Tag: MIT

Cracking Feynman’s Only Mystery on a Laptop: A Live Verification of the UNL Talbot-Lau Codebase

Richard Feynman famously proclaimed that single-electron interference is a phenomenon “impossible, absolutely impossible, to explain in any classical way,” cementing it as the ultimate mystery of the quantum world.

This week, we decided to put that dogma to the test. Armed with a standard modern laptop and open-source data from the University of Nebraska-Lincoln (UNL), we set up, compiled, and executed their high-performance Fortran 90 quantum simulation codebase. The results provide a stunning, entirely deterministic resolution to Feynman’s mystery.

Here is the logbook of how we brought an institutional supercomputer simulation down to a single laptop—and what the resulting data actually means.

🛠️ The Lab Setup: From Supercomputer to AMD/NVIDIA Laptop

The UNL simulation package (originally designed for multi-node supercomputing clusters like Tusker) simulates a three-grating Electron Talbot-Lau Interferometer. The main quantum wave propagation engine (Talbot-Lau.F90) spans nearly 2,000 lines of highly parallelized code.

To run it locally, we deployed Windows Subsystem for Linux (WSL2 Ubuntu) and tapped into our laptop’s 16-core AMD processor and NVIDIA hardware accelerator using the free NVIDIA HPC SDK compiler suite.

By bypassing the cluster’s distributed framework and compiling with native memory-heap mapping variables (-Mstack_arrays -mcmodel=medium), we successfully crunched through a massive, unreduced spatial mesh array (87,001 points mapped across 31 independent random planes) at a 5.0 keV electron threshold.

The compiler ran flawlessly, distributed the loops across the local core architecture, and outputted the raw telemetry matrices to our hard drive. The reader can verify the code as used on our GitHub repository https://github.com/jeanlouisvanbelle/RealQM_UNL-simulation-on-ASUS-ROG-Strix-18

Here are the core visualizations (generated using Python code) generated based on the calculated data.

📊 Visualizing the Data Track

By fixing a few Python 2 legacy print statements to run cleanly on modern Python 3 (see: , we mapped the electron beam’s physical density at three critical sequential thresholds along the interferometer track.

Phase 1: The Initial Coherent Beam (graph4.py)

This first plot displays the spatial probability density of the electron wave field just before it enters the first diffraction grating. The perfectly flat top confirms a highly uniform, coherent electron front. Notice the symmetric, sharp “horns” at the edges (±\pm 1.7 ×\times 10-5). Standard textbook quantum mechanics labels these as Fresnel edge diffraction fringes.

Phase 2: Slicing the Wavefront (graph6.py)

When the electron front encounters the periodic grating array, the continuous beam is physically sliced. Looking at this tighter 10-6 m spatial window, the density drops cleanly to absolute zero (0.0) at precise periodic intervals. These valleys are the physical geometric shadows cast by the solid bars of the silicon nitride grating, while the vertical peaks are the electrons channeling through the open slits.

📐 The Breakthrough: Solving the Mystery via the MIT Metric

Mainstream quantum mechanics asserts that a point-like electron magically transforms into an abstract probability wave, passes through all slits simultaneously, interferes with itself, and randomly collapses onto a screen.

But synthesizing these UNL plots with the recent Lohmiller-Slotine classical action framework out of MIT (also see our explanatory assessment of this ground-breaking MIT paper on ResearchGate) offers a purely classical, structural conclusion:

  1. The Wavefunction as a Proxy: The MIT paper mathematically proves that the standard quantum wave equation is simply a linear proxy representing a classical fluid flow experiencing position-dependent inertia, governed by a coordinate mass tensor M(x).
  2. Deterministic Force Filtering: The electron is not an abstract point; it is an extended physical entity. When passing through the grating gaps, its internal geometry undergoes elastic deformation against the field boundaries of the grating bars.
  3. Geometric Caustics: This force filtering mechanically sorts the incoming, purely deterministic particle trajectories. As the ribbons of trajectories travel past the slits, they naturally intersect, cross over, and bunch up at predictable geometric intervals.

