Bias Analysis and Regularization of Sequential Minimal Optimization in Variational Quantum Eigensolvers

TL;DR

This paper analyzes bias in SMO-VQE algorithms, showing how bias accumulates and proposing a regularization method that improves optimization performance without additional measurements.

Contribution

It provides a detailed bias analysis in SMO-VQE, introduces a bias estimation technique, and proposes a regularization method that enhances optimization stability and accuracy.

Findings

Bias can be accurately estimated without extra measurements.

Bias correction can destabilize optimization in low-curvature directions.

The proposed regularization method improves performance across various settings.

Abstract

The Nakanishi Fujii Todo (NFT) algorithm, also known as Rotosolve, implements Sequential Minimal Optimization for Variational Quantum Eigensolvers (SMO-VQE) by exploiting the trigonometric dependence of the energy on individual circuit parameters. This enables analytical one-dimensional minimization using only a few , typically two, energy evaluations, but introduces bias in the estimated energy. Although performing additional measurements every few tens of iterations can mitigate bias accumulation, we find that such corrections often degrade optimization performance. In this paper, we analyze the origin and accumulation of bias during the SMO-VQE process. Specifically, we show that the bias can be accurately estimated without additional measurements. Furthermore, we find that bias correction destabilizes optimization along directions with small curvature, whereas the original biased estimator implicitly acts as a regularizer. Based on these insights, we propose a simple regularization method that implements error accumulation while maintaining unbiased energy estimation. The resulting algorithm consistently improves performance across different system sizes, circuit depths, target Hamiltonians, and measurement shots, with minimal hyperparameter tuning.