Error Mitigation for Quantum Approximate Optimization

TL;DR

This paper introduces an error mitigation technique for quantum optimization algorithms using the LHZ architecture, which employs redundant encoding to reduce errors on near-term quantum devices, demonstrated specifically with QAOA.

Contribution

The paper presents a novel error mitigation method based on the LHZ architecture that modifies the objective function in QAOA to significantly reduce errors on quantum hardware.

Findings

Error mitigation via LHZ architecture improves QAOA performance.

Redundant encoding helps counteract hardware decoherence.

Modified cost functions lead to better optimization results.

Abstract

Solving optimization problems on near term quantum devices requires developing error mitigation techniques to cope with hardware decoherence and dephasing processes. We propose a mitigation technique based on the LHZ architecture. This architecture uses a redundant encoding of logical variables to solve optimization problems on fully programmable planar quantum chips. We discuss how this redundancy can be exploited to mitigate errors in quantum optimization algorithms. In the specific context of the quantum approximate optimization algorithm (QAOA), we show that errors can be significantly mitigated by appropriately modifying the objective cost function.