Information Theory in Quantum Computing: Geometric Optimization Algorithms

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Quantum computing stands at a pivotal juncture, grappling with the challenges of computational complexity and achieving unprecedented accuracy. Traditional optimization techniques often struggle to effectively address the intricate nature of quantum circuits, necessitating innovative approaches. This paper introduces a novel geometric optimization algorithm based on information theory, leveraging the unique properties of quantum entanglement and superposition. The core mechanism centers around utilizing quantum feedback loops and entanglement to iteratively refine quantum circuit parameters, ultimately leading to improved efficiency and precision. The proposed algorithm demonstrates significant potential for accelerating quantum algorithm development and expanding the capabilities of quantum computation. This research addresses a critical need in the field, providing a robust and theoretically grounded framework for optimizing quantum circuits.

DOI: 10.5281/zenodo.22293780

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