NSF Accepted my Grant Pitch for Studying Quantum Brain Effects

The key to understanding how the brain operates so efficiently at only 20 watts of electricity could come from a better understanding of how migratory birds' internal compass helps them navigate seasonal flights.

While attending the University of California, Berkeley in 2010-2012 I was interested deeply in the topic of what distinguishes the human mind from machines, and what endows people with agency. I assumed the physics by which the brain works must necessarily be different than computers to allow free will. I was introduced to the work of Dr. Penrose on the topic, and later concluded under Dr. Vopson's research group in the UK in a publication that to emulate human consciousness would be an NP-hard problem - intractable by both classic and quantum computers - on par within NP-hard post-quantum lattice cryptography. 

Before coming to Colorado, I traveled the country living in a small off-grid trailer I built attached to my car through both the Iowa winter (which was colder than the arctic) and through the Arizona desert. One hot afternoon I went to the science center in Phoenix Arizona where a young woman named Aurora was presenting on the topic of autonomous aerial vehicles and I told her about my situation. She recommended for me to apply to Aurora Flight Sciences - a research and development subsidiary of Boeing where I then got an offer. 

Combining what I learned at Boeing with what I learned in college and with the inspiration from along the way, I conceived of a new technology - the same physics I had thought about at Berkeley is responsible for the internal compass (or "Liahona") of migratory birds which can be appropriated towards developing novel autonomous sensor and navigation systems. I have since published on this topic and presented at the APS conference, and was invited to present at the TSC conference directly by Dr. Hameroff this year. I have discussed this research with Dr. Hore at the University of Oxford and Dr. Thorsten Ritz at the University of California Irvine agreed to partner on this research, and my pitch was accepted by the NSF for a proposal submission. 

The idea is that the information migratory birds sense from their environment that helps them navigate gets encoded onto optical/spin systems in their brain tissues, and this is also the same mechanism that the human brain uses to encode information that performs the equivalent of backpropagation so efficiently across the tissue macroscopically at scale. I have spent the past decade and a half building a research program around that mechanism, both as physics worth finishing and as a sensor worth making that should help autonomous aerial vehicles navigate in GPS denied environments. I want to set down plainly what is solid, what is not, and where I think the genuinely interesting risk sits, because the field tends to blur those three together and the blurring helps no one.

The mechanism, which is the part no longer in serious doubt

A photon is absorbed and drives an electron from one site of a molecule to another. That leaves two unpaired electrons, one on each fragment, and the pair is born in a correlated singlet state, meaning the two spins are anticorrelated in a specific quantum sense. The pair does not sit still. It oscillates coherently between that singlet arrangement and a triplet one, and the clock that drives the oscillation is the difference between the magnetic environments the two electrons feel. Each electron is coupled to nearby nuclear spins through the hyperfine interaction, the couplings differ between the two radicals, and that mismatch is what interconverts singlet and triplet. An external magnetic field shifts the triplet energy levels and so changes how far and how fast that interconversion runs. Singlet and triplet pairs then recombine into different chemical products, so the field leaves its mark as a change in how much of each product the reaction makes.

None of that, on its own, is a compass.

A compass needs direction, and direction comes from the fact that the hyperfine couplings are anisotropic. The rate of singlet to triplet mixing depends on the angle between the molecule and the field, so the product yield carries heading rather than mere field strength. Hold the molecules still and the yield becomes a function of orientation. Let them tumble freely and the anisotropy averages straight back to zero, which is the single most important practical fact in the whole subject.

The biology lands on flavin chemistry. The carrier is cryptochrome, a protein in the retina, and the field acts on a radical pair formed between its flavin cofactor and a chain of tryptophan residues. Henk Maeda and Christiane Timmel and Peter Hore and their colleagues showed back in 2008 that a sub-millitesla field changes the lifetime of a radical pair in a synthetic carotenoid donor and fullerene acceptor triad, the first chemical compass built on a bench. Kerpal and coworkers pushed the directional response down to Earth strength about a decade later. Emily Xu and the Oldenburg and Oxford groups showed in 2021 that cryptochrome four pulled from a migratory robin is magnetically sensitive in a test tube, and more so than the same protein from birds that do not migrate. The mechanism is real, it is specific, and it has names attached to every step.

What is not solved, and why that is the opportunity

Here is the gap that I think is worth a career. Every clean directional measurement to date has required freezing the sample, somewhere down near a hundred kelvin, precisely to stop the tumbling that would otherwise erase the anisotropy. The bird does this at body temperature inside a structured protein, and we cannot yet reproduce that on a bench without the cold. The field effect that survives at room temperature in the lab is a percent level change in fluorescence, beautiful and real but a long way from a heading you would trust over open water.

Many so-called "quantum theories of mind" like those described by Dr. Penrose often paper over this gap, famously criticized by Max Tegmark as implausible in brain tissues - as the brain is warm, wet, and noisy which is inhospitable for macroscopic quantumlike effects to occur. Understanding how the avian compass works provides an avenue for probing this mechanism at play where controlled information about the environment is fed through the tissue.

So the near term target of the program is narrow and concrete. Build a calibrated, reproducible, room temperature optical readout of the radical pair field effect in cryptochrome and flavoprotein systems, with no cryogenics and no magnetic shielding beyond ordinary background control, and state its field resolution in real units rather than in arbitrary contrast. The field effect surfaces in fluorescence yield and in fluorescence lifetime, and lifetime is the better handle because it is far less sensitive to how much sample you have and how bright your source is. Time resolved methods, and phasor analysis in particular, can pull a small lifetime shift out of a messy signal without committing to a fitted model in advance. Akira Antill and Maeda reached roughly two parts in a thousand on fluorescence near the single photon level in 2025, which tells you the sensitivity is within reach and that nobody has yet turned it into an actual fielded transfer function at room temperature. That transfer function, yield and lifetime against field magnitude and orientation, is the deliverable. A clean negative would be informative too, because it would set an honest bound on how far the optical route can go.

