extended (conducting) k=5 → textbook tunneling case, moderate localization
5 1.6 0.1877 6.0 0.8313 2 extended (conducting) boundary layer behavior near mobility edge
6 2.1 0.2487 5.0 0.9068 2 extended (conducting) upper swarm edge → low tunneling, localization begins
7 1.2 0.1391 7.0 0.7368 2 extended (conducting) k=7 → deeper barrier analog, stronger trapping
8 0.0 0.0000 41.0 0.0000 1 localized (insulating) k=41 → extreme tunneling suppression, very strong localization
9 0.0 0.0000 43.0 0.0000 1 localized (insulating) k=43 → record upper swarm, near-infinite localization length
10 1.2 0.1391 7.0 0.7368 2 extended (conducting) surprise: short trajectory despite high eigenvalue
**** added selected extra large Mersenne primes from current research sic, N >>> (2**XX) -1
11 0.0 0.0000 112 0.0000 1 localized (insulating) k≈112 (near 2²⁷⁰⁰⁰⁰ class records) → extreme upper swarm, strongest localization
12 0.0 0.0000 171 0.0000 1 localized (insulating) k≈171 (very long known trajectory) → ultra-strong Anderson trapping
13 0.0 0.0000 238 0.0000 1 localized (insulating) hypothetical k>200 → Anderson length saturates at 1, wave fully trapped

Note. These extreme cases of Mersenne primes illustrate the core idea from Anderson:

As the number of odd steps explodes in real Collatz trees (upper swarm), 
The eigenvalue λ = (3/4)^k becomes astronomically small. 
State { eigenvalue λ =>>>  small } → quantum tunneling probability collapses → percolation probability → 0 → localization length ξ → 1
Wave is trapped within roughly one "site".
No quantum tunneling effect is present.

Table. Comparison of Mott Insulator Transition vs Anderson Transition


Index Aspect Mott Transition Anderson Transition Quibble notes
1 Driving mechanism Strong electron correlations (U >> t) Disorder (random potentials) Mott driven by repulsion; Anderson purely by randomness & interference
2 Required disorder? No (clean lattice) Yes (essential) Mott occurs in translationally invariant systems; Anderson needs randomness
3 Dimensionality Occurs in any d ≥ 1 1D/2D: always localized; 3D: critical W_c Anderson transition only true phase transition in d ≥ 3; lower d always insulating
4 Localization cause Coulomb repulsion prevents double occupancy Quantum interference traps waves Mott: correlation-driven avoidance; Anderson: coherent backscattering
5 Gap origin Charge (Mott-Hubbard) gap from U No true gap; mobility edge in 3D Mott gap survives at finite temperature; Anderson states have no hard gap
6 Universality class Hubbard / DMFT, often first-order Orthogonal / unitary / symplectic classes Mott frequently shows hysteresis; Anderson scaling is universal in symmetry class
7 Typical model Single-band Hubbard model Anderson tight-binding model Hubbard at half-filling; Anderson with box or Gaussian disorder
8 Experimental platforms Transition-metal oxides (VO₂, V₂O₃), organic salts Ultracold atoms, photonic lattices, microwaves Mott often coupled to lattice; Anderson cleaner in wave-based systems
9 Coexistence possible? Yes (Anderson-Mott insulators) Yes (Anderson-Mott insulators) Real materials frequently show hybrid behavior (disorder + correlations)

Table. Proposed Extension of Collatz Analogies to High-Temperature Superconductors (HTS)


Comparing only Collatz swarms (from program and plots: lower_swarm vs upper_swarm) versus high-temperature superconductors (HTS, focusing on cuprates like Hg-1223, YBCO, etc.).


Index Aspect Collatz Lower Swarm (λ ≥ 0.10) Collatz Upper Swarm (λ < 0.10) HTS Cuprate Analog (d-wave SC family) HTS Analog Tc (K) Real HTS Tc Examples (K) Quibble notes
1 Eigenvalue / Decay factor High λ (close to 1) Very low λ (near 0) Optimal to underdoped doping 120–151 Hg-1223 quenched 151 K (2026) Lower swarm → optimal doping → record Tc possible; upper swarm → no SC
2 Anderson Localization Length Large / >1000 (extended states) Small / ≈1 (very strong localization) Long coherence length ξ (delocalized pairs) 90–151 YBCO 93 K, Bi-2212 95 K, Hg-1223 133–151 K Extended → long ξ → high Tc; localized → coherence collapse → Tc=0
3 Mobility Edge / Phase Boundary λ ≈ 0.10 (conducting side) λ < 0.10 (insulating side) Optimal doping ~0.16 holes/Cu (dome peak) ~151 Hg-1223 151 K (pressure-quenched) Swarm boundary mimics dome peak at ~151 K (2026 record)
4 Tunneling / Transport Probability High transmission T (near 1) Near-zero transmission High critical temperature Tc (up to 151 K) 93–151 YBCO 93 K, Tl-2223 125 K, Hg-1223 133 K High λ → high Tc (YBCO, Tl, Hg families); low λ → Tc=0 (parent compounds)
5 Competing Orders / Behavior Weak competing orders, delocalized Strong competing orders, trapped states Pseudogap, stripes, charge order, AFM 0–60 La₂CuO₄ 0 K (undoped), pseudogap ~0 K Upper swarm → pseudogap/AFM insulator (Tc=0); lower swarm → clean d-wave SC
6 Swarm / Phase Membership Lower_swarm (extended/conducting) Upper_swarm (localized/insulating) Superconducting dome vs parent insulator 0–151 Hg-1223 151 K vs La₂CuO₄ 0 K Collatz swarms mirror HTS diagram: SC dome (high Tc) vs insulating phases (Tc=0)
7 Extreme Cases Short trajectories, high λ Very long odd-step counts (k > 40) Record Tc in Hg-1223 (151 K, 2026 quenched) 0 or 151 Hg-1223 151 K vs undoped parent 0 K Extreme upper swarm → Tc=0 (Mott insulator); lower swarm → peak Tc=151 K
8 Educational Mapping Classical-like regime, high energy analog Strong disorder analog, trapped wave d-wave pairing, spin-fluctuation mediated 0–151 YBCO 93 K, Hg-1223 151 K, parent 0 K λ maps to doping; Anderson length maps to coherence length → Tc scale


Expected Values for Debug on Anderson Snippet


Index No. # k odd_step_count, integer λ' lambda, eigenvalue, floating No. ξ localization length ξ\xi\xi Quibble / Notes
1 0 1.00000000 ≥1000 Initial / reference value, defaulted by ?/0
2 1 0.75000000 4
3 2 0.56250000 3
4 5 0.23730469 2
5 7 0.13348389 2
6 20 0.00317121 1 Very small λ
7 41 0.00000763 1 Practically zero
8 43 0.00000429 1 Numerical limit / tail


Wiki Table: Computer Iteration Solutions for large n


Index Input Stopping Time Quibble notes
1 2^100 - 1 1465
2 2^500 - 1 6748 first case, where 1-2% accuracy shows over random Collatz trajectories (or luck of draw, slang)
3 2^1000 - 1 12157
4 2^5000 - 1 67378
5 2^10000 - 1 134404
6 2^50000 - 1 667858
7 2^100000 - 1 1344926

Note. The Mersenne primes are special case or group for Collatz trajectories, but the Log2 formula is showing better accuracy over some groups of numbers than others, see the referenced papers.


  • Efficient Computation of Collatz Sequence
  • Stopping Times: A Novel Algorithmic Approach ( credit for the new algorithm. and iteration table )
  • EYOB SOLOMON GETACHEW, BEAKAL GIZACHEW ASSEFA
  • The Collatz Conjecture over the Gaussian Integers, Alejandra Alvarado


Wiki Table: Mersenne-tuned Approx. and general Approx. Estimates for large n



MediaWiki Table copy-paste ready


Index Input Stopping Time General Approx Gen % err Mersenne-tuned Tuned % err Est k Quibble notes
1 2^100-1 1465 1323 -9.69 1344 -8.26 241 large gen error large tuned error small n outlier
2 2^500-1 6748 6615 -1.97 6722 -0.39 1205 -
3 2^1000-1 12157 13230 8.83 13445 10.59 2409 large gen error large tuned error known dip in ratio
4 2^5000-1 67378 66148 -1.83 67223 -0.23 12047 -
5 2^10000-1 134404 132295 -1.57 134446 0.03 24094 -
6 2^50000-1 667858 661476 -0.96 672228 0.65 120471 -
7 2^100000-1 1344926 1322952 -1.63 1344457 -0.03 240942 -

Note: Program deck contains multiple procs for both general estimation and Mersenne tuned algorithm. Numbers are estimates here, not exact iterations.


Note: Est k = round( log‚‚(N) / log‚‚(4/3) ) ~~~ odd steps. Recap: Est k is estimated odd steps, rounded integer, not exact iterations.


Note: Quibble notes show obvious deviations or known behavior.



Wiki-Table. Adapting Nassi-Schneiderman Flowchart, Defining Diagram for Quantum SlideRule


Defining Diagram is high-level Nassi-Schneiderman overview of entire TCL program structure.


The two diagrams together give a complete Nassi-Schneiderman-style design view:

   • Defining Diagram = high-level control flow, sequence boxes only for classic N-S.
   • Solution Diagram = concrete implementation with every subroutine’s purpose, category, McCarthy/quantized notes, and quibble column.


Adapting format of wiki table to Nassi-Schneiderman Flowcharts. Mostly subroutines for brevity over lengthy list of lines.



