Quantum Probability Computation: An Adaptive Optimization Framework

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

Quantum probability computation (QPC) holds immense promise for advanced scientific discovery and technological innovation. However, the inherent complexity of QPC algorithms necessitates sophisticated optimization techniques to achieve efficient computation. This paper introduces an adaptive optimization framework for QPC, designed to automatically adjust parameters and minimize computational cost. The core mechanism involves dynamically modifying key parameters based on the problem's characteristics, leading to enhanced efficiency. We present a comprehensive overview of the framework's design, detailing the optimization strategy and its potential impact on QPC performance. This work addresses a critical limitation of traditional QPC approaches, offering a novel solution for improved scalability and throughput.

DOI: 10.5281/zenodo.22243941

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