Quantum-Inspired Constraint Satisfaction via Superconducting Qubits

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper explores a novel approach to solving constraint satisfaction problems (CSPs) by leveraging principles inspired by quantum computing, specifically utilizing superconducting qubits. The core idea is to map the CSP's variables and constraints onto the state space of these qubits, then simulate a quantum annealing process through controlled decoherence and interference. Unlike traditional CSP solvers, this method aims to exploit the potential for rapid convergence by mimicking the superposition and entanglement characteristics inherent in quantum systems. The implementation described here is entirely classical, avoiding the need for specialized quantum hardware. We demonstrate a framework for translating CSP problems into a qubit-based representation and outlining the algorithmic steps for a 'quantum-inspired' annealing process. This approach offers a potentially scalable solution for complex CSPs, bridging the gap between classical computation and the theoretical advantages of quantum computation. The presented method focuses on achieving efficient exploration of the solution space through carefully designed control of qubit decoherence and interference, ultimately leading to near-optimal solutions. The key contribution lies in the practical realization of quantum-inspired algorithms using accessible, controllable superconducting qubits.

DOI: 10.5281/zenodo.22201600

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