Index Subroutine Purpose Category Key Params / Inputs N-S Element McCarthy / Quantized Style Quibble / Notes
1 initConsole Set console colors, font, title and greeting Init none Sequence Pure setup 7-bit ASCII safe; palegreen background for TCL club lab use
2 openLogFile / closeLogFile / logAction Timestamped logging to console + qwalk_actions.log Logging msg (string) Sequence Defensive Only explicit user actions call logAction – no mouse-drag flood
3 startRecording / stopRecording Toggle player-piano record mode Recording none Sequence + flag Iterative Status label shows REC / STOPPED with action count
4 recordStep Capture snapshot of all 6 movable items (3 cursors + 3 slides) Recording none Sequence Multivalued One human-readable STEP line + 6 replayList entries; "Quantum Piano Player Scales" joke implemented
5 replayActions / replayStep Replay stored steps with delay Replay i (step index) Iteration (after) Iterative quantized (safety cap 2000) Uses inline llength to fix V5r2 scope bug; McCarthy-style tail iteration
6 saveReplay / loadReplay Persist / restore replayList to/from qwalk_replay.txt File I/O none Sequence Pure functional 2000-step limit; malformed lines skipped
7 showHelp Print full keyboard/mouse/button reference Help none Sequence Pure Called by C_Help button or console
8 playTone Random 12-tone piano note via PowerShell beep (fallback Tk bell) Audio none Sequence Fun extra A_Tone button; 220 ms duration
9 exitProgram Clean shutdown (close log, exit) Exit none Sequence Defensive F9 / Exit_P button
10 clamp / safe_log10 / safe_pow / frac_lin / frac_log / snapMark Safe math helpers Utilities v, lo, hi / from, to, v / tag, newx Pure functions McCarthy functional + quantized Return defaults on error/zero/negative; NASA/JPL style
11 drawContScale Continuous linear or log scale with ticks & labels Drawing w, nm, label, x, y, dy, mode, from, to, len, tag, slide Sequence Log/Linear Used by all three sections; max 12 000 iterations cap
12 drawKScale Integer k column index (0..30) Drawing w, nm, label, x, y, dy, kMax, len, tag Sequence Linear Blue labels for Section 2
13 drawLambdaScale λ_k = (3/4)^k log-positioned scale Drawing w, nm, label, x, y, dy, kMax, len, tag Sequence Log + quantized Eigenvalue ladder (blue)
14 drawGapScale Gap = (1/4)^n log scale (stator + slide) Drawing w, nm, label, x, y, dy, nMax, len, tag, col Sequence Log Darkgreen stator, red slide
15 drawExpScale Exponential speedup 2^n (n=0..20) Drawing w, nm, label, x, y, dy, nMax, len, tag Sequence Log Purple for Section 3
16 drawCIScale Reciprocal CI 1/x (reversed log) Drawing w, nm, label, x, y, dy, len, tag Sequence Reciprocal Orange; used in Walk Arithmetic
17 buildSection1 CFG Propagation scales (Qn, Qd, Qp, Qc, Qt, Ql) Canvas Build none Sequence Full section Section 1: Tree depth, hit prob, classical bound, time, lambda slide
18 buildSection2 Eigenvalue Ladder (k → λ_k, gap) Canvas Build none Sequence Full section Section 2: k index, lambda slide, two gap scales
19 buildSection3 Walk Arithmetic (C/D mult, A/B sqrt, CI recip, EX speedup) Canvas Build none Sequence Full section Section 3: classic slide-rule ops + quantum speedup
20 update_s1 / update_s2 / update_s3 Compute & refresh info labels from cursor x Readout none Sequence Quantized calc Live 4-sig-fig probabilities; no logAction here
21 buildUI Master UI: pack canvases, labels, button bar, bindings UI none Sequence Top-level F5–F9 + mouse + arrow keys; status label for record state


Wiki-Table. Adapting Nassi-Schneiderman Flowchart, Solution Diagram for Quantum SlideRule


Adapting format of wiki table to Nassi-Schneiderman Flowcharts. Mostly subroutines for brevity over lengthy list of lines.


Solution Diagram for detailed subroutine purposes has one row per proc for brevity.


Index N-S Box Type Component Purpose Quibble / Notes
1 Sequence (Init) Program Startup initConsole + openLogFile + global state Core NASA/JPL defensive setup; console + logging always first
2 Sequence (Player-Piano) Record / Replay System startRecording, stopRecording, recordStep, replayActions, replayStep, saveReplay, loadReplay Iterative "quantum piano" for step-by-step slide-rule snapshots; safety cap at 2000 steps prevents Collatz-style infinite loops
3 Sequence (Safety) Math Utilities clamp, safe_log10, safe_pow, frac_lin, frac_log, snapMark Quantized & multivalued McCarthy-style functions; returns safe defaults instead of crashing
4 Sequence (Drawing) Scale Rendering drawContScale, drawKScale, drawLambdaScale, drawGapScale, drawExpScale, drawCIScale Builds all visual scales for the three-section slide rule (linear/log/exponential/reciprocal)
5 Sequence (Build) Section Canvases buildSection1, buildSection2, buildSection3 Constructs the three physical slide-rule sections (CFG, Eigenvalue Ladder, Walk Arithmetic) with mouse/keyboard bindings
6 Sequence (Update) Real-time Readouts update_s1, update_s2, update_s3 Computes and displays live values from cursor/slide positions (no console flood)
7 Sequence (UI) Master Builder buildUI Packs all canvases, labels, buttons, and binds F-keys / mouse / arrows; final assembly point
8 Sequence (Main) Event Loop & Exit exitProgram + all button bindings Clean shutdown with log close; Tk event-driven loop (implicit)

Time Line of N-S Charts


Index Year Event Key People / Detail Quibble / Notes
1 1972 Diagrams conceived Isaac Nassi & Ben Shneiderman (Stony Brook grad students) Born during the structured programming revolution to kill GOTO
2 1973 First publication "Flowchart Techniques for Structured Programming" (ACM) Original typewriter + hand-drawn paper still online; called "structured flowcharts"
3 1970s Early industrial use IBM System Products Division (Endicott, NY) Used as flowchart replacement in design & coding
4 1985 German standardization DIN 66261 "Structograms" become mandatory in many German CS curricula
5 1990s–2010s Teaching staple High schools & universities (esp. German-speaking) Excellent for QuickSort, Collatz, etc.; enforces McCarthy-style clean logic
6 2020s Rediscovery & tools Visio, Software Ideas Modeler, modern IDE plugins Still the clearest visual for nested sequence/selection/iteration; zero arrow clutter

References


  • Snippets Physics Concepts Qubits
  • Snippets Physics Concepts Feynman
  • Snippets Physics Concepts Quantum
  • Snippets Physics Concepts Toy
  • Snippets Physics Concepts Minimalism
  • Zero Handling Workarounds

Note. These Snippets on Theoretical Physics are a set, not stand alones. Recommend read all of the set.


  • A little slide-rule on TCL Wiki, ( much credit for the algorithms in the sliderule. )
  • Richard Suchenwirth 2003-08-31
  • Smoothing and differentiation of data by simplified least squares procedures
  • Savitzky, A. ; Golay, M. J. E. Two examples are presented as subroutines in the FORTRAN language.
  • Savitzky Golay Filtering, Python
  • Savitzky Golay Filtering — SciPy Cookbook documentation
  • Smoothing Example with Savitzky-Golay Filter in Python
  • Introduction to the Savitzky-Golay Filter: A Comprehensive Guide (Using Python), Thomas Konstantinovsky
  • Konstantinovsky has good explanation. Note detailed. WhittakerSmoother in Python
  • The Perfect Way to Smooth Your Noisy Data, Whittaker-Eilers smoother, Andrew Bowell
  • Feb 28, 2024

  • A Basis for a Mathematical Theory of Computation,Author(s)
  • McCarthy, John
  • John McCarthy: A basis for a mathematical theory of computation, in:
  • Computer Programming and Formal Systems.
  • P.Braffort, D.Hirschberg (ed.), Amsterdam:North Holland 1963,
  • several versions, archived pdf
  • McCarthy’s LISP and Basis for Theory of Computation, archived pdf
  • en.wikipedia.org search on <John McCarthy computer>
  • John McCarthy at Stanford web site, archived
  • Towards a Mathematical Science of Computation, J. McCarthy,
  • Computer Science Department, Stanford University, archived pdf
  • Elephant 2000: A Programming Language Based on Speech Acts
  • John McCarthy, Stanford University, archived
  • Elephant input and output statements are characterized
  • as speech acts and programs, which
  • can refer directly to the past.
  • Elephant proposal contains summary
  • on McCarthy mathematical theory of computation
  • Mysteries and other Matters, development of Lisp , archived
  • Note. A lot of early papers and notes from John McCarthy and Knuth are difficult to assess web links or archived.

  • Machine Learning Approaches to the Collatz Conjecture:
  • A Comprehensive Framework for Pattern Recognition
  • and Automated Conjecture Generation. IJIRT, Vol. 12 Issue 7
  • Transformers Know More Than They Can Tell:
  • Learning the Collatz Sequence , arXiv:2511.10811
  • The Collatz conjecture, Littlewood-Offord theory, and powers of 2 and 3,
  • Aug 2011, Terence Tao,
  • mentions Gambler's Ruin on this 2011 post, but better search on his website for updates.

  • Efficient Computation of Collatz Sequence
  • Stopping Times: A Novel Algorithmic Approach ( credit for the new algorithm. )
  • EYOB SOLOMON GETACHEW, BEAKAL GIZACHEW ASSEFA
  • The Collatz Conjecture over the Gaussian Integers, Alejandra Alvarado

  • An example of the difference between quantum and classical random walks
  • Andrew M. Childs, Edward Farhi, Sam Gutmann ( much credit for the new algorithm. )


Screenshots



Figure 1. Snippets Concepts Quantume SlideRule, prototype V4


Snippets Concepts Quantum Sliderule



Figure. Snippets Concepts Quantum SlideRule V5


Snippets Concepts Slide Version



Figure. Snippets Concepts Quantum SlideRule V5, dat_file on console


Snippets Concepts SlideRule Dat_File



Figure. Snippets Concepts Quantum SlideRule V6


Snippets Concepts SlideRule VX6


Figure 1a. Snippets Concepts SlideRule Collection


Snippets Concepts SlideRule Collection2


Figure. Snippets Concepts First 700 Collatz Pts.


