A Stochastic Quantum Neural Network Model for Ai

TL;DR

This paper proposes a stochastic quantum neural network model inspired by biological neurons, aiming to better capture brain complexity and address limitations of classical AI models using quantum principles.

Contribution

It introduces a formal mathematical framework for stochastic quantum neural networks incorporating biological neuronal fluctuations.

Findings

Formalization of stochastic quantum neural network equations

Discussion of decoherence and qubit stability challenges

Potential implications for AI and neuroscience

Abstract

Artificial intelligence (AI) has drawn significant inspiration from neuroscience to develop artificial neural network (ANN) models. However, these models remain constrained by the Von Neumann architecture and struggle to capture the complexity of the biological brain. Quantum computing, with its foundational principles of superposition, entanglement, and unitary evolution, offers a promising alternative approach to modeling neural dynamics. This paper explores the possibility of a neuro-quantum model of the brain by introducing a stochastic quantum approach that incorporates random fluctuations of neuronal processing within a quantum framework. We propose a mathematical formalization of stochastic quantum neural networks (QNNS), where qubits evolve according to stochastic differential equations inspired by biological neuronal processes. We also discuss challenges related to decoherence, qubit stability, and implications for AI and computational neuroscience.