This is not a confirmation of someone else's result. A room temperature, field referred lifetime transfer function for a flavoprotein radical pair, with resolution stated in tesla per root hertz, does not exist in the literature. It is the first measurement of its kind, it is publishable on its own terms, and it is the rung that everything more ambitious has to stand on.

The frontier, fenced off honestly

Now the part I find hard to stop thinking about, which I will mark clearly as hypothesis so that no one mistakes enthusiasm for evidence.

The readout above treats the emitted light as a meter needle, a brightness or a decay rate that we read off. The deeper question is whether the light carries more than that. The spin state of a radical pair has structure beyond a single yield number, structure that in principle could appear in the polarization of the photons it emits, and a growing body of theoretical work asks whether dense networks of biological emitters can radiate cooperatively rather than independently. If field dependent spin information is genuinely imprinted on emitted or collectively emitted light, then light stops being a passive meter and becomes a channel, something that moves information from one place to another inside tissue.

That idea has a clean and falsifiable first test, and I value it precisely because it can fail. If the magnetoreception signal is encoded on emitted light, then selectively blocking or scrambling that light should measurably degrade the field response, while leaving the rest of the chemistry intact. An intervention and a readout, with a real null available. That is an experiment, not a manifesto.

I will say the harder thing out loud, because the field needs people to say it. Showing that light carries a field dependent signal is not the same as showing that anything computes with that signal.

A wire carrying a voltage is not yet a circuit doing arithmetic. There is a long and genuinely speculative arc that runs from optical signaling, to the idea that such signaling underlies the strange efficiency of neural tissue, to the much larger claim that something in the brain implements the equivalent of error driven learning by optical means. I find that arc compelling as motivation. I do not present it as established, and neither should anyone else, because backpropagation in the brain is unresolved by any mechanism at all, electrical or optical, and you cannot demonstrate that light implements a process that has not been shown to occur in the first place. The honest structure is to treat the encoding experiment as a necessary precondition for the larger story and nothing more. If light carries no functional signal, the larger story is dead. If it does, you have earned exactly one step, and the next step is a separate and much harder experiment.

Why this is important

Because the payoff at the near end is real and the payoff at the far end is enormous, and the two are connected by a sequence of measurements you can actually run rather than by assertion. A passive, room temperature, unshielded magnetometer with no moving parts would matter on its own, in navigation that cannot lean on satellites and in sensing where cryogenics and heavy shielding are not options. And the same readout, made sensitive enough, is the instrument you would need to ask the wilder question at all.

The bird already solved this. It runs a quantum mechanical measurement in warm, wet, disordered tissue, reliably enough to bet its life on twice a year amplified to a macroscopic environment. We have not matched that on a bench, and we do not fully understand how it is done. Closing that gap is worth doing carefully, one rung at a time, with the speculative weight kept off the rungs that have to bear load. That is what this program is, and that is the order I intend to build it in.

Brain Backpropagation Doesn't Work Like AI Architectures

A presentation on consciousness, post-quantum cryptography, the black hole information

paradox, and why the dominant singularity narrative may be inverted. Prepared for the

American Physical Society and Information Physics Institute conferences 2026.

Video presentation: https://youtu.be/Zr4Q27MFsRY

These supposedly "fringe" or "speculative" or "unverifiable" ideas have been taken seriously by the world's top governments, scientists, leaders, and corporations.

Introduction

Today I will be presenting on a somewhat controversial topic which I've been thinking deeply

about for the past 15 years. It is important to note that the topic I will be discussing is

controversial, and yet, in spite of being built on an ostensibly niche or fringe theory that has

been widely dismissed, it has somehow also captured the attention of the world's top academics, corporations, executives, governments, and leaders, where it has been taken very seriously. It's an opinion that's been banned largely from many Reddit communities and LessWrong, though it isn't clear why.

The Question

We begin like with most scientific investigations with a question, and that question is: what

makes the way the human brain works different than machines or current AI architectures built

on transformers? How can we approach this scientifically to build a predictive model? Or put

differently, what gives us consciousness, and thus also the right of moral agency, and can we

understand this empirically?

This is a topic which is particularly relevant right now, as large tech companies would like to

have you buy on faith that we are reaching a so-called "technological singularity" where

machines will outwit the masses, and where we will become enslaved by them. They would like

to convince you that AI programs have agency on their own, and that we must give rights to

them (possibly to collectively outcrowd human agency as a marketing gimmick).

I would like to make the argument instead that, rather than reaching a point where we will be

outwitted by machines, this so-called "technological singularity" as it is called is the point at which our collective intelligence, otherwise known in academic literature as CI, surpasses the

games being played on us by these thought leaders, and we discover that we have been outwitted

the entire time.

This is to say that information throughput in groups scales faster than the performance of these

AI surveillance architectures per unit of energy, which is ultimately derivative and reaches

asymptotic limits of scale. This has been explored in more detail in books like Joseph Tainter's

The Collapse of Complex Societies, and through the phenomenon known as interbrain

synchrony and explored in greater detail through catastrophe theory and social laser theory

(which I admit sounds like a sort of fanciful academic field).