Log2 vs Log2 plot is closest to a lion tamer that I know. Points would follow straight line if linear data. Some points or "binary probabilistic bins in computer lingo" are reused and overlaid, making a cleaner look to a non-linear function.


Snippets Concepts SlideRule pts


Figure 1b. Collatz Length, Points from Collatz Conjecture, 10 Random pts for N < 100


Snippets Concepts Collatz Plot_1


Figure 1c. Collatz Length, Points from Collatz Conjecture, N < 500


Snippets Collatz Length 2


Figure 2. Collatz Length, lower region and lower region of solutions


Snippets Collatz Length


Figure 3. Collatz Length, curve fit on envelope of lower region


Snippets Collatz Length 3



Figure 4. Collatz Length, Envelope of lower region


Snippets Concepts Collatz Lower



Figure 5. Bankroll over number of games


Unlike the Collatz Conjecture which goes to infinity, Starting Bankroll with diminishing games is a linear and deterministic function. But one can see multiple, quantized, and simultaneous solutions in the "crowd" of gamblers. Essentially, Bankroll(s) = { Bankroll start} - K1 * {number of games} ending at Zero, but not ending at infinity like the Collatz Conjecture.



Snippets Concepts Collatz bankroll versus games



Figure 6.Snippets Concepts Collatz staircase


Snippets Concepts Collatz stair



Figure. Snippets Concepts Collatz Polynomial


Snippets Concepts SlideRule Poly3




Figure. Snippets Concepts Collatz Power Law


Snippets Concepts SlideRule Power Law


**** figure. QUANTUM WALK SLIDE RULE OVERVIEW ****

+----------------------------------------------------------------------------------+
| QUANTUM WALK SLIDE RULE                                                          |
|                                                                                  |
|    Three Sections:                                                               |
|      SEC 1 : CFG Propagation (Tree depth n, hit prob, classical bound)           |
|      SEC 2 : Eigenvalue Ladder (k → λ_k = (3/4)^k, gap scales)                   |
|      SEC 3 : Walk Arithmetic (C/D multiply, A/B sqrt, CI reciprocal, EX speedup) |
|                                                                                  |
|    Mouse: LMB = drag slide, RMB = snap/drag cursor                               |
|    Keyboard: Arrow keys per section + Shift/Ctrl/Alt modifiers                   |
|    Player-Piano: F5 Record | Record_Step | F7 Replay | F8 Save | F9 Load         |
|                                                                                  |
|    Visual tool for Collatz → Quantum Walk analogies (Childs-Farhi-Gutmann)      |
+----------------------------------------------------------------------------------+

**** figure. COLLATZ ANALOGY, COLLATZ SWARMS AND ANDERSON LOCALIZATION ****

+----------------------------------------------------------------------------------+
| LOWER SWARM vs UPPER SWARM (Scatter Plot Analogy)                                |
|                                                                                  |
|    Lower Swarm (λ ≥ 0.10)                                                        |
|      Short trajectories → Fast percolation to sink (node 1)                      |
|      High transmission T ≈ 1                                                     |
|      Extended states → Conducting phase (delocalized)                            |
|                                                                                  |
|    Upper Swarm (λ < 0.10)                                                        |
|      Long trajectories (high k) → Slow leakage / trapped probability             |
|      Tunneling probability near zero                                             |
|      Strong localization → Insulating phase                                      |
|                                                                                  |
|    Mobility Edge ≈ λ = 0.10                                                      |
|    Visual: Dense lower band + sparse upper band with staircase boundaries        |
+----------------------------------------------------------------------------------+

**** figure. EIGENVALUE LADDER λ_k = (3/4)^k ****

+----------------------------------------------------------------------------------+
| EIGENVALUE LADDER (Anderson Localization Strength)                               |
|                                                                                  |
|    k (odd steps)   λ = (3/4)^k          Behavior                                 |
|    0               1.0000               Fast percolation, conducting             |
|    5               0.2373               Moderate tunneling                       |
|    7               0.1335               Boundary layer                           |
|    20              0.00317              Strong suppression                       |
|    41 (n=27)       ~0.0000076           Near-zero → Localized / trapped          |
|    43 (n=97)       ~0.0000043           Extreme localization                     |
|                                                                                  |
|    Small λ → Anderson localization (upper swarm)                                 |
|    Large λ → Delocalized percolation (lower swarm)                               |
+----------------------------------------------------------------------------------+

**** figure. QUANTUM WALK SLIDE RULE SECTIONS ****

+----------------------------------------------------------------------------------+
| THREE SECTIONS OF THE QUANTUM WALK SLIDE RULE                                    |
|                                                                                  |
|    Section 1: CFG Propagation                                                    |
|      Tree depth n, time t ≈ n/(2√2), hit prob ~1/(2n), classical bound 2^{-n}    |
|                                                                                  |
|    Section 2: Eigenvalue Ladder                                                  |
|      k index, λ_k = (3/4)^k, gap = (1/4)^n (stator + slide)                      |
|                                                                                  |
|    Section 3: Walk Arithmetic                                                    |
|      C/D multiply-divide, A/B square-root, CI reciprocal, EX exponential speedup |
|                                                                                  |
|    Mouse + keyboard move slides and cursors → live 4-sig-fig readouts            |
+----------------------------------------------------------------------------------+

**** figure. GAMBLER'S BANKROLL vs COLLATZ ANALOGY ****

+----------------------------------------------------------------------------------+
| GAMBLER'S RUIN ANALOGY (Entropy Drift)                                           |
|                                                                                  |
|    Bankroll B starts high → repeated win/loss steps → absorption at zero         |
|                                                                                  |
|    Collatz Mapping:                                                              |
|      Odd step (3n+1)   ≈ Win (energy injection)                                  |
|      Even step (n/2)   ≈ Loss (energy release)                                   |
|      Sink at 1         ≈ Ruin (absorbing state)                                  |
|                                                                                  |
|    Eigenvalue λ controls drift speed                                             |
|      High λ → Fast ruin (lower swarm)                                            |
|      Low λ  → Slow leakage / long survival (upper swarm)                         |
|                                                                                  |
|    Note: Analogy direction sometimes inverts between domains                     |
+----------------------------------------------------------------------------------+

**** figure. NASSI-SHNEIDERMAN DEFINING DIAGRAM (High-Level) ****

+----------------------------------------------------------------------------------+
| NASSI-SHNEIDERMAN DEFINING DIAGRAM (Quantum Slide Rule)                          |
|                                                                                  |
|    Sequence (Init)          → initConsole + openLogFile + globals                |
|    Sequence (Player-Piano)  → Record / Replay / Save / Load system               |
|    Sequence (Safety)        → clamp, safe_pow, snapMark etc.                     |
|    Sequence (Drawing)       → All scale procs (drawContScale, drawLambdaScale…)  |
|    Sequence (Build)         → buildSection1 / 2 / 3                              |
|    Sequence (Update)        → update_s1 / s2 / s3 (live readouts)                |
|    Sequence (UI)            → buildUI + bindings + buttons                       |
|    Sequence (Main)          → Event loop + clean exit                            |
|                                                                                  |
|    True left / False right convention preserved in wiki tables                   |
+----------------------------------------------------------------------------------+

**** figure. PLATO'S CAVE ANALOGY (AI vs Human on Collatz Analogy) ****

+----------------------------------------------------------------------------------+
| PLATO'S CAVE ON COLLATZ                                                              |
|                                                                                  |
|    Prisoners (Humans) see shadows: decimal magnitudes, apparent chaos            |
|    Outside (AI / Binary view): true forms = bit-shifts, eigenvalue ladder,       |
|                                deterministic automaton, spectral bands           |
|                                                                                  |
|    Decimal notation = cave wall                                                  |
|    Binary structure = sunlight outside cave                                      |
|    AI sees native binary patterns → immediate structure                          |
|    Humans must translate → often miss underlying order                           |
|                                                                                  |
|    Entropy / energy metaphors: natural to probabilistic binary logic             |
+----------------------------------------------------------------------------------+

**** figure. SUMMARY OF COLLATZ ANALOGY AND  QUANTUM WALK ANALOGIES ****
+----------------------------------------------------------------------------------+
| KEY ANALOGIES SUMMARY                                                            |
|                                                                                  |
|    Collatz Graph      → Directed graph for quantum walk                          |
|    Odd-step count k   → Eigenvalue index λ = (3/4)^k                             |
|    Lower swarm        → Delocalized / fast percolation (conducting)              |
|    Upper swarm        → Localized / trapped states (insulating)                  |
|    Mobility edge      ≈ λ ≈ 0.10                                                 |
|    Sink at 1          → Absorbing state                                          |
|    Gambler's ruin     → Biased random walk to absorption                         |
|    HTS Cuprates       → Lower swarm ≈ superconducting dome (high Tc)             |
|                                                                                  |
|    Educational value: Concrete classical problem → quantum concepts              |
+----------------------------------------------------------------------------------+




Appendix Code


Appendix TCL Programs and Scripts


1. Expanded Toy for Demo



Experimenting with a iterative quantized and multivalued solution in McCarthy Function style


This is a draft.


Right mouse button moves slide in each section. Left mouse button moves cursor or blue hairlines in each section. May contain Dependencies on Windows 11 and ActiveState


V5r2 is 778 lines with 30 distinct procs.


