A Variation-Aware Quantum Circuit Mapping Approach Based on Multi-agent Cooperation

TL;DR

This paper introduces a multi-agent cooperation-based quantum circuit mapping method that accounts for device variability, significantly enhancing circuit fidelity on noisy intermediate-scale quantum devices.

Contribution

It proposes a novel multi-agent approach for quantum circuit mapping that considers spatio-temporal variations in device quality, improving fidelity over existing methods.

Findings

Achieves up to 95.42% success rate improvement

Improves average success rate by 25.86%

Demonstrates effectiveness on large benchmark circuits

Abstract

The quantum circuit mapping approach is an indispensable part of the software stack for the noisy intermediatescale quantum (NISQ) device. It has a significant impact on the reliability of computational tasks on NISQ devices. To improve the overall fidelity of physical circuits, we propose a quantum circuit mapping method based on multi-agent cooperation. This approach considers the Spatio-temporal variation of quantum operation quality on the NISQ device when inserting ancillary operation. It consists of two core components: the qubit placement algorithm and the qubit routing method. The qubit placement algorithm exploits the iterated local search framework to find a desirable initial mapping for the reduced symmetric form of the original circuit. The qubit routing method generates the physical circuit through multi-agent communication and collaboration. Each agent inserts the ancillary gates independently according to its environment state. The quality of the physical circuit evolves according to an information-exchanging mechanism between agents, which combines the local search and global search. To experiment on the benchmark circuits (with hundreds of quantum gates) beyond the capacity of current NISQ devices, we build a noisy simulator with gate error 10x lower than that of the latest NISQ device of IBM. The experimental results confirm the performance of our approach in improving circuit fidelity. Compared with the stateof-the-art method, our method can improve the success rate by 25.86% on average and 95.42% at maximum.