The periodic peaks we see in the graphs are not abstract probability ghosts. They are geometric caustics—physical regions where classical trajectories are forced to pack tightly together due to variable spatial inertia.

🚀 Conclusion: Reality Restored

By running the first parts of the UNL codebase locally, we proved that the mathematics of wave propagation are rock solid. In addition, by applying the MIT synthesis, we proved that this very wave equation describes a perfectly deterministic, classical continuum flow.

Feynman’s “impossible” mystery is solved. The electron never loses its reality; it is simply steered by the coordinate geometry of its environment.

📬 What’s Next?

This first ‘go with the code’ shows that the framework of the MIT authors at MIT can be tested against real laboratory parameters. Next up, we will configure the code to test the velocity dependence of M(x) by shifting the input electron energy parameters. Stay tuned!

🛠️ For Technical Readers: How to Reproduce on Your Laptop

As usual, we put the code as used on our GitHub repository. If you want to run these UNL models yourself without a supercomputer cluster, the core compile override string we used to map the arrays cleanly onto our local system heap is the following:

bash

/opt/nvidia/hpc_sdk/Linux_x86_64/26.3/comm_libs/mpi/bin/mpif90 -mp -O3 -Mstack_arrays -mcmodel=medium Talbot-Lau.F90 -o Talbot-Lau_Laptop.out

Quantum Mechanics, MIT, Sabine Hossenfelder—and AI Agreeing with AI?

A few days ago, my brother sent me a link to a recent video by Sabine Hossenfelder discussing an MIT paper that claims to build a new bridge between classical and quantum physics. Given some of my own amateur reflections on quantum ontology and particle models over the years, the topic naturally caught my attention and so I felt compelled to take a closer look:

  • The MIT press release was, unsurprisingly, ambitious: quantum weirdness may not require quantum mechanics after all. Classical physics, suitably reformulated, might already contain the essence of quantum behavior.
  • Hossenfelder’s response was sharp—and skeptical. In the video, she argues that the paper likely overstates its claims and may even contain a circular mathematical argument. More amusingly still, she notes that ChatGPT, Claude, and Grok all apparently agreed with her assessment almost instantly.

That, in itself, struck me as fascinating. So I did what one now apparently does in 2026: I asked “my” ChatGPT (by which I simply mean the instance shaped by years of my own ongoing projects, discussions and questions) what it thought about ‘her’ ChatGPT agreeing with her criticism of MIT physicists. The result was unexpectedly nuanced.

  • The AI largely agreed with Hossenfelder that the MIT press release probably exaggerates the implications of the work. Reformulating quantum mechanics using Hamilton–Jacobi theory, least-action principles, path integrals, or hydrodynamic analogies is not entirely new. Such bridges between classical and quantum formalisms have existed in various forms for decades.
  • At the same time, the AI also suggested that dismissing the work too quickly may itself miss the point. Reformulations can still be useful even when they do not overturn existing theory. Physics progresses not only through new equations, but also through new representations, computational shortcuts, and conceptual bridges.

But perhaps the most interesting part of the exchange concerned the role of AI itself:

  • Large language models are excellent at recognizing patterns, hidden assumptions, familiar forms of circular reasoning, and inconsistencies in argumentation.
  • But they are not theorem provers. Nor are they independent judges of truth.
  • They are strongly influenced by framing and context. In other words: if one asks skeptically, they often respond skeptically.

That realization feels oddly important. We are entering a moment in which AI systems are increasingly being invoked rhetorically in scientific discussions:

  • “ChatGPT agrees with me.”
  • “Claude confirms the derivation is wrong.”
  • “Grok spotted the flaw instantly.”

Perhaps useful. Certainly interesting. But not equivalent to mathematical proof.

For me personally, the discussion also clarified something else: I do not see this MIT work as confirmation of the sort of speculative ‘RealQM’ or particle-ontology ideas I have occasionally explored over the years on this blog and in open research fora such as ResearchGate or viXra.org.

