I'm Partnering with Pioneer of Quantum Lidar on Project FRINGE

Letter of support from Dr. S Michael Angel

Many years ago in a dorm room in college, I used lasers to measure the width of a hair by measuring the interference pattern of light scattered against a back wall:

https://www.instructables.com/Homemade-Quantum-Laser-Micrometer-Nestors-Microm

There is a good amount of information that you can gather from the properties of light in unexpected ways. Somewhere above you, right now, a NASA satellite is measuring the speed of the wind two hundred kilometers up. It does this without a single moving part. The instrument doing the measuring is a solid brick of cemented glass, roughly the size of a shoebox, and the way it reports a wind speed is by shifting a pattern of light and dark stripes a fraction of a stripe's width across a camera chip.

That instrument belongs to a family called spatial heterodyne spectroscopy, and almost nobody outside a small circle of optical physicists has heard of it. That is a shame, because the same architecture sits underneath a set of problems that are very much in the news, from weather forecasting to methane leaks to the hardware race in quantum sensing. Here is the plain version of how it works and why it matters.

A spectrometer takes light apart. Shine sunlight through a prism and you get a rainbow, and if you look closely at that rainbow you find dark gaps in it, wavelengths missing because something in the sun or the atmosphere absorbed them. Those gaps are how we know what stars are made of, what a distant planet's air contains, and whether a plume drifting off a wellhead is methane or steam.

The trouble is that reading fine detail in the rainbow requires sending the light through a narrow slit first. Widen the slit and more light gets in, but the colors blur together and the detail disappears. Narrow it and the detail sharpens, but you have just thrown away most of your light. Every spectrometer in every laboratory makes some version of this bargain, and it is not a limitation anyone has engineered around. It is a consequence of the geometry.

That bargain is fine when the light is plentiful. It is ruinous when the light is scarce, and the light is almost always scarce in the cases people care about most. A laser pulse scattering back off thin air. A faint glow spread across a huge patch of sky. A weak signal from a substance you are trying to identify from a safe distance.

The alternative is more than a century old and it works on a different principle entirely.

Take a beam of light and split it in two. Send the halves down two slightly different paths, then bring them back together. Because light behaves as a wave, the two halves either reinforce or cancel each other depending on how far out of step they are, and what lands on the detector is a pattern of bright and dark bands. Physicists call them fringes.

Against a conventional instrument at the same level of detail, the light advantage runs to orders of magnitude.

The older versions of this idea required a mirror sliding along a track, which is acceptable in a laboratory and a liability on a spacecraft. Spatial heterodyne spectroscopy is the modern refinement that eliminates the motion. Two diffraction gratings, tilted just so, spread the barcode out across space instead of sweeping it through time. One snapshot from a camera captures everything. Nothing inside the instrument moves, which is why it can be cemented into a single monolithic block and why it holds its alignment through a rocket launch.

Then comes the refinement that makes it a speedometer. Deliberately lengthen one of the two paths by a few centimeters. The barcode now becomes extraordinarily sensitive to tiny changes in the color of the incoming light, and it registers those changes by sliding sideways.

Why that is useful comes down to the Doppler effect.

The same phenomenon that drops the pitch of a passing siren also shifts the color of light bouncing off anything in motion. The shift is minuscule, which is why measuring it is hard. In the instrument described in that solicitation, something moving at about two meters per second, an ordinary walking pace, slides the barcode by roughly one part in a thousand of a single stripe. That is a measurable quantity. Which means a static block of glass with no moving parts can tell you how fast the wind is blowing, how fast a current is running, or how fast a cloud of atoms is drifting in a laboratory.

Where quantum lidar keeps hitting the wall

Lidar is radar with light. Send out a pulse, wait for the reflection, learn something about what it bounced off. Quantum lidar is the effort to push that idea to its physical floor, using the strange statistics of individual photons and, in some designs, entangled pairs of them, to detect targets that are fainter or better hidden than conventional systems can manage.

The promise is real and the field is well funded.

