Robustness of Variational Quantum Algorithms against stochastic parameter perturbation

TL;DR

This paper analyzes how stochastic parameter perturbations affect the robustness of variational quantum algorithms, identifying noise thresholds and highlighting gate errors with minimal impact to improve quantum computation stability.

Contribution

It introduces a realistic noise model for variational quantum algorithms, performs a perturbative analysis to determine noise thresholds, and evaluates the impact of specific gate errors on algorithm robustness.

Findings

Identifies noise thresholds for stability in variational algorithms

Certain gate errors minimally affect coherence, enabling faster execution

Validates findings on problems with up to 14 qubits

Abstract

Variational quantum algorithms are tailored to perform within the constraints of current quantum devices, yet they are limited by performance-degrading errors. In this study, we consider a noise model that reflects realistic gate errors inherent to variational quantum algorithms. We investigate the decoherence of a variationally prepared quantum state due to this noise model, which causes a deviation from the energy estimation in the variational approach. By performing a perturbative analysis of optimized circuits, we determine the noise threshold at which the criteria set by the stability lemma is met. We assess our findings against the variational quantum eigensolver and quantum approximate optimization algorithm for various problems with up to 14 qubits. Moreover, we show that certain gate errors have a significantly smaller impact on the coherence of the state, allowing us to reduce the execution time without compromising performance.