AI: Would Conceptual Brain Science Advance Quantum Computing? - CDOTrends

A.I. Machine Learning AI: Would Conceptual Brain Science Advance Quantum Computing?

By David Stephen December 14, 2025 There is a new [December, 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.”“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.” Related Brain Science: AI Companies Don't Have Human Intelligence Research Labs Understanding the Impact of AI on Human Workforce Security “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…”“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.”The link between AI and our brainA 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?How AI benefits quantum computingThere 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 set up labs for conceptual brain science research at quantum computing companies, to work on this, especially as early as January 1, 2026.The views and opinions expressed in this article are those of the author and do not necessarily reflect those of CDOTrends.