Player-piano record and replay system


The replay system shown on the console should not be necessary to work the slide rule manually, just use the slides & cursor. Personally, I like to have a record file of the steps I made on a successful set of calculations, even as I used to keep a track of slide rule "slippages" on scratch paper. Just in case, I did something right by mistake, I want to travel on the same path or with same piano song. Joke!!! Actually, TCL could run and replay the slide rule as "Quantum Piano Player Scales" with just a little more coding for music and sound.


The five function keys F5 through F9 map to Record, Stop, Replay, Save, and Load. The same five functions are also available as colored buttons in the button bar below Section 3, which is useful for mouse-only operation. The status label below the button bar shows the current state (IDLE, REC, STOP, PLAY, SAVED, or LOADED) with a step count.


# tcl
# Quantum Walk Slide Rule – Childs-Farhi-Gutmann Algoruthm V6
# Using multiple safety caps or clamps, Collatz Sequence is infinite,
# danger of endless loops in program.
# ----
# Educational version with visible probabilities (4 sig figs)
# Alternate results in wiki table format (header with %|, alternating rows &|)
# ----
# Tcl/Tk 8.6+ 7-bit ASCII safe. NASA/JPL defensive programming style.
# NASA/JPL defensive programming style.
# Compatible with Tcl/Tk (Tool Control Language / Toolkit) 8.6+
# Written for Windows 11 on ActiveState Tcl.
# Working under strict 7-bit ASCII encoding.
# Optimized for collegiate information technology lab environments.
# Written to be very modular for transferable procs.
# Program deck may contain multiple estimation procs.
# May contain  code dependencies on Active State and Windows 11
# Complex math calculations up to 8 units computer time
# Wait for complete calculations before saving files.
# This is a hacker's patch, not rigorously derived.
# appears correct solutions for autotests.
# pure ASCII code - no Unicode characters used anywhere.
# Approaching =>>> computer time limit on this TCL configuration setup. 
# TCL club, 03/23/2026
# Quantum Walk Slide Rule – inspired by Childs-Farhi-Gutmann glued trees
# Adapted from Richard Suchenwirth's "A little slide-rule" (2003)
# Fixed expression bracing errors – March 2026 – for Tcl wiki & 10-test integration
# tcl 
# Quantum Walk Slide Rule V5r3 -- Childs-Farhi-Gutmann Algorithm
# Three-section instrument:
#   Section 1 : CFG Propagation scales
#   Section 2 : Eigenvalue Ladder  k --> lambda_k = (3/4)^k
#   Section 3 : Walk Arithmetic    C/D, A/B, CI, EX
# ----
# V5r3 changes from V5r2:
#   FIX 1 : logAction removed from update_sN and motion bindings.
#            Console no longer floods on every mouse drag event.
#   FIX 2 : replayStep $n bug fixed.  Variable $n was out of scope.
#            Now uses [llength $::replayList] inline.
#   NEW 1 : Record_Step button -- one click captures current positions
#            of all six moveable items as a single snapshot entry.
#            Writes one summary line to console and log file.
#            Also appends to ::replayList so the step is replayable.
#   NEW 2 : C_Help button -- prints keyboard/mouse help to console.
#   NEW 3 : A_Tone button -- plays random note from 12-tone piano scale
#            via PowerShell beep on Windows.  Falls back to bell.
#   NEW 4 : Exit_P button -- clean shutdown, closes log, exits.
# ----
# V5r4 changes from V5r3:
#   NEW : Re_Start button added as next-to-last button.
#         Re_Start flushes the log then relaunches via exec wish, same
#         path as the Esc key.  Esc key still works identically.
# Button bar order (left to right):
#   F5-Record | F6-Stop | Record_Step | F7-Replay | F8-Save | F9-Load
#   | C_Help | A_Tone | Re_Start | Exit_P
# ----
# Mouse:
#   Left  mouse button drag  = move the slide (colored band)
#   Right mouse button click or drag = snap / move hairline cursor
# ----
# Keyboard:
#   Arrow keys           --> Section 1 cursor / Shift-Arrow = Section 1 slide
#   Ctrl-Arrow           --> Section 2 cursor / Ctrl-Shift-Arrow = Section 2 slide
#   Alt-Arrow            --> Section 3 cursor / Alt-Shift-Arrow  = Section 3 slide
#   F5=record  F6=stop  F7=replay  F8=save  F9=load
#   Esc --> reload program
# ----
# NASA/JPL defensive programming style.
# Tcl/Tk 8.6+, 7-bit ASCII safe, Windows 11 ActiveState Tcl.
# TCL club, 03/2026

console show
package require Tk

# ============================================================
#  CONSOLE STYLING
# ============================================================

proc initConsole {} {
    catch {
        console eval {.console config -bg palegreen}
        console eval {.console config -font {fixed 20 bold}}
        console eval {.console config -background palegreen \
            -highlightcolor tan -relief raised -border 8}
        console eval {wm geometry . 60x24}
        console eval {wm title . "QWalk SlideRule Console"}
    }
    puts "Quantum Walk Slide Rule V5r4 -- console ready."
}

# ============================================================
#  PRECOMPUTED CLASSICAL SINK PROBABILITIES  (200-step demos)
# ============================================================

array set ::cSink {
    2  1.000000   3  0.999999   5  0.999950   7  0.950000
    9  0.920000  15  0.880000  25  0.750000  27  0.000010
    97 0.000002 100  0.000001
}

# ============================================================
#  LOGGING SYSTEM
#  logAction writes a timestamped line to console and log file.
#  The log file is opened once at startup and closed on exit.
#  logAction is called ONLY by explicit user actions:
#    startRecording, stopRecording, recordStep, replay procs,
#    saveReplay, loadReplay, showHelp, playTone.
#  logAction is NOT called from update_sN or motion bindings.
#  This prevents console flood on mouse drag.
# ============================================================

set ::logFd   ""
set ::logFile "qwalk_actions.log"

proc openLogFile {} {
    if {[catch {open $::logFile a} fd]} {
        puts "WARNING: cannot open log file $::logFile -- $fd"
        return
    }
    set ::logFd $fd
    logAction "=== Session start V5r4 ==="
}

proc closeLogFile {} {
    if {$::logFd eq ""} { return }
    catch {
        logAction "=== Session end ==="
        close $::logFd
    }
    set ::logFd ""
}

proc logAction {msg} {
    set ts   [clock format [clock seconds] -format "%H:%M:%S"]
    set line "$ts  $msg"
    puts $line
    if {$::logFd ne ""} {
        catch { puts $::logFd $line ; flush $::logFd }
    }
}

# ============================================================
#  PLAYER-PIANO RECORD / REPLAY SYSTEM
#
#  Each stored action is a 4-element Tcl list:
#    { section  type  tag  px }
#  section = 1|2|3
#  type    = cursor|slide
#  tag     = mark1|mark2|mark3|slide1|slide2|slide3
#  px      = integer pixel x (absolute canvas coords)
#
#  Record_Step captures all six moveable items at once
#  as six consecutive list entries.  The result reads like
#  a program listing:
#    1 cursor mark1 210
#    1 slide  slide1base 0
#    2 cursor mark2 350
#    ... etc.
# ============================================================

set ::recording   0
set ::replayList  {}
set ::replayFile  "qwalk_replay.txt"
set ::replayDelay 400

proc startRecording {} {
    set ::recording  1
    set ::replayList {}
    set ::recStatus  "REC -- click Record_Step to capture each position"
    logAction "RECORD START -- click Record_Step after each slide rule setting."
}

proc stopRecording {} {
    set ::recording 0
    set n [llength $::replayList]
    set ::recStatus "STOPPED  ($n actions in buffer)"
    logAction "RECORD STOP -- $n actions stored.  F7=replay  F8=save."
}

proc recordStep {} {
    # Capture current pixel x of all six moveable items as one snapshot.
    # Appends six entries to ::replayList if recording is active.
    # Always logs one summary line to console and file.

    # Helper: safe coordinate read returns 0 on any error.
    set px1m  [expr {int([lindex [.c1 coords mark1]      0])}]
    set px1s  [expr {int([lindex [.c1 coords slide1base] 0])}]
    set px2m  [expr {int([lindex [.c2 coords mark2]      0])}]
    set px2s  [expr {int([lindex [.c2 coords slide2base] 0])}]
    set px3m  [expr {int([lindex [.c3 coords mark3]      0])}]
    set px3s  [expr {int([lindex [.c3 coords slide3base] 0])}]

    if {$::recording} {
        lappend ::replayList [list 1 cursor mark1      $px1m]
        lappend ::replayList [list 1 slide  slide1base $px1s]
        lappend ::replayList [list 2 cursor mark2      $px2m]
        lappend ::replayList [list 2 slide  slide2base $px2s]
        lappend ::replayList [list 3 cursor mark3      $px3m]
        lappend ::replayList [list 3 slide  slide3base $px3s]
        set n [llength $::replayList]
        set ::recStatus "REC -- $n actions in buffer"
    }

    # Build the human-readable summary line.
    # Read computed values from the current info labels (already computed).
    set line "STEP |  $::info1"
    append line "  ||  $::info2"
    append line "  ||  $::info3"
    logAction $line
}

proc replayActions {} {
    set n [llength $::replayList]
    if {$n == 0} {
        logAction "REPLAY: nothing in buffer.  Load a file (F9) or record steps (F5)."
        return
    }
    logAction "REPLAY START -- $n actions at $::replayDelay ms each."
    set ::recStatus "PLAYING $n actions ..."
    replayStep 0
}

proc replayStep {i} {
    # Safety cap at 2000 steps prevents runaway loops.
    # FIX V5r3: replaced out-of-scope $n with inline list length call.
    set total [llength $::replayList]
    if {$i >= $total || $i > 2000} {
        logAction "REPLAY END -- $total actions completed."
        set ::recStatus "REPLAY DONE ($total actions)"
        return
    }

    set item [lindex $::replayList $i]
    set sec  [lindex $item 0]
    set type [lindex $item 1]
    set tag  [lindex $item 2]
    set px   [lindex $item 3]
    set w    ".c$sec"

    catch {
        set coords [$w coords $tag]
        if {[llength $coords] >= 2} {
            set curx [lindex $coords 0]
            $w move $tag [expr {$px - $curx}] 0
        }
    }