What tends to get lost in the coverage is that quantum advantage lives or dies on photon efficiency. When your return signal consists of a handful of photons, every photon your receiver discards is a percentage of the advantage you spent years engineering into the transmitter. A receiver that throws away most of the light, which is what a high-detail conventional spectrometer does by design, will quietly undo the cleverness upstream.

This is where an instrument with no slit becomes interesting. It is not itself a quantum device. It is a receiver architecture that stops wasting the photons a quantum system worked so hard to prepare.

There is a second connection, and it runs the other direction. Quantum sensors, the atom interferometers and cold atom devices being built as gravimeters, gyroscopes, and clocks, work by cooling clouds of atoms to a hair above absolute zero and then watching how they move. The people building them need to know how fast those atoms are traveling, ideally without destroying the cloud to find out. That is a Doppler measurement of a faint signal, which is the exact problem this architecture was built for. The instrument becomes diagnostic equipment for the quantum industry rather than a quantum device in its own right, which is a less romantic role and a more commercially durable one.

The list of things you could point it at

Wind is the clearest near-term case. Wind measurements through the depth of the atmosphere are the single observation weather models are most starved of, a European satellite mission demonstrated that filling that gap improves forecasts, and a small static receiver is the kind of payload that makes a constellation of wind-measuring satellites financially sensible rather than merely desirable. Closer to the ground, offshore wind farms lose real revenue to turbines misaligned with a wind they cannot see, and airports lose capacity to invisible wake turbulence behind departing aircraft.

Shift the same design to different colors of light and it becomes a methane detector for pipelines and production sites, which is now a monitored and penalized activity with money attached to it.

Then there is Raman spectroscopy, which identifies unknown substances by the faint fingerprint they scatter back. Raman signals are notoriously weak, so a slit is exactly the wrong thing to put in front of one. A version of this architecture built for Raman has already been demonstrated, and it points toward handheld or vehicle-mounted identification of unknown materials at a distance, which is what hazardous materials teams, border screening, mining, and pharmaceutical manufacturing all want and mostly do not have.

Astronomy has the oldest claim of all, since the technique was invented for it. A telescope can concentrate the light of a star. It cannot concentrate a faint glow spread across the sky, which describes a great deal of what is interesting out there, including the tenuous gas between the stars, the tails of comets, the auroras of other planets, and the expanding shells of dead ones. For those targets an instrument that wastes no light is not a convenience.

Not because the science is unsettled. The foundational paper came out in 1992, the instruments have flown, and the measurements are published and checked.

The constraint is craft. Making one of these means bonding precision optical components into a single block to tolerances that survive temperature swings and launch, and the number of people on earth who have done that successfully at this scale is somewhere around a dozen. The firm that assembled the flight units is in Ottawa. The gratings came from a shop in Boulder. The design lineage traces back through a US Navy laboratory and a Minnesota university.

Letter of Support from Dr. Kiwiat

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.

Letter of interest for Project BLUEBIRD

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.

Penrose's Original Argument has been Contested. Here are My Thoughts

The Penrose-Lucas argument is a claim that human mathematical understanding cannot be reproduced by any algorithm, which is meant to show that the mind is not simply a computer running a program, which is motivation for exploring non-computable physics (in Penrose's model, ultimately implicating gravity, which is controversial and not the explicit goal of this research topic, though I could provide my own substantiated opinions on this). It builds on Gödel's first incompleteness theorem. Gödel showed that for any consistent formal system powerful enough to express basic arithmetic, there exists a true statement that the system cannot prove from within itself.

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 physics by which the brain works 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:

Penrose–Lucas argument - Wikipedia

While some have pointed out that Penrose's application of Gödel's theorem has been contested here, I used 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.

Representing the brain's perceptual network is sometimes represented as a non-commutative torus (like an ourborus) which is mathematically related to its representation as a high dimensional lattice (formalized by the Blue Brain project) and has been formalized by research groups:

Toroidal topology of population activity in grid cells | Nature

Understanding the Godelian argument in this way helps to ground discussions about models as always incomplete models of nature that require physical and empirical study (which is one reason that the 2024 Nobel Prize in physics awarded to Hinton and Hopfield has been contested within the physics community).

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.

One thing I suspect is that organoid computing is going to be the only viable affordable way to build scalable quantum computers.