Will Conceptual Brain Science Advance Quantum Computing?

By David Stephen 

There is a new [December 2, 2025] paper in Nature, Artificial intelligence for quantum computing, stating that, “Quantum computing (QC) has the potential to impact every domain of science and industry, but it has become increasingly clear that delivering on this promise rests on tightly integrating fault-tolerant quantum hardware with accelerated supercomputers to build accelerated quantum supercomputers.”

Will Conceptual Brain Science Advance Quantum Computing?

“However, transitioning hardware from noisy intermediate-scale quantum (NISQ) devices to fault-tolerant quantum computing (FTQC) faces a number of challenges. Though recent quantum error correction (QEC) demonstrations have been performed, all popular qubit modalities suffer from hardware noise, preventing the below-threshold operation needed to perform fault-tolerant computations.”

“Though high-performance computing (HPC), and in particular, accelerated GPU computing, already drives QC research through circuit and hardware simulations, the rise of generative artificial intelligence (AI) paradigms has only just begun.”

“Despite the considerable promise of AI, it is critical to recognize its limitations when applied to QC. AI, as a fundamentally classical paradigm, cannot efficiently simulate quantum systems in the general case due to exponential scaling constraints imposed by the laws of quantum mechanics. Classical simulation of quantum circuits suffers from exponential growth in computational cost and memory consumption.”

“In the broadest of strokes, we can categorize deep neural network (DNN) applications as discriminative and generative. The former seeks to learn the conditional probability distribution P(y?x) of value vector y given feature vector x, whereas the latter seeks the joint probability distribution P(x, y).”

“Critical for training all of these deep learning methods is high-quality data. In the case of QC, this data must often be obtained via simulation with supercomputers due to noise and scale limitations of quantum computers, as well as the cost (time and economic) of obtaining quantum data.”

“AI for quantum computer development and design. Device design. Learning models of quantum systems. AI for preprocessing. Quantum circuit compilation. Unitary synthesis. AI for circuit optimization. AI models to generate compact circuits. AI for device control and optimization. Designing optimal dynamics. Remove unwanted dynamics. AI for quantum error correction. AI for post-processing. Efficient observable estimation and tomography. Error mitigation techniques. Accelerated quantum supercomputing systems. Simulating high quality data sets.”

“Most importantly, each aspect of QC needs to scale, and AI might be the only tool with the ability to both solve these problems effectively and do so efficiently at scale. AI has only begun to benefit QC, and it is likely that AI will play an increasingly critical role into the realization of useful QC applications and FTQC.”

AI

A simple way to describe AI is a technology that copied what works: the brain. Or, simply, AI is a technology that looked at the best case of intelligence in nature, the human brain, and imitated it, in the ways that is mathematically possible.

Also, large language models [LLMs] copied a major basis of intelligence, language. While it is possible to operate intelligence in other ways, language is central — to human intelligence — for thinking, listening, writing, reading, singing, signing, speaking and so on.

So, AI is as good as it is, following the lead of the brain, directly.

Now, if this made AI relevant more than any technology that has ever existed, what should any other aspirational technology do? Copy the imitation, AI, or copy the source, the human brain?

Quantum Computing

There are several engineering gaps in quantum computing where fundamental answers should be sought in the brain.

While AI can be currently useful for several improvement cases, the brain should be aggressively explored [theoretically] for areas that can shape how to approach development of new modalities for quantum computing as well as several other need areas, like stability, error correction, compactness and so forth.

AI took cues from neurons. Quantum computing can look at electrical and chemical signals. Such that, since the objective is to improve quantum computing, it is possible to develop varied postulations about electrical and chemical signals, in ways that would stymie challenges in quantum computing.

For example, how does the brain correct what is called prediction error. It is often said that in predictive coding — sometimes linked to predictive process —that the brain makes a prediction but that when it does not match, there is a correction, then update.

It is possible to postulate that what takes the action that appears like prediction in the brain are electrical signals, in sets, in clusters of neurons.

And how they do so is that in a set, some split from others, going ahead, before others follow.

Now, the early-split goes for interaction [at a junction of a set of chemical signals], to quickly allow for interpretation, so that processes can proceed.

However, for the incoming signal or the second part of the split, if the input matches, it goes in the same direction, if not, it goes in another direction which is correcting the prediction error.

For example, the initial parts of a sound can be heard and then there is a split of electrical signals, with the initial one, going for interpretation to determine or show that it is a specific sound.

However, as the rest of the sound is heard, the incoming electrical signal goes in the same direction if it matches, or elsewhere if it does not, correcting the error.

This postulate can be used to remotely explore paths towards error correction in quantum computing.

There is also possible to define memory in the brain by thick and thin sets, such that thin sets contain what is unique about anything and thick sets for whatever is common between two or more thin sets.

For quantum computing, entanglement and superposition can be explored with both thick sets and thin sets, to also draw notes for quantum storage as well as against decoherence and other weaknesses.

Conceptual brain science has a lot to offer quantum computing, at a fundamental level, to benefit engineering for important use cases before the decade is out. The opportunity is to setup labs for conceptual brain science research at quantum computing companies, to work on this, especially as early as January 1, 2026.

David Stephen currently does research in conceptual brain science with focus on the electrical and chemical configurators for how they mechanize the human mind with implications for mental health, disorders, neurotechnology, consciousness, learning, artificial intelligence and nurture. He was a visiting scholar in medical entomology at the University of Illinois at Urbana Champaign, IL. He did computer vision research at Rovira i Virgili University, Tarragona.

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