    # Refresh section display after moving.
    catch { update_s$sec }

    set stepnum [expr {$i + 1}]
    logAction "PLAY $stepnum/$total  S$sec $type $tag px=$px"

    after $::replayDelay [list replayStep [expr {$i + 1}]]
}

proc saveReplay {} {
    if {[llength $::replayList] == 0} {
        logAction "SAVE: buffer is empty -- nothing to save."
        return
    }
    if {[catch {open $::replayFile w} fd]} {
        logAction "SAVE ERROR: cannot open $::replayFile -- $fd"
        return
    }
    foreach item $::replayList { puts $fd $item }
    close $fd
    set n [llength $::replayList]
    logAction "SAVE: $n actions written to $::replayFile"
    set ::recStatus "SAVED $n actions to $::replayFile"
}

proc loadReplay {} {
    if {[catch {open $::replayFile r} fd]} {
        logAction "LOAD ERROR: cannot open $::replayFile -- $fd"
        return
    }
    set ::replayList {}
    set count 0
    while {[gets $fd line] >= 0} {
        set line [string trim $line]
        if {$line eq ""} { continue }
        if {[catch {llength $line} len] || $len != 4} {
            logAction "LOAD: skipping malformed line: $line"
            continue
        }
        lappend ::replayList $line
        incr count
        if {$count > 2000} { break }
    }
    close $fd
    logAction "LOAD: $count actions loaded from $::replayFile  F7=replay"
    set ::recStatus "LOADED $count actions from $::replayFile"
}

# ============================================================
#  C_HELP -- print keyboard and mouse reference to console
# ============================================================

proc showHelp {} {
    set lines {
        "=== Quantum Walk Slide Rule V5r3 -- Help ==="
        "MOUSE:"
        "  Left button drag     = move the slide (colored band)"
        "  Right button click   = snap hairline cursor to that position"
        "  Right button drag    = drag hairline cursor"
        "KEYBOARD:"
        "  Arrow Left/Right     = move Section 1 cursor"
        "  Shift-Arrow          = move Section 1 slide"
        "  Ctrl-Arrow           = move Section 2 cursor"
        "  Ctrl-Shift-Arrow     = move Section 2 slide"
        "  Alt-Arrow            = move Section 3 cursor"
        "  Alt-Shift-Arrow      = move Section 3 slide"
        "PLAYER-PIANO:"
        "  F5 = start recording"
        "  F6 = stop recording"
        "  Record_Step button   = capture current position as one step"
        "  F7 = replay recorded steps"
        "  F8 = save steps to qwalk_replay.txt"
        "  F9 = load steps from qwalk_replay.txt"
        "BUTTONS:"
        "  C_Help = this help text"
        "  A_Tone = random piano note (A4=440 Hz base, 12 tones)"
        "  Re_Start = flush log and relaunch fresh (same as Esc key)"
        "  Exit_P = close log and exit"
        "  Esc    = reload program"
        "LOG FILE: qwalk_actions.log (append mode, session start/end marked)"
        "============================================"
    }
    foreach l $lines { logAction $l }
}

# ============================================================
#  A_TONE -- play a random note from the 12-tone piano scale
#  Frequencies are equal-temperament starting at middle C (C4=261 Hz).
#  Uses PowerShell Console::Beep on Windows.
#  Falls back to Tk bell if PowerShell is unavailable.
# ============================================================

proc playTone {} {
    # 12-tone equal temperament: C4 through B4.
    set noteNames {C4 Db4 D4 Eb4 E4 F4 Gb4 G4 Ab4 A4 Bb4 B4}
    set noteFreqs {261  277 294  311 330 349  370 392  415 440  466 494}

    set idx  [expr {int(rand() * 12)}]
    set freq [lindex $noteFreqs $idx]
    set name [lindex $noteNames $idx]

    # Try PowerShell beep (Windows).  Duration = 220 ms.
    set cmd "powershell -Command \"\[console\]::beep($freq,220)\""
    if {[catch {exec {*}$cmd} err]} {
        # Fallback: Tk bell (system default beep, no pitch control).
        catch { bell }
    }
    logAction "A_Tone: $name  freq=$freq Hz"
}

# ============================================================
#  EXIT_P -- clean shutdown
# ============================================================

proc exitProgram {} {
    logAction "Exit_P pressed -- closing."
    closeLogFile
    exit
}

proc restartProgram {} {
    # Re_Start: same path as the Esc key binding.
    # Flush and close log, launch a fresh wish process, then exit.
    logAction "Re_Start pressed -- relaunching."
    closeLogFile
    catch { exec wish $::argv0 & }
    exit
}

# ============================================================
#  SAFETY AND MATH UTILITIES
# ============================================================

proc clamp {v lo hi} {
    if {$v < $lo} { return $lo }
    if {$v > $hi} { return $hi }
    return $v
}

proc safe_log10 {v} {
    if {$v <= 0.0} { return -300.0 }
    return [expr {log10(double($v))}]
}

proc safe_pow {b e} {
    set e [clamp $e -300 300]
    if {[catch {expr {pow(double($b), double($e))}} result]} { return 0.0 }
    return $result
}

proc frac_lin {from to v} {
    if {$to == $from} { return 0.0 }
    return [expr {(double($v) - double($from)) / (double($to) - double($from))}]
}

proc frac_log {from to v} {
    set lf [safe_log10 $from]
    set lt [safe_log10 $to]
    set lv [safe_log10 $v]
    if {$lt == $lf} { return 0.0 }
    return [expr {($lv - $lf) / ($lt - $lf)}]
}

# ============================================================
#  MOUSE HELPER
# ============================================================

proc snapMark {w tag newx} {
    set coords [$w coords $tag]
    if {[llength $coords] < 2} { return }
    set curx [lindex $coords 0]
    $w move $tag [expr {$newx - $curx}] 0
}

# ============================================================
#  SCALE DRAWING PROCS
#  drawContScale  -- continuous linear or log scale
#  drawKScale     -- integer k scale, linear spacing
#  drawLambdaScale -- lambda_k = (3/4)^k, log-positioned
#  drawGapScale   -- eigenvalue gap (1/4)^n, log-positioned
#  drawExpScale   -- exponential speedup 2^n
#  drawCIScale    -- reciprocal 1/x, reversed
# ============================================================

proc drawContScale {w nm label x y dy mode from to len tag slide} {
    set col   [expr {$slide ? "red" : "black"}]
    set maxIt 12000
    set count 0
    $w create text $x [expr {$y + $dy * 3.5}] \
        -text $nm -font {Helvetica 10 bold} -fill $col -tag $tag -anchor w
    $w create text [expr {$x + $len + 5}] [expr {$y + $dy * 2.0}] \
        -text $label -font {Helvetica 7} -fill $col -tag $tag -anchor w
    if {$mode eq "log"} {
        set d0 [expr {int(floor([safe_log10 $from]))}]
        set d1 [expr {int(ceil( [safe_log10 $to  ]))}]
        for {set d $d0} {$d <= $d1} {incr d} {
            for {set s 1} {$s <= 9} {incr s} {
                incr count ; if {$count > $maxIt} { break }
                set v [expr {$s * pow(10.0, $d)}]
                if {$v < $from * 0.9999 || $v > $to * 1.0001} { continue }
                set frac [frac_log $from $to $v]
                set pos  [expr {$x + $frac * $len}]
                if {$pos < $x-1 || $pos > $x+$len+1} { continue }
                set h [expr {$s==1 ? 2.2 : ($s==5 ? 1.5 : 1.0)}]
                $w create line $pos $y $pos [expr {$y+$h*$dy}] -fill $col -tag $tag
                if {$s == 1} {
                    $w create text $pos [expr {$y+$dy*4.5}] \
                        -text [format "%.2g" $v] -font {Helvetica 7} \
                        -anchor n -fill $col -tag $tag
                }
            }
        }
    } else {
        set step [expr {double($to-$from)/100.0}]
        if {$step <= 0} { return }
        for {set i 0} {$i <= 100} {incr i} {
            incr count ; if {$count > $maxIt} { break }
            set v    [expr {$from + $i*$step}]
            set frac [frac_lin $from $to $v]
            set pos  [expr {$x + $frac*$len}]
            if {$pos < $x-1 || $pos > $x+$len+1} { continue }
            set h [expr {($i%10==0) ? 2.2 : ($i%5==0) ? 1.5 : 1.0}]
            $w create line $pos $y $pos [expr {$y+$h*$dy}] -fill $col -tag $tag
            if {$i % 10 == 0} {
                $w create text $pos [expr {$y+$dy*4.5}] \
                    -text [format "%.3g" $v] -font {Helvetica 7} \
                    -anchor n -fill $col -tag $tag
            }
        }
    }
}

proc drawKScale {w nm label x y dy kMax len tag} {
    $w create text $x [expr {$y+$dy*3.5}] \
        -text $nm -font {Helvetica 10 bold} -fill black -tag $tag -anchor w
    $w create text [expr {$x+$len+5}] [expr {$y+$dy*2.0}] \
        -text $label -font {Helvetica 7} -fill black -tag $tag -anchor w
    for {set k 0} {$k <= $kMax} {incr k} {
        set frac [expr {double($k)/double($kMax)}]
        set pos  [expr {$x + $frac*$len}]
        set h    [expr {($k%5==0) ? 2.2 : 1.0}]
        $w create line $pos $y $pos [expr {$y+$h*$dy}] -fill black -tag $tag
        if {$k%5 == 0} {
            $w create text $pos [expr {$y+$dy*4.5}] \
                -text $k -font {Helvetica 7} -anchor n -fill black -tag $tag
        }
    }
}

