Quantum computing and the workings of the brain have many aspects in common and have been attracting increasing attention in academia and industry. The computation in both is parallel and non-discrete at least in time . Though the underlying physical dynamics (e.g., equation of motion) may be deterministic, the observed or interpreted outcomes look probabilistic. Consequently, various investigations have thus been undertaken to understand and mimic the brain on the basis of quantum computing and physics . However, there have been arguments from physics and cognitive science points of view on whether the brain can and have to take advantage of quantum phenomena that need to survive in the macroscopic space-time region at room temperature. This paper presents a unique physical and microscopic computational model of the brain based on an ansatz that the brain computes in a manner similar to quantum computing, not with quantum waves but with classical waves. Log-scale encoding of information in the context of wave-based computing plays a critical role in bridging the gap between Cartesian and Tensor product states of classical and quantum waves. Our model can provide a basis for a unified computing framework of artificial intelligence and quantum computing  .
Quantum computing and the workings of the brain have many aspects in common and have been attracting increasing attention in academia and industry. The computation in both is parallel and non-discrete . Though the underlying physical dynamics (e.g., equation of motion) may be deterministic, the observed or interpreted outcomes are often probabilistic. Consequently, various investigations have been undertaken to understand and reproduce the brain on the basis of quantum physics and computing . However, there have been arguments on whether the brain can and have to take advantage of quantum phenomena that need to survive in the macroscopic space-time region at room temperature. This paper presents a unique microscopic computational model for the brain based on an ansatz that the brain computes in a manner similar to quantum computing, but with classical waves. Log-scale encoding of information in the context of computing with waves is shown to play a critical role in bridging the computing models with classical and quantum waves. Our quantum-inspired computing model opens up a possibility of unifying the computing framework of artificial intelligence and quantum computing beyond quantum machine learning approaches .
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at least in time
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look
are often
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thus
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mimic
reproduce
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computing and physics
physics and computing
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from physics and cognitive science points of view
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physical and
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of
for
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not with quantum waves
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wave-based computing plays
computing with waves is shown to play
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gap between Cartesian and Tensor product states of
computing models with
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model can provide a basis for a unified
quantum-inspired computing model opens up a possibility of unifying the
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beyond quantum machine learning approaches