Why should we take their word for it and prop up our entire economy on this technology with

billions of dollars in spending on outlandish or infeasible plans, like Mark Zuckerberg's desire

for data centers the size of Manhattan or Google's alleged plans for data centers launched into

orbit, each with their own dedicated power plants which strain our resources, when by many

estimates the human brain is hundreds of thousands of times more efficient at compute, and we

cannot even house, support, or train our own people?

There are plans by researchers like Yann LeCun for so-called world models, where we are

supposed to place AI-endowed robotic agents into society and teach them how to take human

jobs. And yet years later, with enormous amounts of training data, we still don't have reliable

self-driving cars, and we don't even have the resources or patience to teach or raise our own

children.

There has been even more widespread criticism of the 2024 Nobel Prize in Physics, awarded to

John Hopfield and Geoffrey Hinton for their theory of machine learning. Rather than teaching

us about the laws of nature by empirical means which could falsify predictions, critics have

argued that their work does not reflect the science of fundamental physics, but rather is a work

of computer science with a heuristic architecture imposed onto it, which is reflected only in

simulations, and which ultimately requires humans with attention in the loop to function and

interpret data. Nature, however, continues to surprise us. We cannot rely on mere simulations,

or we risk leaving room for a god of the gaps and a devil in the details.

Machine Learning Stirs Controversy in Nobel Prize in Physics - Harvard Technology Review

Some have even made the argument that the current AI, mass surveillance, black hole,

cybernetics, or ouroboros bubble we are seeing today has been in the works for at least as long as

I have been thinking about this foundational problem, and that this Nobel Prize has been used

to rationalize its mass adoption, financially and socially engineering total and complete control

over societies, or crowding out and restricting human agency and subverting democratic

institutions.

How I Got Here

This leads me to my own involvement with all of this. Contrary to the views of Stephen Hawking

back in 2010, I had a debate with one of my colleagues at UC Berkeley where I used the

infamous Gödel's incompleteness theorem to skeptically argue that the way in which the brain

works to generate consciousness likely implicates new physics, or must be different, or

ultimately non-algorithmic, and therefore could not be simulated by a computer, or that a

simulation would otherwise be too lossy a compression to be fully useful. I was then introduced to the work of Dr. Penrose, who to my surprise came to the same conclusion based on Gödel's theorem, with about as much controversy as you might imagine, even going so far as to implicate bizarre ideas about quantum gravity and macroscopic quantum-like effects you might see with phenomena like quantum chaos, but in brain tissue.

While some have pointed out that Penrose's application of Gödel's theorem has been contested here, I use it as inspiration, and looking at the critiques of it, one might just as easily craft cogent ontological counter-counter-arguments with infinite egress which reveals more about the physics of the brain as represented mathematically by noncommutative tori (like an ourborus snake eating it's own tail) and how the process of thought actually works. Whoever is willing to stipulate more axioms can extend the chain further. In philosophy of mathematics, this is sometimes called the Münchhausen trilemma where any chain of justification either circles, regresses infinitely, or terminates in something accepted without further justification. In essence, in our case this is more of a critique of mathematics or computer science in themselves to model reality where it blurs into the realm of physics and the cycle of thought and measurement.

This idea that with the brain we are dealing with things like macroscopic quantumlike effects would seem to be implausible because they would seem to obviously decohere in wet,

warm, and noisy environments like the brain, which I considered to be a major challenge to the

theory and its original formulation. I was nonetheless intrigued by this idea.

The next year I started finding an interest in quantum gravity physics and lattice mathematics,

as I was a student of Fields Medalist Dr. Richard Borcherds who specializes in this area and

proved the famous monstrous moonshine conjecture. As it turns out, later in 2018 when I was in

Boulder, Colorado, the National Institute of Standards and Technology formalized post-

quantum cryptography based on the fact that certain lattice problems like the shortest vector

problem are NP-hard under random reductions, making them impossible to tractably solve by

either quantum or classical computers under known assumptions in either established classical

or quantum physics.

This interested me because it has always been my position that having any class of truly

unbreakable encryption above scrutiny is too hubristic a request of the universe for any group of

elites to use to hoard secrets.

Four Problems, One Shape

Connecting this back to our main question, there is an interesting connection that you can make

between the black hole information paradox, post-quantum cryptography, the shortest vector

problem, and the problem of consciousness. In fact, what you will find is that in academic

literature these problems have all been framed as related or equivalent. In some cases the

research of a scientist, for example into the black hole information paradox, could at least in

theory be funded or appropriated toward developing cryptography. The research of a

mathematician or physicist into string theory or lattice mathematics could likewise be

appropriated toward constructing AI surveillance systems, both without them or the public even

knowing.

A Computational Perspective on Visual Attention | Books Gateway | MIT Press

https://arxiv.org/pdf/2211.05491

From Black Holes Entropy to Consciousness: The Dimensions of the Brain Connectome

[hep-th/9411193] Information Loss in Black Holes and/or Conscious Beings?

What we know is that the physics for how to resolve these problems is not well-established or

widely known. We know for example that the black hole information paradox is like

cryptography. Information flows or falls into a black hole, but does not obviously have a means

of escape. And yet quantum mechanics tells us information unitarity must be preserved.

The hard problem of consciousness and the binding problem in literature has been mapped by

some researchers like Tsotsos to the shortest vector problem over either a high-dimensional

lattice or its geometric equivalent, which is a non-commutative torus, both of which seem to

map to the discovery of these geometric structures in the brain, both by the Blue Brain Project

and by the work that awarded Edvard and May-Britt Moser, as well as John O'Keefe, the 2014

Nobel Prize in Medicine for their discovery of lattices of grid cells in the brain.

