A novel framework for Shot number minimization in Quantum Variational Algorithms

TL;DR

This paper introduces a generalized framework to minimize the number of measurement shots in Variational Quantum Algorithms, combining estimators and optimizers to improve efficiency and reduce resource consumption.

Contribution

It proposes a novel, flexible framework that integrates different estimators and optimizers to effectively reduce shot evaluations in VQAs.

Findings

Significant reduction in measurement shots compared to traditional methods.

Enhanced performance demonstrated with sample mean and recursive estimators.

Effective combination of estimators and optimizers for resource-efficient quantum algorithms.

Abstract

Variational Quantum Algorithms (VQAs) have gained significant attention as a potential solution for various quantum computing applications in the near term. However, implementing these algorithms on quantum devices often necessitates a substantial number of measurements, resulting in time-consuming and resource-intensive processes. This paper presents a generalized framework for optimization algorithms aiming to reduce the number of shot evaluations in VQAs. The proposed framework combines an estimator and an optimizer. We investigate two specific case studies within this framework. In the first case, we pair a sample mean estimator with a simulated annealing optimizer, while in the second case, we combine a recursive estimator with a gradient descent optimizer. In both instances, we demonstrate that our proposed approach yields notable performance enhancements compared to conventional methods.