proc drawLambdaScale {w nm label x y dy kMax len tag} {
    set lMin [safe_log10 [safe_pow 0.75 $kMax]]
    set lMax 0.0
    set lRng [expr {$lMax - $lMin}]
    if {abs($lRng) < 1e-10} { return }
    $w create text $x [expr {$y+$dy*3.5}] \
        -text $nm -font {Helvetica 10 bold} -fill blue -tag $tag -anchor w
    $w create text [expr {$x+$len+5}] [expr {$y+$dy*2.0}] \
        -text $label -font {Helvetica 7} -fill blue -tag $tag -anchor w
    for {set k 0} {$k <= $kMax} {incr k} {
        set lam  [safe_pow 0.75 $k]
        set logL [safe_log10 $lam]
        set frac [expr {($logL-$lMin)/$lRng}]
        set pos  [expr {$x + $frac*$len}]
        if {$pos < $x-1 || $pos > $x+$len+1} { continue }
        set h    [expr {($k%5==0) ? 2.2 : 1.0}]
        $w create line $pos $y $pos [expr {$y+$h*$dy}] -fill blue -tag $tag
        if {$k%5 == 0} {
            $w create text $pos [expr {$y+$dy*4.5}] \
                -text $k -font {Helvetica 7} -anchor n -fill blue -tag $tag
        }
    }
}

proc drawGapScale {w nm label x y dy nMax len tag col} {
    set lMin [safe_log10 [safe_pow 0.25 $nMax]]
    set lMax 0.0
    set lRng [expr {$lMax - $lMin}]
    if {abs($lRng) < 1e-10} { return }
    $w create text $x [expr {$y+$dy*3.5}] \
        -text $nm -font {Helvetica 10 bold} -fill $col -tag $tag -anchor w
    $w create text [expr {$x+$len+5}] [expr {$y+$dy*2.0}] \
        -text $label -font {Helvetica 7} -fill $col -tag $tag -anchor w
    for {set n 0} {$n <= $nMax} {incr n} {
        set gap  [safe_pow 0.25 $n]
        set logG [safe_log10 $gap]
        set frac [expr {($logG-$lMin)/$lRng}]
        set pos  [expr {$x + $frac*$len}]
        if {$pos < $x-1 || $pos > $x+$len+1} { continue }
        set h    [expr {($n%5==0) ? 2.2 : 1.0}]
        $w create line $pos $y $pos [expr {$y+$h*$dy}] -fill $col -tag $tag
        if {$n%5 == 0} {
            $w create text $pos [expr {$y+$dy*4.5}] \
                -text $n -font {Helvetica 7} -anchor n -fill $col -tag $tag
        }
    }
}

proc drawExpScale {w nm label x y dy nMax len tag} {
    set lMin 0.0
    set lMax [expr {$nMax * log10(2.0)}]
    set lRng [expr {$lMax - $lMin}]
    if {abs($lRng) < 1e-10} { return }
    $w create text $x [expr {$y+$dy*3.5}] \
        -text $nm -font {Helvetica 10 bold} -fill purple -tag $tag -anchor w
    $w create text [expr {$x+$len+5}] [expr {$y+$dy*2.0}] \
        -text $label -font {Helvetica 7} -fill purple -tag $tag -anchor w
    for {set n 0} {$n <= $nMax} {incr n} {
        set logN [expr {$n * log10(2.0)}]
        set frac [expr {($logN-$lMin)/$lRng}]
        set pos  [expr {$x + $frac*$len}]
        if {$pos < $x-1 || $pos > $x+$len+1} { continue }
        set h    [expr {($n%5==0) ? 2.2 : 1.0}]
        $w create line $pos $y $pos [expr {$y+$h*$dy}] -fill purple -tag $tag
        if {$n%5 == 0} {
            $w create text $pos [expr {$y+$dy*4.5}] \
                -text $n -font {Helvetica 7} -anchor n -fill purple -tag $tag
        }
    }
}

proc drawCIScale {w nm label x y dy len tag} {
    $w create text $x [expr {$y+$dy*3.5}] \
        -text $nm -font {Helvetica 10 bold} -fill orange -tag $tag -anchor w
    $w create text [expr {$x+$len+5}] [expr {$y+$dy*2.0}] \
        -text $label -font {Helvetica 7} -fill orange -tag $tag -anchor w
    for {set d 0} {$d <= 1} {incr d} {
        for {set s 1} {$s <= 9} {incr s} {
            set v [expr {$s * pow(10.0,$d)}]
            if {$v < 1.0-1e-9 || $v > 10.0+1e-9} { continue }
            set frac [expr {1.0 - log10($v)}]
            set pos  [expr {$x + $frac*$len}]
            if {$pos < $x-1 || $pos > $x+$len+1} { continue }
            set h [expr {$s==1 ? 2.2 : ($s==5 ? 1.5 : 1.0)}]
            $w create line $pos $y $pos [expr {$y+$h*$dy}] -fill orange -tag $tag
            if {$s==1 || $v==5.0} {
                $w create text $pos [expr {$y+$dy*4.5}] \
                    -text [format "%.4g" $v] -font {Helvetica 7} \
                    -anchor n -fill orange -tag $tag
            }
        }
    }
}

# ============================================================
#  SECTION 1 : CFG PROPAGATION SCALES
#  Canvas .c1  |  Cursor mark1 (blue)  |  Slide tag slide1
# ============================================================

proc buildSection1 {} {
    set w   .c1
    set len 700
    $w create rect 0 8 762 222 -fill grey92 -outline grey80
    $w create rect 0 48 762 178 -fill beige -outline beige \
        -tag {slide1 slide1base}
    $w create text 6 2 \
        -text "SEC 1: CFG  LMB=slide  RMB=cursor  Arrow/Shift-Arrow  | Record_Step to log a reading" \
        -font {Helvetica 8 bold} -anchor nw -fill navy
    $w create line 150 0 150 225 -tag mark1 -fill blue -width 2

    drawContScale $w "Qn" "TREE DEPTH n  (1..1000)"    10  22  5  lin  1    1000  $len  {}     0
    drawContScale $w "Qd" "COLUMN d  (log 1..2000)"    10 105  5  log  1    2000  560   {}     0
    drawContScale $w "Qp" "HIT PROB  ~1/(2n)"          10 185  5  log  1e-4 1     560   {}     0
    drawContScale $w "Qc" "CLASSICAL  <=2^{-n}"       400 185  5  log  1e-12 1   280   {}     0
    drawContScale $w "Qt" "TIME t=n/(2*sqrt2)"         10  63 -5  lin  0.1  1000  $len  slide1 1
    drawContScale $w "Ql" "LAMBDA ~(3/4)^k"            10 143 -5  log  1e-4 1     560   slide1 1

    set xi 18
    foreach n {2 3 5 7 9 15 25 27 97 100} {
        $w create text $xi 216 -text $n -font {Helvetica 7} -anchor s -fill grey50
        incr xi 70
    }

    # Left button: drag slide.  Right button: snap/drag cursor.
    # No logAction in motion bindings -- prevents console flood.
    bind .c1 <ButtonPress-1>  { set ::grab1 %x }
    bind .c1 <B1-Motion>      { .c1 move slide1 [expr {%x-$::grab1}] 0
                                  set ::grab1 %x ; update_s1 }
    bind .c1 <ButtonPress-3>  { snapMark .c1 mark1 %x ; update_s1 }
    bind .c1 <B3-Motion>      { snapMark .c1 mark1 %x ; update_s1 }
    bind .  <Left>             { .c1 move mark1  -2 0 ; update_s1 }
    bind .  <Right>            { .c1 move mark1   2 0 ; update_s1 }
    bind .  <Shift-Left>       { .c1 move slide1 -3 0 ; update_s1 }
    bind .  <Shift-Right>      { .c1 move slide1  3 0 ; update_s1 }
}

# ============================================================
#  SECTION 2 : EIGENVALUE LADDER   k --> lambda_k = (3/4)^k
#  Canvas .c2  |  Cursor mark2 (darkblue)  |  Slide tag slide2
# ============================================================

proc buildSection2 {} {
    set w   .c2
    set len 700
    $w create rect 0 8 762 235 -fill grey95 -outline grey80
    $w create rect 0 48 762 188 -fill #ddeeff -outline #ddeeff \
        -tag {slide2 slide2base}
    $w create text 6 2 \
        -text "SEC 2: Eigenvalue Ladder  lambda=(3/4)^k  gap=(1/4)^n  LMB=slide  RMB=cursor  Ctrl-Arrow" \
        -font {Helvetica 8 bold} -anchor nw -fill darkblue
    $w create line 150 0 150 238 -tag mark2 -fill darkblue -width 2

    drawKScale      $w "Sk"  "COLUMN INDEX k  (0..30)"      10  22  5  30  $len  {}
    drawLambdaScale $w "SL"  "LAMBDA_k=(3/4)^k  slide"      10  68 -5  30  $len  slide2
    drawGapScale    $w "SG"  "GAP=(1/4)^n stator n=0..20"  10 118  5  20  $len  {}  darkgreen
    drawGapScale    $w "SG2" "GAP slide"                    10 165 -5  20  $len  slide2  red
    $w create text 10 226 \
        -text "Ref: k=5 lam=0.2373  k=10 lam=0.0563  k=20 lam=0.00317  k=30 lam=1.78e-4" \
        -font {Helvetica 7} -anchor w -fill navy

    bind .c2 <ButtonPress-1>       { set ::grab2 %x }
    bind .c2 <B1-Motion>           { .c2 move slide2 [expr {%x-$::grab2}] 0
                                      set ::grab2 %x ; update_s2 }
    bind .c2 <ButtonPress-3>       { snapMark .c2 mark2 %x ; update_s2 }
    bind .c2 <B3-Motion>           { snapMark .c2 mark2 %x ; update_s2 }
    bind .  <Control-Left>         { .c2 move mark2  -2 0 ; update_s2 }
    bind .  <Control-Right>        { .c2 move mark2   2 0 ; update_s2 }
    bind .  <Control-Shift-Left>   { .c2 move slide2 -3 0 ; update_s2 }
    bind .  <Control-Shift-Right>  { .c2 move slide2  3 0 ; update_s2 }
}

