DISQ: Dynamic Iteration Skipping for Variational Quantum Algorithms

TL;DR

DISQ introduces a noise drift detection and skipping method for variational quantum algorithms, significantly improving fidelity and noise robustness with reduced overhead by using reference circuits and selective circuit execution.

Contribution

The paper presents DISQ, a novel approach that detects and skips noise-affected iterations in VQAs, enhancing stability and fidelity while reducing execution overhead.

Findings

DISQ achieves 1.51-2.24x fidelity improvement over baseline.

DISQ outperforms the best alternative by 1.1-1.9x in noise mitigation.

Noise detection speed is increased by 2.07x.

Abstract

This paper proposes DISQ to craft a stable landscape for VQA training and tackle the noise drift challenge. DISQ adopts a "drift detector" with a reference circuit to identify and skip iterations that are severely affected by noise drift errors. Specifically, the circuits from the previous training iteration are re-executed as a reference circuit in the current iteration to estimate noise drift impacts. The iteration is deemed compromised by noise drift errors and thus skipped if noise drift flips the direction of the ideal optimization gradient. To enhance noise drift detection reliability, we further propose to leverage multiple reference circuits from previous iterations to provide a well founded judge of current noise drift. Nevertheless, multiple reference circuits also introduce considerable execution overhead. To mitigate extra overhead, we propose Pauli-term subsetting (prime and minor subsets) to execute only observable circuits with large coefficient magnitudes (prime subset) during drift detection. Only this minor subset is executed when the current iteration is drift-free. Evaluations across various applications and QPUs demonstrate that DISQ can mitigate a significant portion of the noise drift impact on VQAs and achieve 1.51-2.24x fidelity improvement over the traditional baseline. DISQ's benefit is 1.1-1.9x over the best alternative approach while boosting average noise detection speed by 2.07x