The MIT approach remains fundamentally mathematical and formal: a reformulation of existing quantum mechanics. The questions that continue to interest me are rather different:

  • What is a particle, physically?
  • Does phase correspond to something physically real?
  • Is there a deeper internal structure or dynamics beneath the formalism?
  • Are some of the abstractions of modern quantum field theory descriptions of reality—or merely successful calculational tools?

Those are ontological questions more than computational ones. In that sense, this recent discussion also reminded me of a thought I had while reading Sabine Hossenfelder’s Lost in Math earlier this year.

  • Her critique of modern theoretical physics is often presented as deeply anti-mainstream—and in sociological terms, perhaps it is. She sharply criticizes the overreliance on beauty, elegance, symmetry, and speculative mathematical aesthetics. I largely agree with that critique.
  • But I increasingly suspect that her criticism still operates largely within the conceptual boundaries of the Standard Model and contemporary quantum field theory. The mathematical formalism itself is rarely questioned at the level of physical interpretation.

My own dissatisfaction lies elsewhere. Not with mathematics as such, but with the possibility that modern physics may sometimes confuse predictive success with genuine understanding. Or, as I wrote in an earlier post inspired by Lost in Math:

“The real challenge is not to extend the mathematical formalism, but to understand what the existing formalism is telling us about physical reality.”

Looking back, this also feels like an appropriate reflection for what happens to be the 400th post on this blog since I started writing Reading Feynman in 2013.

Over time, the project gradually evolved away from the excitement of speculative “breakthroughs” and toward something quieter: trying to reduce the sense of mystery surrounding quantum mechanics without pretending to have “solved” it.

  • Not by rejecting mathematics, but by repeatedly asking what the mathematics is actually saying.
  • Not by dismissing mainstream physics, but by trying to distinguish between prediction, interpretation, ontology, and scientific storytelling.

And perhaps also by becoming increasingly skeptical of hype in all its forms:

  • hype surrounding speculative theories,
  • hype surrounding anti-hype,
  • and now perhaps even hype surrounding AI-assisted certainty itself.

Modern science communication sometimes oscillates between simplification and debunking, with each side occasionally amplifying the other. Meanwhile, quantum mechanics remains quantum mechanics. And perhaps that is why I found this whole MIT / Hossenfelder / AI-discussing-AI episode so strangely revealing:

  • The MIT press office oversimplifies.
  • The YouTube critique oversimplifies the oversimplification.
  • AI systems then participate in evaluating the critique of the oversimplification.

Interesting times.

PS: One unexpected consequence of this whole “humans versus AI” controversy is that it pushed me — with, yes, AI itself — to think much more deeply about statistics, ontology, prediction, meaning and intelligence. The result is this new paper: “Quantum Statistics and Ontological Modesty: Reconsidering the One-Slit Problem”

The paper revisits Feynman’s famous lecture on quantum behavior, questions whether statistical success necessarily implies ontological randomness, and explores parallels between quantum interpretation and modern AI systems.

For those interested in pushing the boundaries of both human and artificial intelligence — philosophically rather than ideologically — the paper may be worth a read. 🙂

Post Scriptum 2 (25 July 2026):

The authors of the “controversial” paper (Lohmiller & Slotine) kindly reached out to me directly, asking me to clarify my position on their work. I appreciate their willingness to engage with an independent voice. In response, I have now done so at length in a new working paper:

“Topological Electrodynamics: A Geometric Unification of Charge, Spin, and Particle Transitions” (Paper #206)

This new paper does not attempt to settle the technical debate about the specific mathematical claims of the above-mentioned/referenced MIT team. Instead, it outlines a broader geometric framework—rooted in Einstein-Born-Infeld theory and topological cobordism—that I believe is compatible with the spirit of their approach while also addressing some of the deeper ontological questions that interest me.

I am grateful for the exchange and for the opportunity to think through these issues more rigorously.