LarQucut: A New Cutting and Mapping Approach for Large-sized Quantum Circuits in Distributed Quantum Computing (DQC) Environments

TL;DR

LarQucut is a novel approach for cutting and mapping large quantum circuits in distributed quantum computing, reducing overheads by minimizing cuts, reusing sub-circuit results, and adaptively mapping qubits based on interaction patterns.

Contribution

LarQucut introduces a new quantum circuit cutting and mapping method that minimizes cuts, reuses isomorphic sub-circuit results, and employs adaptive mapping for large-scale DQC circuits.

Findings

Reduces overall overheads in large quantum circuit execution.

Achieves results closer to ground truth with fewer cuts.

Effective for circuits with hundreds to thousands of qubits.

Abstract

Distributed quantum computing (DQC) is a promising way to achieve large-scale quantum computing. However, mapping large-sized quantum circuits in DQC is a challenging job; for example, it is difficult to find an ideal cutting and mapping solution when many qubits, complicated qubit operations, and diverse QPUs are involved. In this study, we propose LarQucut, a new quantum circuit cutting and mapping approach for large-sized circuits in DQC. LarQucut has several new designs. (1) LarQucut can have cutting solutions that use fewer cuts, and it does not cut a circuit into independent sub-circuits, therefore reducing the overall cutting and computing overheads. (2) LarQucut finds isomorphic sub-circuits and reuses their execution results. So, LarQucut can reduce the number of sub-circuits that need to be executed to reconstruct the large circuit's output, reducing the time spent on sampling the sub-circuits. (3) We design an adaptive quantum circuit mapping approach, which identifies qubit interaction patterns and accordingly enables the best-fit mapping policy in DQC. The experimental results show that, for large circuits with hundreds to thousands of qubits in DQC, LarQucut can provide a better cutting and mapping solution with lower overall overheads and achieves results closer to the ground truth.