Some recent researchers have attempted to contest Tsotsos'work (unconvincingly in my

opinion). The problem with Tsotsos' detractors is that strictly hierarchical or merely

approximate models of perceptual binding (like predictive coding) assume that the brain

successively pools local features into ever more complex conjunctions until a unified object code

emerges. Empirical evidence now shows this feed-forward scheme is insufficient: MEG and

fMRI reveal that the moment at which features are bound coincides with the onset of late,

recurrent activity that re-enters early visual areas, and when these feedback loops are disrupted

pharmacologically or with Transcranial Magnetic Stimulation (TMS), illusory contours,

perceptual switching and contextual grouping all collapse even though the putative hierarchical stages remain intact. These give local learning rules that approximate backprop but don't address the physics of the measurement problem or binding problem.

Tsotsos mapped visual attention complexity to an NP-hard problem in a computational-complexity sense which is what we are proposing to deal with here.

When researchers looked at the problem of the black hole information paradox, they started to

find that monstrous moonshine and macroscopic black hole entropy are connected by the

holographic description of quantum black hole microstates, with many new theories of quantum

gravity claiming that gravity may actually be an entropic or thermodynamic force. The Cardy

formula describes the asymptotic density of states in a 2D conformal field theory, providing a

microscopic derivation of the Bekenstein-Hawking black hole entropy.

Crucially, to solve the problem of needing information to be both publicly hidden but also

uniquely accessible, the idea of secret black hole information islands was introduced. These are

supposed to be regions inside a black hole's event horizon that, according to recent quantum

gravity research, are holographically encoded in, and possibly entangled with, leaking Hawking

radiation. These so-called hidden islands of entanglement entropy emerge as crucial

components in calculating the entanglement entropy of radiation, resolving the black hole

information paradox by allowing information to both be trapped within a black hole but also

escape in an encoded fashion, thus following the unitarity-preserving Page curve.

This would seem to be a kind of Cartesian duality, where you might think of these hidden islands

as where the mind is stored, apart from the body of a black hole. The same shape could be useful

when trying to understand what physics might be implicated in the ways our brain might work.

What We Know About the Brain

If we take a look at what we know empirically, we know that unlike in von Neumann

architectures with information stored solely within localized binary logic gates, information and

memory is processed non-locally and distributed across brain tissues, and that the speed of

behavior and information retrieval seems to be faster than standard electrochemical signals

across dendritic membranes permits.

We know that unlike with artificial neural networks, backpropagation in the brain, also known

as the weight transport problem or sometimes the credit assignment problem (relevant for

economic modeling), does not have any widely accepted, obviously plausible mechanism for the

bidirectional feedforward and feedback signaling that maps to biological tissues.

We know from hyperscanning studies that brain activity synchronizes across people in shared

social environments, and corresponds with shared understanding or empathy, where group

performance in teams seems to grow faster than each individual's performance. This is a feature

not found in transformer-based AI architectures. This is different than the logic by which our

economic systems function (and our AI architectures by extension) which depend on one way

flows of information in the form of transactions.

When we say that AI doesn't "feel," or that AI doesn't "love," this is roughly that from an empirical perspective. Logic flows in one direction but does not backpropagate back by the same physics both in the networks themselves but in a layer above that within groups. This is useful when you are trying to build a system you can paywall and protect with a moat, but does not scale the same way:

https://pmc.ncbi.nlm.nih.gov/articles/PMC12159917/

We know from empirical studies that human behaviors seem to indicate interference patterns in

decision-making. We know from research into psychedelics that the brain can generate

conformal fractal patterns across scales in the visual field, which would also seem to be

indicative of geometry in non-classical physics. We know there are single-celled organisms

which seem to display complex behaviors we might expect an organism to need a brain for. The

energy efficiency of the brain also seems to indicate that electrochemical signals cannot fully

account for perceptual binding or brain function, and that there must be something going on

underneath the neural network layer.

Within the cytoskeletons of cells are long cylindrical proteins called microtubules, which are

selectively blocked by anesthetics. Xenon anesthetics were tested for potency with different

xenon isotopes, and what was found was that anesthetic potency varies depending on the

isotope of xenon used, suggesting that consciousness is partly generated by non-classical means

or quantum properties like spin dynamics, consistent with what is known as the radical pair

mechanism. This would be underneath the neural network layer and at the spin state layer.

Ultraviolet superradiance has also been measured in brain tissue in some newer and

controversial studies, where it has been suggested, for example by Dr. Anirban, who I've had

many long conversations with, that microtubules act as time-crystalline optical waveguides, and

light has been found to modulate long-term potentiation and long-term depression. This could

implicate superradiant biophoton emission, which is a macroscopic quantum-like effect, as the

efficient mechanism for the adjustment of dendritic weights and therefore backpropagation.

Recent studies by Dr. Babcock and others have also shown that ultra-weak photon emission

from isolated neurons correlates with action potential firing. Some controversial and even more

fringe studies suggest superconductivity or near-superconductivity-induced effects in these

microtubules. Claims which admittedly need further experimental study.

The Model

In Penrose's original theory, information is orchestrated across brain tissues in macroscopically

evolving superpositions to a point of gravitational collapse at a critical tipping point or fixed

point, which binds it in the form of conscious experience, resolving the measurement problem,

broadcasting or distributing it across the entire tissue as a learning update that adjusts dendritic

weights through gravitational collapse or feedback (like an ourborus snake eating its own tail - or in mathematical terms - the noncommutative torus).

