Quantum-Inspired Computing: Can it be a Microscopic Computing Model of the Brain?
This work addresses the challenge of unifying AI and quantum computing models for researchers in computational neuroscience and quantum-inspired computing, though it appears incremental as it builds on existing quantum-inspired approaches.
The paper tackles the problem of modeling brain computation by proposing a microscopic computational model based on quantum-inspired classical waves, using log-scale encoding to bridge classical and quantum computing frameworks.
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.