Robust Qubit Mapping Algorithm via Double-Source Optimal Routing on Large Quantum Circuits

TL;DR

This paper presents Duostra, a novel qubit mapping algorithm designed for large quantum circuits, which improves mapping efficiency and cost reduction on real hardware with limited connectivity, especially for circuits beyond the NISQ era.

Contribution

Introduces Duostra, an innovative qubit mapping algorithm with heuristic scheduling, optimized for large-scale quantum circuits on real devices, outperforming existing methods in cost reduction.

Findings

Reduces mapping cost by 21.75% on large circuits with over 50 qubits.

Improves mapping costs on SABRE-large benchmark by up to 25.7%.

Demonstrates effectiveness on circuits beyond the NISQ era.

Abstract

Qubit Mapping is a critical aspect of implementing quantum circuits on real hardware devices. Currently, the existing algorithms for qubit mapping encounter difficulties when dealing with larger circuit sizes involving hundreds of qubits. In this paper, we introduce an innovative qubit mapping algorithm, Duostra, tailored to address the challenge of implementing large-scale quantum circuits on real hardware devices with limited connectivity. Duostra operates by efficiently determining optimal paths for double-qubit gates and inserting SWAP gates accordingly to implement the double-qubit operations on real devices. Together with two heuristic scheduling algorithms, the Limitedly-Exhausitive (LE) Search and the Shortest-Path (SP) Estimation, it yields results of good quality within a reasonable runtime, thereby striving toward achieving quantum advantage. Experimental results showcase our algorithm's superiority, especially for large circuits beyond the NISQ era. For example, on large circuits with more than 50 qubits, we can reduce the mapping cost on an average 21.75% over the virtual best results among QMAP, t|ket>, Qiskit and SABRE. Besides, for mid-size circuits such as the SABRE-large benchmark, we improve the mapping costs by 4.5%, 5.2%, 16.3%, 20.7%, and 25.7%, when compared to QMAP, TOQM, t|ket>, Qiskit, and SABRE, respectively.