While the original formulations of Penrose's so-called orchestrated objective reduction model

have seemed to fail experiments, some recent work, especially in the field of quantum biology,

has suggested that these macroscopic quantum effects in the warm, wet, and noisy environment

of the brain might actually be possible through periodic driving into what are called Fröhlich

condensates, or through other mechanisms like topological protection, which is under active

investigation at the major tech companies.

Later in my career I was an employee at Microsoft, and one thing I learned is that Microsoft has

taken an interest in a niche academic field called Majorana physics to attempt building quantum

computers at macroscopic scale. Looking deeper into the academic literature on the topic, I was

introduced to the research of James Tagg and Dr. Craddock, which ties this to our model.

In our model, information stored in Majorana-like fermionic spin states hosted within

microtubules is driven, or orchestrated you might say, to saturation, and reaches a critical fixed

point or tipping point, after which information is bosonized into light-like modes which

manifest as cascades of so-called superradiant ultra-weak Majorana-like vortex biophotons,

which collapse the evolving superpositions. I know that's quite a mouthful. I will be providing

citations as well so that you can take a look at this.

The idea is that this collapse is triggered by a gravitational action, and it is at a phase transition

where information that has become saturated is discharged. Mathematically you can understand

this with Z2 orbifolds.

The Mathematics

I once discussed with Ed Witten his proposal that he believed the behavior of pure gravity in

anti-de Sitter space (which is by the way a spacetime of negative curvature) could be understood

with the monster conformal field theory, which describes the behavior of massless bosons, and

Z2 orbifolds are how you describe the transition to CFTs which describe fermionic spin systems

in de Sitter space with positive curvature, such as the so-called baby monster conformal field

theory.

I understand these concepts sound quite esoteric, but once again there will be citations as well.

This is an actual mathematical object that you can find. Z2 orbifolds, particularly in the context

of models of quantum gravity, are fundamental in constructing what are called Israel junction

conditions, which describe how you glue two spacetime geometries together or transition

between them, like gluing an anti-de Sitter spacetime, for example, to a de Sitter spacetime.

The famous Riemann zeta function or its generalizations, such as the Epstein zeta function for

high-dimensional lattices, are used to regularize divergent vacuum energy, compute loop

corrections, or define partition functions.

You can see a sort of mock demonstration of this phase transition in some experiments where it

turns out you can reproduce the zeros of the famous Riemann zeta function by periodically

driving qubits. Mathematical physicists like Dr. Tamburini, who I've had many long discussions

with, have shown that you can actually describe the behavior of particles with split properties,

like particles which are their own antiparticles (Majorana fermions), in curved spacetime

geometries with the Riemann zeta function. The critical line of the Riemann zeta function

describes the critical point of saturation in the statistics of these systems. It is also implicated in

understanding tipping points in models of macroscopic quantum-like behaviors like quantum

chaos and fluid turbulence.

This is of course directly related to what is known as the Hilbert-Pólya conjecture, which poses

the idea that the zeros of the Riemann zeta function could possibly be understood as the energy

levels of some mysterious quantum mechanical system. This would therefore be an example of

such a system.

What is interesting is that in the theory of loop quantum gravity you have what are called spin

foam networks, and you also see in theories like causal fermion systems theory similar graph

structures, which are supposed to quantize or describe spacetime. These structures are very

similar to our network of spin states in our brain neural networks. In fact, we can use these

abstractions to understand them.

Dr. Aaronson, who I've had many discussions with, once proposed that it might be possible to

leverage gravity and these spinfoam networks to perform a kind of non-computable calculation.

This would seemingly be the same as Dr. Penrose's proposal (though he denies this even though

it's directly in his publication NP Problems and Physical Reality). While spinfoams are not the

same as brain neural networks, in this context they are very strangely similar as to be almost

indistinguishable - the idea here is that the brain uses optical/spin physics where topologically

protected spin states are hosted in dendritic microtubules which saturate a critical point which

results in a superradiant cascade. This critical point is characterized by scale invariance,

meaning that the same models used to describe spinfoam networks in models of quantum

gravity could conceivably be appropriated towards understanding brain neural networks.

Dr. Aaronson expresses public skepticism towards Orch-OR theory and its variants but nonetheless muses about similar ideas in his publications.

Furthermore, Dr. Penrose has suggested that a complete theory of quantum gravity would likely

be described by the mathematics of null light geodesics, sometimes called soft hair in twistor

theory, which nicely describes the light-like modes that the information stored in our spin state

networks gets bosonized into and even ties into Einstein-Cartan theory. Corresponding to the

monster CFT is what is called the monster vertex operator algebra, which maps nicely to

twistors. These light-like modes and twistors are thus analogous to the so-called hidden islands

of entanglement entropy we discussed earlier in our approach to the black hole information

paradox, describing the mind to the neural network body that we understand by the Cartesian

mind-body problem.

But Didn't Tegmark and Others Rule Out Orch-OR?

There's interesting molecular-scale evidence that biology uses quantum effects more than the field thought 25 years ago, but no clear evidence of macroscopic quantum coherence in brain tissue at the scale Orch-OR needs. The strongest result is Li et al. (Anesthesiology 2018), where xenon isotopes with non-zero nuclear spin are measurably less potent anesthetics than spin-zero isotopes despite identical chemistry, which standard pharmacology cannot explain and which a radical-pair mechanism (Smith et al., Sci. Rep. 2021) reproduces quantitatively. Babcock et al. (J. Phys. Chem. B 2024) demonstrated UV superradiance in tryptophan networks within microtubule architectures at room temperature, though the bright states last only hundreds of femtoseconds.

