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.
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.
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.
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.
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.
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!
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.
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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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.
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. ---
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?
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.
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.
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.
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.
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, 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.
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:
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.
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.
| 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.
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.
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 |
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 |
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 |
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.
| 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 |
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.| 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) |
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 |
| 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 |
| 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.
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.
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 |
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) |
| 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 |
Note. These Snippets on Theoretical Physics are a set, not stand alones. Recommend read all of the set.
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.
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.
**** 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 |
+----------------------------------------------------------------------------------+
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.
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 fileSUMMARY 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
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
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.
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.
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