Characterizing Error Mitigation by Symmetry Verification in QAOA

Abstract

Hardware errors are a major obstacle to demonstrating quantum advantage with the quantum approximate optimization algorithm (QAOA). Recently, symmetry verification has been proposed and empirically demonstrated to boost the quantum state fidelity, the expected solution quality, and the success probability of QAOA on a superconducting quantum processor. Symmetry verification uses parity checks that leverage the symmetries of the objective function to be optimized. We develop a theoretical framework for analyzing this approach under local noise and derive explicit formulas for fidelity improvements on problems with global ${\mathbb{Z}}_2$ symmetry. We numerically investigate the symmetry verification on the MaxCut problem and identify the error regimes in which this approach improves the QAOA objective. We observe that these regimes correspond to the error rates present in near-term hardware. We further demonstrate the efficacy of symmetry verification on an IonQ trapped ion quantum processor where an improvement in the QAOA objective of up to 19.2\% is observed.

11 pages

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• Constrained Optimization via Quantum Zeno Dynamics
• Multi-Angle QAOA Does Not Always Need All Its Angles
• Enabling High Performance Debugging for Variational Quantum Algorithms using Compressed Sensing
• Performance of Quantum Approximate Optimization with Quantum Error Detection
• Symmetry-based quantum algorithms for open-shop scheduling with hard constraints