# Quantum-Inspired Computing: Can it be a Microscopic Computing Model of the Brain?

## TL;DR

This paper proposes a novel microscopic brain model inspired by quantum computing, utilizing classical waves and log-scale encoding to unify AI and quantum computing frameworks beyond existing quantum machine learning methods.

## Contribution

It introduces a unique classical wave-based brain model inspired by quantum principles, bridging classical and quantum computing paradigms.

## Key findings

- Log-scale encoding is crucial for wave-based computation.
- The model suggests a unified framework for AI and quantum computing.
- Potential to extend beyond current quantum machine learning approaches.

## Abstract

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.