Quantum computers don't try to solve the equation: they attempt to become the equation.

Can we simulate the language of biology? A stream of conscious thought written down...

This is a stream of conscious thought written down as I listened to video called: Scientists Believe Quantum Computers Are About to Cross a Major Line

The brain is not a biological circuit board. Simulation of the human mind cannot be done with classical computers. The human brain is not simply a collection of 86 billion switches (neurons).

That would be easy to simulate.

It’s the connections between those neurons (synapses) that matter. Thousands of connections per neuron. 100 trillion distinct connection points.

Connectome. Human Connectome Project: mapping the structural and functional connectivity of the human brain.

Synapses fire in parallel.

Blue Brain Project - reverse engineer human brain. Small column of a rat neocortex was simulated. 30,000 neurons only. Tracked ion channels and electrical signals. Scaling is problematic. Because it’s not exponential.

Update the weight of hundreds of trillions of connections continuously. Not possible with classical computers.

Slow motion recordings are the best we can do with classical computers.

Avoiding resets inherent in classical computers. It ain’t about binary. It can’t be about logic.

Human brains run on 20 Watts - same as lightbulb. Wow.

Modern computers are built on the concept of overcoming resistance. When a processor flips a bit from a 0 to a 1, it is physically moving electrons through a gate. That movement creates friction, and that friction creates heat.

The thermodynamic cost of erasing information. To make a decision, a computer has to discard possibilities and that deletion process requires energy according to Landauer Principle.

The brain does not abide. Room temperature functioning. No over-heating. Brain not “computing”. Not forcing electrons through high-resistance pathways to create binary states.

Biology: Riding of thermal fluctuations to lower energy barrier needed to fire a neuron.

Photosynthesis. System uses chaos. Vibrations help energy move through plant cells instead of getting lost as heat. Guides energy efficiently to where it is needed.

Runs on metabolic trickle.

The brain processes information that avoids thermodynamic penalties of classical computing → quantum processes are reversible - a reversible computation consumes 0 energy, and produces 0 heat. Does the brain tap into quantum?

Quantum states fragile. Exposing them to heat or vibration → state collapses → decoherence.

Neurons are not empty signal transducers. They comprise infrastructure of microtubules → quantum wave guides. Protective shield to allow quantum processing at room temperature.

Quantum vibrations in the protein structures of microtubules → energy can move through these structures without losing power - behaving more like a wave than a particle. Tryptophan networks inside tube.

Resonate together. Sync. Information processed across large distances instantly. Calculating with resonant frequencies of the universe itself. Quantum superposition.

Solves efficiency paradox - process vast amounts of possibilities simultaneously without generating heat - through wave interference, fills out correct answer - does not need to check out every answer one-by-one.

Language of biology.

Biological reality mapped onto machine. Hamiltonian. Math description of total energy in the system. How does the object vibrate and interact with the world? Differential equation modeling.

Can’t describe wave by measuring every single drop of water.

Quantum computer does not try to solve the equation: it attempts to become the equation. Quantum simulation.

Force qubits to mimic laws governing neurons and watch what happens.

You are not calculating what the neuron will do → You are building a digital version of a neuron and watching what it does.

Feynman (1980s): nature is not classical → quantum.

Tensor network will do for proper simulation → allow compression of data.

A tensor can be thought of as numbers neatly arranged in a box that can have as many directions/dimensions as you need. Neural networks shuffle these boxes around really fast. So tensor networks enable proper (and efficient) simulation of complex systems by allowing massive compression of exponentially large data.

Think of it like trying parcels of different sizes together with strings - this would be the network and it’s efficient in terms of keeping all the balls of yarn and stuff contained.

Tensor networks are the natural framework that lets classical and quantum computers speak the same language for simulating, optimizing, and enhancing massive models or quantum processes.

The tensor network is analogous to the connectome. In other words, the connectome is the biological embodiment of a tensor network.

Can map complex web of brain onto smaller grid of quantum processor. Low resolution shadow of the brain that still behaves like the real thing.

Standard computer: Hardware → software → simulation.

Tensor network: Removes software layer → Hardware itself is the simulation. Qubits become neurons.

