Quantum-Inspired Probabilistic Circuit Synthesis

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper explores a novel approach to neural circuit synthesis, drawing inspiration from the principles of quantum mechanics. Traditional neural network optimization often relies on deterministic algorithms, which can be susceptible to local optima and limited exploration of the solution space. This work proposes a methodology termed "Quantum-Inspired Probabilistic Circuit Synthesis" that leverages concepts like superposition and interference to introduce probabilistic pathways within a neural network architecture. The core idea is to generate a network where connections between nodes are not fixed but governed by probabilities, allowing for a more diverse and robust exploration of the circuit's behavior. We formulate a synthesis process based on probabilistic gate selection and connection weighting, mimicking quantum interference to constructively reinforce promising pathways and destructively cancel unfavorable ones. This approach aims to significantly improve the efficiency and robustness of neural circuit design, offering a potentially superior alternative to conventional deterministic methods. The theoretical framework is presented, along with a description of the core algorithmic components, and a discussion of the potential benefits and challenges associated with this new paradigm.

DOI: 10.5281/zenodo.22273683

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