Microtubule resonance work (Bandyopadhyay group) and the finding that microtubule-stabilizing drugs delay anesthesia onset point to microtubules as functionally relevant to consciousness, though not necessarily quantumly so. The Kerskens 2022 MRI study reporting consciousness-dependent multiple-quantum-coherence signals in brain water is suggestive but in a low-impact venue, lacks independent replication, and has plausible classical explanations from heartbeat-correlated motion. Matthew Fisher's Posner molecule proposal offers nuclear spin coherence times potentially measured in days but remains empirically unconfirmed.

Tegmark's original calculation about electronic coherence in microtubule conformational superpositions has been theoretically pushed by Hagan, Hameroff and Tuszyński to 10⁻⁵ to 10⁻⁴ seconds, still short of the 25 ms Orch-OR needs and still a theoretical re-estimate rather than a direct measurement. The honest summary is that spin-based proposals (xenon, radical pairs, Posner) hold up against Tegmark better than electronic-coherence-in-microtubules proposals do, because nuclear spins really are well-isolated from thermal noise, but no published study has directly measured millisecond-scale coherent superposition in brain tissue, and the gap between "biology uses quantum effects at the molecular scale" and "the brain implements quantum gravitational state reduction" has narrowed without closing.

A Falsifiable Prediction

Now that we have a basic biological, physical, and mathematical model and a theory, it should

now be possible to build testable hypotheses.

Unfortunately, as I do not have the funding for this, and it seems that the funding available is

often allocated more toward perpetuating the status quo, what I can present at least is the

results of a numerical simulation, or a prediction to inspire further investigations into this

physics, skepticism toward the claims of our popular AI models, and a direction for our

curiosity. It is possible that the solutions we are looking for don't require data centers the size of

Manhattan or projects in space, but instead, more efficiently, a deep interest in what makes us

human, what gives us consciousness, and what allows us to connect to one another.

One falsifiable prediction I can make based on numerical simulations is that, if this model is

correct, information stored in Majorana-like spin states hosted in microtubules within our

neural networks should imprint onto superradiant Majorana-like ultra-weak biophotons (or

other forms of structured light like OAM photons, Skyrmions, or Hopfions), and spectral

analysis of these signatures could provide one method of post-quantum cryptanalysis.

Specifically, the smallest eigenvalue of the Dirac-like operator spectrum over the space

corresponds to the shortest vector of the high-dimensional lattice or non-commutative torus

represented by any arbitrary neural network.

In fact, there has been some similar research on this topic to find new methods for post-

quantum cryptanalysis and approaching the shortest vector problem using what are called spin

glass and folded spectrum methods.

So you can imagine that you have a sort of neural network that represents a lattice problem, and

you can approach the shortest vector problem over that lattice by driving, or otherwise called

orchestrating, the system to a point of gravitational collapse. At that crucial phase transition,

you can measure the ultra-weak signal spectra, which should possibly imprint information about

the geometry of the space, including the shortest vector.

Ultra-weak light from cells, or biophotons, could carry quantum fingerprints if it originates from

these exotic Majorana-like states within the cell. A quantum system with a conserved parity is

linked to the polarization of the photons it emits. Through numerical simulations, what I've

done is predict three unique measurable signatures.

1. Floquet sidebands — extra spectral lines from periodic driving.

2. A magnetic field-dependent polarization bias.

3. Strong cross-correlations showing photons alternate polarization in sequence.

Detecting any of these would be strong supporting evidence that biophotons are not merely

chemical noise or metabolic byproducts, but carry quantum information from deep within the

cell that is critical toward understanding how the brain works to achieve the equivalent of

backpropagation, and ultimately what distinguishes mind from machine.

"Post-Quantum Cryptography" Is Not Secure

A presentation on lattice-based cryptography, the shortest vector problem, and its surprising

connections to consciousness, biophotons, and the black hole information paradox.

Video presentation: https://youtu.be/jSqeYz8Wh-Q

Introduction

Today I would like to present on the topic of possible vulnerabilities of post-quantum

cryptography to emerging physics beyond the standard model. This is a controversial topic I've

thought deeply about for the last 15 years, going back to when I was a student of Fields Medalist

Dr. Richard Borcherds at the University of California, Berkeley, who specializes in lattice

mathematics and string theory and is famous for solving what is called the monstrous

moonshine conjecture. It isn't clear to me why this presentation is banned from both Reddit and LessWrong, it's pretty trivial to both check citations and also show it's not AI generated (the video presentation where most of this is extracted transcript directly from this article is in my natural cadence, and I was working on these opinions even before LLMs became mainstream though admittedly sometimes I use it for formatting - maybe more evidence that academia, science, and technology have become more of an orthodoxy like a religion - like a pyramid scheme of interlocking monetary incentives that seeks to repress outside thought?).

The Baseline Assumption, and the Surprise

We start with the base assumption that in the next few years quantum computing is likely to

imperil our current cryptographic standards. This is what prompted the National Institute of

Standards and Technology in 2018, when I was visiting Boulder, Colorado, to evaluate newer

standards that are supposedly resilient to both quantum and classical attacks:

How NIST Picked the Post-Quantum Cryptography Winners — and Why It Matters | by Piyush | Medium

The resilience of post-quantum cryptography to both quantum and classical computers has

never been fully proven. In fact, one of the candidates for post-quantum cryptography, known as

SIKE, or supersingular isogeny key encapsulation, surprised the entire cryptographic

community when it was cracked within only 62 minutes on a standard Intel CPU.

