Information-Driven Constraint Optimization for Quantum Algorithms

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

This paper explores a novel approach to quantum algorithm design centered on information-driven constraint optimization. Classical optimization methods often struggle to effectively leverage the inherent complexity of quantum systems, leading to suboptimal solutions. We propose a "quantum information flow network" – a dynamically evolving system that iteratively refines the algorithm's state based on the information it processes, mimicking the process of quantum information flow. This method aims to shift the design paradigm from classical optimization to information flow, providing a fundamentally new pathway for algorithm development. The core mechanism involves constructing a network of nodes that represent quantum states, and the algorithm iteratively adjusts these nodes to minimize a defined objective function, guided by the network's internal state. We present a preliminary model demonstrating the potential of this approach to achieve enhanced performance in certain quantum algorithm tasks.

DOI: 10.5281/zenodo.22242549

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