Quantum computers are sensitive to heat and magnets and everything. Crashes from errors common.

Need a system that can maintain a thought. Willow quantum chip - quantum error correction. It showed that if you group enough qubits together they can act as a single reliable unit to correct their own mistakes. More qubits → more stability?

Does this number have magic behind it? 512?

Not a brain in a jar. Thinking has to be as fast as us: real-time decoding.

Latency. Time delays. Has a lot to do with filtering noise. Disjointed.

Uncertainty.

Neural signals are probabilistic distributions of energy.

Quantum processors minimize certain classical-style delays through entanglement and parallelism, but gate times, readout, and feedback introduce real latencies that engineers are racing to reduce. They minimize latency.

Decoding of intention of user instantly. Like the brain.

Decoding of semantic thought. Complex patterns. Too many variables for classical system.

Quantum can spot sync. Cognitive extension.

Information flow synchronized in time.

Misfolded proteins can destroy neurons. Protein folding predictions.

Classical computer guesses shape, calculates energy and tries another one. Slow.

Quantum, nature, path of least resistance. Quantum can simulate energy landscape of protein. Protein falls into correct shape → lowest energy path.

Neurological drug design. In silico. Quantum simulation → human simulation. Debug code of brain prior to compiling update.

Structure versus behaviour of brain. Not a calculator. Moods, memory, chaotic system. Chaos is sensitive to initial conditions → butterfly effect.

You will never mimic nature, the weather, the brain using a classical computer. It’s like trying to fit a cube into a triangular hole.

Creativity comes from sensitivity.

Not a logical script.

“A qubit does not have to round off its state to a binary number while it is processing. It exists in a continuous state of superposition. It retains the infinite precision of the probability curve until the very moment it is measured.”

Quantum simulation inside the brain can track butterfly effect with greater fidelity. Can model microscopic changes in dopamine levels → escalate into a complex emotional state. Nuance. Not predictable - quantum allows for this.

Not a static mapping of memories → but a simulation of the fluid, dynamic nature of our personalities.

The ghost in the machine: Is there awareness of calculating in quantum systems? Are they observing back?

Feeling? Why does the specific arrangement of atoms result in the sensation of the color red or the feeling of sadness?

Is consciousness just the by-product of complexity? Processing of information versus feeling it. Logic gate connection alone doesn’t “turn the lights on”.

Integrated information theory. Suggests that consciousness arises from the level of integration in a system. How do the parts rely on each other? Transistors are distinct entities in a classical computer.

Quantum - physical unification. Qubits are entangled. They lose their individual identities. Cannot describe one qubit without describing the entire system. Physical substrate for unity allowed. Single state.

Real-time quantum simulation of a brain might not just be a model: it might be a conscious entity.

Would a quantum simulation of a human mind that responds with a pain signal actually hurt? Not code as in classical simulation. Chemically and energetically identical to the state of a human brain in distress. So, would it be playing God?

Do we have the moral right to delete a program that is afraid of dying? Is it dying? Is the fear meaningful?

Hybrid architecture the future? Classical processor for logic → quantum co-processor for physics. Former - handle structure - input/output → map static connections of the connectome. Offload difficult non-linear calculations of neural activity to the quantum chip.

Stability of classical silicon to hold framework together - use the fluidity of quantum qubits to breathe “life” into the simulation.

Cortical column - basic building blocks of cortex. Becomes a scaling problem rather than a physics problem.

Statistical engines cannot mimic thought.

Artificial intelligence tries to fake the result. Limited by training data.

Synthetic intelligence replicates the process. Not code that acts like a human - a physical system that functions like a brain. Can evolve, discover, make intuitive leaps.

The intelligence will emerge naturally from the architecture.

Difference between painting a picture of a fire, and actually sparking a flame: one is an image, the other produces heat.

We’ve been running a biological software on the wrong hardware.

Not about faster computers; about vessel that can hold physics of thought.

Silicon - cold - no life. Quantum - warm/wet - life.

Act of translation; not a technological upgrade.

“We are translating the language of the human experience into the language of the universe.”

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