AI: Quantum Computing Requires Conceptual Brain Science Research

The biggest weakness of quantum computing is that it completely ignored conceptual brain science. \n \n The success of artificial intelligence from whatever it was to what it has become for productivity and social activities is [in part] because of theoretical neuroscience. Even though the architectures are mathematical, they borrowed theories from the brain, and pursued algorithms that mirrored those, to obtain the outstanding results of present-day.

\ Simply, the brain is already doing a lot of what AI wants, so even if the objective is to surpass the brain, the near goal is to reach it, and so far, so cool.

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For example, even though it is not true that the brain makes predictions, that presumption has been pivotal to achieving advances in frontier AI models. The field of artificial intelligence listened to biology, kept tabs on drawing ideas from the field, and the results have been excellent.

\n \n Quantum computing however mostly ignored springboards from biology. Although, there are pockets of efforts like quantum neuroscience, protein qubits and much else they don't bear the quality of the answers that quantum computing seeks. \n \n For example, neuroscience itself does not have a quantum problem, this means that there nothing particular that quantum solves for the unanswered questions in neuroscience for neurology, psychiatry and human intelligence. Intelligence is the new division of neuroscience with the advent of AI] \n \n Also, quantum sentience or quantum consciousness does not mean anything, other than appending quantum to something, like quantum bread or quantum toothbrush. \n \n Protein qubits is an audience chorus, but the goal is what would work for a mass market quantum computer?

\ \n \n Cryogenic Integrated Circuits \n There is a recent piece on FT, [November 19, 2025] Quantum computing needs its own industrial revolution, stating that, “getting to a general-purpose quantum computer — the kind that works like a normal computer but has the exponential processing power of quantum mechanics able to explore a vast number of possibilities at once — requires upwards of 1mn physical qubits (quantum bits). This needs a technological leap of equal magnitude.”

\ \n “The numbers bear out these concerns. Between 2019 and 2025, Google’s quantum chips went from 53 to 105 qubits, a factor-of-two increase in six years.” \n \n “We need cryogenic integrated circuits to operate at the very low temperatures required for superconducting qubits. Using this approach, we can put not hundreds but 20,000 high-fidelity qubits on a single, clean wafer, and then achieve the target of millions of qubits per system by interconnecting those wafers.” \n \n \n \n [Every] Quantum Computing Company Needs a Conceptual Brain Science Lab \n AI has domesticated neurons. What else in the brain can quantum computing hitch to hit “millions of qubits per system”. The purpose is to reach this engineering goal. So, if inspiration is drawn from the brain, one could look at what to copy, and then how to explore existing and develop new modalities to achieve that goal. For example, electrical signals or chemical signals or electrical and chemical signals, in sets?

\n \n Also, while mirroring the brain, what unsolved problem can this new solution be used for within a year?No one in the world knows what mental health is, imagine that quantum computing solves that, even if it is through some entanglement application, like to match therapy with condition.

\n \n Conceptual brain science research labs at quantum computing companies and startups would be pivotal to at least follow the AI playbook that followed the brain's excellence. For nimble Quantum startups, January 1, 2026 is not early enough to get started given the amount of work that is necessary.

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