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 aggres...