Post-quantum cryptography is based largely on what are called lattice problems and the

difficulty of resolving what is called the shortest vector problem over a high-dimensional lattice,

or its close geometric equivalent, the non-commutative torus. SIKE was isogeny-based, not lattice-based. Its break (an algebraic attack using Kani's theorem on abelian surfaces) has no implication for the security of ML-KEM/Kyber or other lattice schemes, but we can think of it as a motivation to consider that reality itself may prove to be less predictable than mathematicians or cryptographers have assumed.

https://medium.com/@kootie73/the-shortest-vector-problem-svp-a-pillar-of-modern-cryptography-3769c2bf4314

My own personal view has always been that any truly unbreakable encryption is too hubristic a

request of the universe, and there is a pattern of nature surprising us and collapsing even our

strongest assumptions. So while this problem seems impossible from the perspective of classical

attacks from any classical physics, and from the perspective of quantum attacks from quantum

physics, there is one emerging area of physics, at the intersection of classical and quantum

approaches, or physics beyond the standard model, that has evaded much attention.

Three Problems, One Shape

The NP-hard shortest vector problem, where NP-hard is a classification of computational

complexity, is related to the so-called learning with errors problem. That problem is needed to

understand how the brain efficiently achieves the equivalent of backpropagation, and also what

is known as the perceptual binding problem, which is the problem of how the brain binds

sensory features into coherent experiences. Cryptography uses approximate-SVP, which isn't known to be NP-hard in the relevant approximation regimes; in fact, gap-SVP at the parameters used is in NP ∩ coNP and unlikely to be NP-hard, but lattice crypto's hardness assumption is weaker than people sometimes imply when they wave around "NP-hard." Only exact SVP and SVP with small approximation factors are NP-hard, which is a stronger problem we are going to think about where lattice problems are thought to be intractable under worst case assumptions.

The perceptual binding problem was mapped to the shortest vector problem by researchers like

Tsotsos, and brain neural networks have been mapped to high-dimensional lattices and non-

commutative tori in academic literature by groups like the Blue Brain Project. More recently

some have (unconvincingly) contested Tsotsos' work, proposing that the way in which the brain

achieves perceptual binding is by so-called "predictive coding." Strictly hierarchical or merely approximate models of perceptual binding, however, (like predictive coding) assume that the

brain successively pools local features into ever more complex conjunctions until a unified

object code emerges. Empirical evidence now shows this feed-forward scheme is insufficient:

MEG and fMRI reveal that the moment at which features are bound coincides with the onset of

late, recurrent activity that re-enters early visual areas, and when these feedback loops are

disrupted pharmacologically or with Transcranial Magnetic Stimulation (TMS), illusory

contours, perceptual switching and contextual grouping all collapse even though the putative

"higher"; hierarchical stages remain intact.

Tsotsos mapped visual attention complexity to an NP-hard problem in a computational-complexity sense which is exactly what we are referring to here.

The shortest vector problem has also been tied to the black hole information paradox in

academic literature, because on the surface, the black hole information paradox is a kind of

cryptographic question involving the flow of information one way across an event horizon, but

somehow this information must escape in some scrambled fashion from the black hole to

preserve information unitarity, which quantum theory demands.

A Computational Perspective on Visual Attention | Books Gateway | MIT Press

https://arxiv.org/pdf/2211.05491

From Black Holes Entropy to Consciousness: The Dimensions of the Brain Connectome

[hep-th/9411193] Information Loss in Black Holes and/or Conscious Beings?

So there is a kind of near equivalence between these problems. Post-quantum cryptography, the

black hole information paradox, and the way in which the brain processes sensory information

and binds it into a coherent experience. At least as a starting point, in theory it might be possible

to use cultures of biological neurons stimulated to encode a lattice problem to somehow retrieve

the shortest vector over that lattice, and understanding the physics of this may shed some light

into the black hole information paradox, at least from what we know so far.

As a brief aside, this might even shed some light on the physics of collective behaviors of people

in social networks, since we know that brain activity synchronizes across individuals in a group,

that collective intelligence seems to scale faster in groups than adding individual intelligence

together, and that more controversial theories like so-called social laser theory, which I admit

sounds somewhat strange, attempt to explain the sudden emergence of macroscopic quantum-

like collective behaviors in groups of people. In cybernetics theory, social networks of people are

much like brain neural networks, where one-way flows of information in the form of transactions between social and economic institutions often backpropagate in the form of

unpredictable behaviors.

The Proposed Experiment

So let's say we have a culture of biological neurons and we train these neurons to represent a

lattice problem, which we can theoretically do by Hamiltonian engineering. How can we retrieve

the shortest vector over that space?

There is a good amount of evidence mounting that underneath the neural network layer in these

tissues, within neuronal cytoskeletons, there are long cylindrical proteins called microtubules,

which host topologically protected fermionic spin states. The reason the brain is so efficient at

compute, operating on only about 20 watts of electricity when compared to the supercomputers

we have (the ones many tech leaders would like to power with their own dedicated nuclear

power plants) is because this physics is much more efficient than what is facilitated by

electrochemical signaling alone.

Within these microtubules are supposed to be entangled networks of these fermionic spin states

distributed across the tissue. The idea is that they are driven or orchestrated to a point of

saturation, and then at a critical phase transition, the information stored within the

entanglements of these spin states gets bosonized into light-like modes.

