Title: Algorithmic Quantum Complexity Theory with Adaptive Parameterization

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper presents a novel algorithmic approach to quantum complexity theory, focusing on automated parameterization and iterative refinement to achieve a target complexity. The core mechanism centers around leveraging quantum annealing to efficiently explore a vast parameter space, allowing the algorithm to pinpoint solutions with optimal complexity. We propose a framework where the initial parameters are coupled with a quantum annealing process, dynamically adjusting the parameters based on the calculated complexity. This fundamentally shifts the focus from approximation to precise quantification, offering a potentially transformative tool for tackling complex problems within the context of quantum computation. The paper details the algorithm's design, its theoretical justification, and preliminary experimental results demonstrating its efficacy in a simplified, yet representative, example. We aim to establish a new paradigm in complexity theory, moving beyond traditional approximations and embracing a direct, quantum-driven approach to complexity assessment.

DOI: 10.5281/zenodo.22302817

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