# ============================================================
#  SECTION 3 : WALK ARITHMETIC
#  Canvas .c3  |  Cursor mark3 (darkred)  |  Slide tag slide3
# ============================================================

proc buildSection3 {} {
    set w   .c3
    set len 700
    $w create rect 0 8 762 262 -fill grey97 -outline grey80
    $w create rect 0 48 762 210 -fill #fff8e0 -outline #fff8e0 \
        -tag {slide3 slide3base}
    $w create text 6 2 \
        -text "SEC 3: Walk Arithmetic  C/D=mult  A/B=sqrt  CI=recip  EX=speedup  LMB=slide  RMB=cursor  Alt-Arrow" \
        -font {Helvetica 8 bold} -anchor nw -fill darkred
    $w create line 150 0 150 265 -tag mark3 -fill darkred -width 2

    drawContScale $w "D"  "D multiply/divide (1..10) stator" 10  22  5  log  1  10  $len  {}     0
    drawContScale $w "C"  "C multiply/divide (1..10) slide"  10  68 -5  log  1  10  $len  slide3 1
    drawContScale $w "A"  "A square/sqrt (1..100) stator"    10 118  5  log  1 100  $len  {}     0
    drawContScale $w "B"  "B square/sqrt (1..100) slide"     10 165 -5  log  1 100  $len  slide3 1
    drawCIScale   $w "CI" "CI 1/x reciprocal (1..10)"       380  22  5  280  {}
    drawExpScale  $w "EX" "EX speedup 2^n (n=0..20)"         10 210  5  20  $len  {}
    $w create text 10 252 \
        -text "C/D: set C(1) over D(x), read D under C(y) = x*y.  CI: 1/cursor.  EX: 2^n speedup." \
        -font {Helvetica 7} -anchor w -fill maroon

    bind .c3 <ButtonPress-1>      { set ::grab3 %x }
    bind .c3 <B1-Motion>          { .c3 move slide3 [expr {%x-$::grab3}] 0
                                     set ::grab3 %x ; update_s3 }
    bind .c3 <ButtonPress-3>      { snapMark .c3 mark3 %x ; update_s3 }
    bind .c3 <B3-Motion>          { snapMark .c3 mark3 %x ; update_s3 }
    bind .  <Alt-Left>            { .c3 move mark3  -2 0 ; update_s3 }
    bind .  <Alt-Right>           { .c3 move mark3   2 0 ; update_s3 }
    bind .  <Alt-Shift-Left>      { .c3 move slide3 -3 0 ; update_s3 }
    bind .  <Alt-Shift-Right>     { .c3 move slide3  3 0 ; update_s3 }
}

# ============================================================
#  READOUT PROCS -- update label text only, no logAction here.
#  logAction is called only by explicit user commands (buttons/keys).
# ============================================================

proc update_s1 {} {
    set cx [lindex [.c1 coords mark1] 0]
    set f  [clamp [expr {($cx-10.0)/700.0}] 0.0 1.0]
    set n  [clamp [expr {1.0 + $f*999.0}]  1.0 1000.0]
    set t  [expr {$n * 0.35355}]
    set qp [expr {1.0 / (2.0*$n + 1.0)}]
    set cp [safe_pow 2.0 [expr {-$n}]]
    set best 2 ; set bestD 1e9
    foreach tn {2 3 5 7 9 15 25 27 97 100} {
        set d [expr {abs($n-$tn)}]
        if {$d < $bestD} { set bestD $d ; set best $tn }
    }
    set ::info1 [format \
        {S1: n=%.1f  t=%.2f  Q_hit=%.4f  C_bound=%.2e  | n=%d sink=%.6f} \
        $n $t $qp $cp $best $::cSink($best)]
}

proc update_s2 {} {
    set cx [lindex [.c2 coords mark2] 0]
    set f  [clamp [expr {($cx-10.0)/700.0}] 0.0 1.0]
    set k  [clamp [expr {$f*30.0}]          0.0 30.0]
    set lam [safe_pow 0.75 $k]
    set gap [safe_pow 0.25 $k]
    set ::info2 [format \
        {S2: k=%.2f  lam=(3/4)^k=%.6f  log10=%.3f  gap=(1/4)^k=%.3e} \
        $k $lam [safe_log10 $lam] $gap]
}

proc update_s3 {} {
    set cx [lindex [.c3 coords mark3] 0]
    set f  [clamp [expr {($cx-10.0)/700.0}] 0.0 1.0]
    set d  [clamp [safe_pow 10.0 $f]        1.0 10.0]
    set a  [clamp [safe_pow 10.0 [expr {$f*2.0}]] 1.0 100.0]
    set ci [expr {$d > 0 ? 1.0/$d : 0.0}]
    set nx [expr {$f*20.0}]
    set ex [safe_pow 2.0 $nx]
    set ::info3 [format \
        {S3: D=%.4f  A=%.4f  sqrt(A)=%.4f  CI=%.5f  EX: n=%.2f  2^n=%.2f} \
        $d $a [expr {sqrt($a)}] $ci $nx $ex]
}

# ============================================================
#  MASTER UI BUILDER
# ============================================================

proc buildUI {} {
    wm title . "Quantum Walk Slide Rule V5r4  --  CFG / Eigenvalue / Arithmetic"

    pack [canvas .c1 -width 780 -height 228 -bg white \
              -relief ridge -bd 1] -fill x -pady 2
    pack [label  .l1 -textvariable ::info1 -fg navy \
              -font {Helvetica 9 bold} -anchor w -justify left] -fill x

    pack [canvas .c2 -width 780 -height 242 -bg #f4f7ff \
              -relief ridge -bd 1] -fill x -pady 2
    pack [label  .l2 -textvariable ::info2 -fg darkblue \
              -font {Helvetica 9 bold} -anchor w -justify left] -fill x

    pack [canvas .c3 -width 780 -height 270 -bg #fffff4 \
              -relief ridge -bd 1] -fill x -pady 2
    pack [label  .l3 -textvariable ::info3 -fg darkred \
              -font {Helvetica 9 bold} -anchor w -justify left] -fill x

    # Player-piano status label
    pack [label .lrec -textvariable ::recStatus -fg darkgreen \
              -font {Helvetica 10 bold} -anchor w] -fill x

    # Button bar -- left to right order as requested:
    # F5-Record | F6-Stop | Record_Step | F7-Replay | F8-Save | F9-Load
    # | C_Help | A_Tone | Re_Start | Exit_P
    frame .bf
    pack  .bf -fill x -pady 3

    button .bf.b1  -text "F5 Record"     -command startRecording  -bg #ffcccc -width 9
    button .bf.b2  -text "F6 Stop"       -command stopRecording   -bg #ffe0b0 -width 9
    button .bf.b3  -text "Record_Step"   -command recordStep      -bg #ffff99 -width 11 \
                   -font {Helvetica 9 bold}
    button .bf.b4  -text "F7 Replay"     -command replayActions   -bg #ccffcc -width 9
    button .bf.b5  -text "F8 Save"       -command saveReplay      -bg #cce0ff -width 9
    button .bf.b6  -text "F9 Load"       -command loadReplay      -bg #e0ccff -width 9
    button .bf.b7  -text "C_Help"        -command showHelp        -bg #d0ffe0 -width 8
    button .bf.b8  -text "A_Tone"        -command playTone        -bg #ffe0ff -width 8
    button .bf.b9  -text "Re_Start"      -command restartProgram  -bg #ffd0a0 -width 9
    button .bf.b10 -text "Exit_P"        -command exitProgram     -bg #ffdddd -width 8

    pack .bf.b1 .bf.b2 .bf.b3 .bf.b4 .bf.b5 .bf.b6 .bf.b7 .bf.b8 .bf.b9 .bf.b10 \
        -side left -padx 2 -pady 2

    # Help label strip
    pack [label .lhelp \
        -text "LMB=slide  RMB=cursor  |  Arrow=S1  Ctrl=S2  Alt=S3  +Shift=slide  |  F5 F6 Record_Step F7 F8 F9  |  C_Help A_Tone Re_Start Exit_P  Esc=restart" \
        -fg grey40 -font {Helvetica 8} -anchor w] -fill x

    buildSection1
    buildSection2
    buildSection3

    bind . <F5>      { startRecording }
    bind . <F6>      { stopRecording  }
    bind . <F7>      { replayActions  }
    bind . <F8>      { saveReplay     }
    bind . <F9>      { loadReplay     }
    bind . <Escape>  { closeLogFile ; exec wish $argv0 & ; exit }
    bind . <Destroy> { closeLogFile }
}

# ============================================================
#  GLOBAL STATE INITIALIZATION
# ============================================================

set ::info1     "Section 1 ready -- set positions then click Record_Step."
set ::info2     "Section 2 ready."
set ::info3     "Section 3 ready."
set ::recStatus "IDLE  (F5=record  Record_Step=capture  F7=replay  F8=save  F9=load)"
set ::grab1     0
set ::grab2     0
set ::grab3     0

# ============================================================
#  MAIN
# ============================================================

initConsole
openLogFile

wm geometry . 800x1000
buildUI
focus -force .

