Probability Geometric Optimization for Quantum Computing: A Novel Approach

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Quantum computing holds immense promise for revolutionizing various fields, but its practical realization faces significant challenges related to algorithm development and hardware optimization. Traditional optimization methods often struggle to effectively address the complex, multi-dimensional nature of quantum systems. This paper introduces a novel probabilistic geometric optimization framework for quantum computing, leveraging the principles of probability geometry to model quantum potential functions and guide the optimization of quantum circuits. We propose a method that dynamically adjusts the quantum circuit's parameters based on the probability distribution of the potential energy landscape, resulting in more efficient and robust quantum algorithms. The core mechanism centers around utilizing the inherent probabilistic nature of quantum mechanics to create a system that naturally evolves towards optimal solutions. This approach offers a potential pathway to significantly enhance the performance of quantum computations, moving beyond the limitations of conventional optimization techniques. This work presents a new theoretical framework and preliminary implementation demonstrating the efficacy of the proposed method in a simplified quantum circuit design.

DOI: 10.5281/zenodo.22291359

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