In experiments what this looks like are superradiant cascades of ultra-weak Majorana-like

vortex biophotons, or biophotons with a quantum property called orbital angular momentum,

and other possible forms of structured light (such as Skyrmions or Hopfions) which carries the

information that was stored in these spin states. At critical points, the superradiant cascades

broadcast error backpropagation across the brain tissue, and account for perceptual binding.

Experiments have demonstrated these superradiant cascades and even that light can modulate

long-term potentiation and long-term depression in neuron cells.

In theory, then, it should be possible to extract the shortest vector over our lattice space by

spectral analysis of this light, where the shortest vector should appear as the smallest non-zero

eigenvalue. Recent work has also shown that OAM light is capable of storing information about

the high-dimensional lattice geometries that would be needed.

https://pubs.aip.org/aip/app/article/8/5/051101/2888637/Quantum-structured-light-in-high-dimensions

What Evidence Do We Have?

So what evidence do we have of this supposedly niche, fringe theory? It turns out there has been

quite a lot of evidence accumulating over the years.

First, experiments done with xenon anesthetics blocking microtubule channels showed that the

isotope of xenon used modulated anesthetic potency, suggesting that the quantum property of

spin might be implicated in the way the brain processes information through what is called the

radical pair mechanism.

We also know that the speed and efficiency of the brain cannot fully be accounted for by

electrochemical signaling, and that information is non-locally distributed across the tissue,

which is stored and retrieved much differently than what you would see in a von Neumann

architecture.

Studies of microtubules show resonance frequency peaks across scales consistent with

conformal field theories, and even time-crystalline behaviors, which could be implicated in the

way microtubules facilitate backpropagation. The idea here is that fermionic, possibly Majorana-

like spin states might be hosted within the hydrophobic pockets of microtubules, and the

information might be bosonized into these superradiant cascades, where the microtubules act as

optical waveguides.

Microtubule theories of consciousness or brain function have been criticized because they

sometimes implicate what seem like bizarre ideas of quantum gravity or macroscopic quantum

entanglement, and don't appear to be viable based on what most of us are taught about physics.

Newer investigations and research call these assumptions into question, and there has even

been an emerging field of quantum biology, which indicates under-examined quantum effects

are required to explain phenomena like cellular signaling, photosynthesis, avian navigation,

sensory olfaction, and even human behaviors, which in studies seem to follow interference

patterns like quantum decision trees.

The Mathematical Framework

The mathematics of this can be understood with twistor theory, which describes null light

geodesics, and Einstein-Cartan theory, where these null light geodesics describe spacetime

torsion. In string theory, information carried in the form of light with orbital angular

momentum is sometimes referred to as "soft hair," and is implicated in one theoretical angle for resolving the black hole information paradox.

The transition of information stored in the form of entanglements of fermionic spin states,

sometimes called hidden islands of entanglement entropy in the literature, to these light-like

modes in superradiant cascades can be understood with Z2 orbifolds, and the behavior at the

phase transitions can be understood with the Riemann zeta function.

In fact, there have been many recent studies that were able to replicate the zeros of the Riemann

zeta function by periodically driving qubits, and models that explicitly link the Riemann zeta

function to the behavior of Majorana spin states in curved spacetimes, which can be simulated

in these environments. Both of these results demonstrate a possible resolution to the so-called

Hilbert-Pólya conjecture, which poses the possibility that the zeros of the Riemann zeta function

might eventually prove to be observed as the energy levels of some quantum physical system.

The Riemann zeta function has also been linked to macroscopic quantum-like physics such as

fluid turbulence, quantum chaos, and phase transitions in nonlinear systems theory. Similar experiments have also approached the shortest vector problem by means of spin glasses and

folded spectrum methods.

https://arxiv.org/html/2408.16062v2

https://link.aps.org/doi/10.1103/PhysRevA.103.032433

The Penrose-Hameroff Model

According to Dr. Penrose and Dr. Hameroff's model, this phase transition is facilitated by

gravity itself. At this critical point, macroscopic quantum superpositions and entanglements of

these spin states are orchestrated and saturate a complexity bound, possibly into what are called

Fröhlich condensates, after which the information content stored in these entanglement islands

or entanglement wedges is discharged in what is called an objective reduction event through

these superradiant cascades, which are a macroscopic quantum-like behavior, with gravitational

feedback that adjusts dendritic weights and facilitates learning in neural networks.

This is supposed to be the solution to the measurement problem, and one angle for explaining

why the world around us does not appear in a superposition. Whether this explanation pans out

will require further empirical study. It is conceivable that the information about a black hole

interior may escape encoded in a similar fashion and printed on light with orbital angular

momentum that might be investigated by spectral analysis.

Why This Matters

Regardless of what you think about this controversial physics, it is under active investigation by

top scientists, academics, corporations, and governments, where it has been taken very

seriously. You might think this is just a fringe theory. You can go ahead and think that, but you

might be left behind.

Whether or not these theories pan out, this is a road map for further investigation and fuel for

our skepticism of the claims made about the supposed resiliency of post-quantum cryptography,

or the viability of our artificial intelligence architectures, where it may actually prove to be more

economical to invest directly in people and communities than in AI data centers.

In conclusion, it is possible that the next breakthroughs in artificial intelligence and

cryptography might not come from scaling up data centers with their own dedicated nuclear

power plants the size of Manhattan and sending them into orbit, as many of our tech leaders

suggest (sort of a crazy idea), which strain our resources or require millikelvin temperatures

with gold-plated nanowires to manipulate qubits. They might come from a greater

understanding about what makes us human, and the physics for how our brain works and

extends to others and within our communities.