# end of file

Outputs from Active State


Prototype Format, testcase(s) underlies input from previous calculations


SUMMARY TABLE - All 10 Test Cases after 200 Steps



Index Start N Odd Steps k Eigenvalue lambda_k Forward Rate sin2(theta) Sink Probability after 200 steps Swarm Classification
1 2 0 1.000000 0.500000 1.000000 FAST PERCOLATION (lower swarm, conducting phase)
2 3 2 0.562500 0.308658 1.000000 FAST PERCOLATION (lower swarm, conducting phase)
3 5 1 0.750000 0.395552 1.000000 FAST PERCOLATION (lower swarm, conducting phase)
4 7 5 0.237305 0.139377 1.000000 FAST PERCOLATION (lower swarm, conducting phase)
5 9 6 0.177979 0.105827 1.000000 FAST PERCOLATION (lower swarm, conducting phase)
6 15 5 0.237305 0.139377 1.000000 FAST PERCOLATION (lower swarm, conducting phase)
7 25 7 0.133484 0.080104 0.999898 FAST PERCOLATION (lower swarm, conducting phase)
8 27 41 0.000008 0.000005 0.000000 SLOW / LOCALIZED (upper swarm, insulating phase)
9 97 43 0.000004 0.000003 0.000000 SLOW / LOCALIZED (upper swarm, insulating phase)
10 100 7 0.133484 0.080104 0.996806 FAST PERCOLATION (lower swarm, conducting phase)

Model notes and quibble annotations:


forward rate = sin^2(sqrt(lambda_k) * pi/4) computed at EACH node along path.
stay rate    = cos^2(sqrt(lambda_k) * pi/4) at each node.
Each node uses its OWN k value, not the starting node k.
k=0 nodes (powers of 2, near sink): fwd_rate = 0.500000 (fastest).
k=41 (n=27): fwd_rate approx 4e-6 (essentially frozen at 200 steps).
k=43 (n=97): fwd_rate approx 2e-6 (even more frozen).

Quibble on n=27 analogy inversion:
  Eigenvalue formula places n=27 (k=41) in upper swarm: correct.
  But n=27 has a LONG actual trajectory (111 steps, peak 9232).
  A small eigenvalue normally means fast ruin in the gambler model.
  For n=27, small lambda means slow PERCOLATION, not fast ruin.
  The direction of the analogy inverts between the two domains.
  See the parent document table row 14 for full discussion.

Conservation: no YELLOW FLAG expected with this corrected model.
If YELLOW FLAG fires, the advance proc has a floating-point accumulation error.

Further research direction:
  Extend max_walk_steps to 1000 for n=25 and n=100 (k=7) to see whether
  sink probability converges to 1.0 or plateaus below 1.0.
  A plateau below 1.0 would indicate a path escaping the node safety cap.

--- End of Collatz Eigenvalue Percolation Walk V10 ---

Educational summary:


  * Lower-swarm cases reach sink quickly
  * Upper-swarm cases show very slow leakage
  * All runs use full graph with proper absorption, but caps in prototype.
  * Summary capped at 200 steps to avoid long waits

End of Auto-run for 10 Test Cases






Mockup Displays for Pseudocode Development


for start_node = 27, see why caps are needed?


Key nodes along Collatz path: 27 -> 82 -> 41 -> 124 -> 62 -> 31 -> 94 -> 47 -> 142 -> 71 -> 214 -> 107 -> 322 -> 161 -> 484 -> 242 -> 121 -> 364 -> 182 -> 91 -> 274 -> 137 -> 412 -> 206 -> 103 -> 310 -> 155 -> 466 -> 233 -> 700 -> 350 -> 175 -> 526 -> 263 -> 790 -> 395 -> 1186 -> 593 -> 1780 -> 890 -> 445 -> 1336 -> 668 -> 334 -> 167 -> 502 -> 251 -> 754 -> 377 -> 1132 -> 566 -> 283 -> 850 -> 425 -> 1276 -> 638 -> 319 -> 958 -> 479 -> 1438 -> 719 -> 2158 -> 1079 -> 3238 -> 1619 -> 4858 -> 2429 -> 7288 -> 3644 -> 1822 -> 911 -> 2734 -> 1367 -> 4102 -> 2051 -> 6154 -> 3077 -> 9232 -> 4616 -> 2308 -> 1154 -> 577 -> 1732 -> 866 -> 433 -> 1300 -> 650 -> 325 -> 976 -> 488 -> 244 -> 122 -> 61 -> 184 -> 92 -> 46 -> 23 -> 70 -> 35 -> 106 -> 53 -> 160 -> 80 -> 40 -> 20 -> 10 -> 5 -> 16 -> 8 -> 4 -> 2 -> 1


Final probability at sink (node 1): 0.00001234


Probability on Key Collatz Nodes Over Time (only > 1e-6)


Step Sink (1) 27 82 41 124 ... (truncated)
0 0.00e+00 1.00e+00 0.00e+00 0.00e+00 0.00e+00 ...
50 1.23e-08 2.45e-04 1.12e-05 4.78e-07 0.00e+00 ...
100 5.67e-07 8.91e-06 3.45e-08 1.23e-10 0.00e+00 ...

Final probability at sink (node 1): 1.000000


Probability at sink (node 1) vs time


t=  0 |  0.00000
t=  1 |  0.50000
t=  2 |  1.00000
t=  3 |  1.00000

...

Test Energy E (eV) – Classical-like Incident Energy Transmission T – Quantum Tunneling Probability Barrier Analog Width (nm) – Effective Disordered Barrier
1 5.0 1.0000 0.0 1.0000 0 extended (conducting) high eigenvalue → high energy → T near 1 (conducting regime)
8 1.0 0.0564 41.0 0.0000 >1000 localized (insulating) k=41 → extreme tunneling suppression, very strong localization *negative clamped*

Final probability at sink (node 1): 1.000000

Probability at sink (node 1) vs time

t=  0 |  0.00000
t=  1 |  0.00000
...
t= 10 |  0.00000
t= 11 |  1.00000
t= 12 |  1.00000
...
t= 80 |  1.00000

Testing Quantum SlideRule


This is a draft. Adapted from Richard Suchenwirth's "A little slide-rule" (2003)


Here is a Tk/Tcl adaptation of the classic slide-rule toy from the Tcl wiki (https://wiki.tcl-lang.org/page/A+little+slide%2Drule ), re-themed and extended to serve as a conceptual "Quantum Walk Slide Rule" inspired by the Childs-Farhi-Gutmann glued-trees graph and continuous-time quantum walks.



gold 2/9/2026. Added categories, so can find message in Wiki.



Hidden Comments Section


Program Change Log

gold 2/3/2025. Testing, encountered initial difficulty in saving work? Long code blocks with or unmatched wiki markup can sometimes confuse the Tcl Wiki formatting engine, especially if fences are not balanced or a line begins with markup it treats specially.


gold 2/14/2026. Added Automatic Dump of Examples, Using ActiveState.


gold 2/14/2026. convert to strict 7-bit ASCII for Playground V9. reporting error at bottom. program should run to completion with automatic test suite.


gold 2/14/2026.



gold 3/7/2026. convert to strict 7-bit ASCII for Playground V9. variables need to be human readable and very explanatory. avoid variables with single letter names. Assume a future maintainer either AI or human would have to maintain code with info content in program. the program is working the numbers correctly . so minimal changes.


gold 3/10/2026. Other than a clipping function or a number clamp { y =< limit } in tcl program, not sure how to separate lower solutions band from upper solutions band. Are you able to produce 2 sets of x,y columns for fitting upper and lower solutions, from the 500 points? Referee my weak eyes, but seems real possibility that quantized levels of solutions could be intermixing?


Matrix of Collatz solutions look like two swarms of bees rather a single linear solution or even look like multiple fuzzy levels of solution ranges, eg. non-linear solutions, observable in various pngs. You can tell me different. Based on long experience of fitting equations in engineering, possibly the probabilistic reasoning or pattern matching on quantum solutions plural is more adaptable.



gold 3/25/2026. I’m having trouble moving the blue lines in each section. I need to move the slides with the left mouse button, and move the blue lines in each section with the right mouse button. Possibly a mouse‑down event would be useful for both the sliders and the blue lines.” We have previous programs that threw commands & valuations to console window, would make for some hefty ode, 580 lines or so now. A little tricky, but we could store commands or slide actions by human "expert" and then replay that stored algorithm, sort of like an automatic slide rule or piano player style.


gold 3/25/2026. A little too many fire‑offs. Need a single resting valuation for each mark or slide. This output file should read like a list of commands to a calculator machine. Suggest the protocol should include a Record_Step button that reports each step as a final instruction to both the file and the console. alternate text >>> There are too many automatic fire‑offs. Each mark or slide needs a single stable resting value. The output file should behave or look like a sequential command list for a calculator engine. A Record_Step button would help: it would capture the current state and write one final instruction per step to both the console and the output file.”


# trial output
Quantum Walk Slide Rule V5r2 -- console ready.
09:53:16  === Session start ===
09:53:23  S3:  D=1.5849  A=2.5119  sqrt(A)=1.5849  CI=0.63096  EX: n=4.00  2^n=16.00
09:53:23  S3:  D=1.5849  A=2.5119  sqrt(A)=1.5849  CI=0.63096  EX: n=4.00  2^n=16.00
09:53:23  S3:  D=1.5849  A=2.5119  sqrt(A)=1.5849  CI=0.63096  EX: n=4.00  2^n=16.00


Please place any comments here with your wiki MONIKER and date, Thanks.gold 3/4/2026



Note. Testing computer methods and computer programs, maybe wrong numbers.