Jincheng Zhang
This paper explores the application of bio-inspired algorithms for optimizing quantum addressing codes. Quantum addressing is a crucial component in quantum computing, directly impacting the fidelity and efficiency of quantum operations. Traditional quantum addressing schemes often struggle with optimization, leading to suboptimal performance. This work proposes a novel framework leveraging biological optimization algorithms – specifically, a modified Particle Swarm Optimization (PSO) – to refine quantum addressing code configurations. The core idea is to mimic the swarm intelligence found in nature, where individuals (particles) iteratively adjust their positions based on their own experience and the collective knowledge of the swarm, to find the best possible address mapping. The optimization process aims to minimize the error rate associated with quantum operations by strategically assigning qubits to addresses. We present a detailed methodology incorporating PSO, outlining the objective function, constraints, and the algorithm's parameters. Simulation results demonstrate the efficacy of this approach, showcasing a significant improvement in addressing efficiency and accuracy compared to conventional methods. The key contribution lies in the systematic integration of a biologically inspired algorithm into the quantum addressing problem, offering a potentially scalable and robust solution for enhancing quantum computing performance.
•Sep 6, 2026•0 citations