Distributed Scheduling of Quantum Circuits with Noise and Time Optimization

TL;DR

This paper presents a novel scheduling approach for quantum circuits that optimizes fidelity and execution time by partitioning circuits and using ILP and graph-theoretic methods, improving performance on noisy quantum hardware.

Contribution

It introduces an ILP-based scheduler and a polynomial-time graph-theoretic method for quantum circuit scheduling that enhances fidelity and reduces execution time under hardware constraints.

Findings

Achieves ~12.3% fidelity improvement for 10-qubit circuits.

Achieves ~21% fidelity improvement with measurement error mitigation.

Provides a polynomial-time scheduling method matching ILP results under certain conditions.

Abstract

Quantum computers are currently noisy, particularly without error correction and fault tolerance. Methods like error suppression and mitigation are widely used to improve performance. Circuit cutting, which partitions a circuit into smaller subcircuits, can also reduce noise. In this paper, we propose an Integer Linear Program (ILP) based scheduler for optimizing subcircuit schedules on available hardware. The goal is to maximize overall fidelity and ensure each hardware does not exceed its predefined execution time. For 10-qubit circuits, our method achieves an average fidelity improvement of ~12.3% and ~21% with and without measurement error mitigation, respectively, even with minimal execution time. Additionally, we introduce a polynomial-time graph-theoretic scheduling method that matches the ILP scheduler's results when the number of subcircuits does not exceed the number of hardware units, each with minimal execution time. This noise and time-optimized scheduler represents a crucial step towards optimal quantum computing performance, especially with limited hardware access.