Improvement in Variational Quantum Algorithms by Measurement Simplification

TL;DR

This paper introduces a Measurement Simplification technique for Variational Quantum Algorithms that reduces calculation time and memory requirements, demonstrated on VQLS, VQE, and quantum machine learning algorithms.

Contribution

The paper proposes a novel measurement simplification method that enhances the efficiency of VQAs by simplifying measurement expressions, leading to significant computational improvements.

Findings

Significant reduction in calculation time.

Decreased memory usage.

Effective application to multiple VQA algorithms.

Abstract

Variational Quantum Algorithms (VQAs) are expected to be promising algorithms with quantum advantages that can be run at quantum computers in the close future. In this work, we review simple rules in basic quantum circuits, and propose a simplification method, Measurement Simplification, that simplifies the expression for the measurement of quantum circuit. By the Measurement Simplification, we simplified the specific result expression of VQAs and obtained large improvements in calculation time and required memory size. Here we applied Measurement Simplification to Variational Quantum Linear Solver (VQLS), Variational Quantum Eigensolver (VQE) and other Quantum Machine Learning Algorithms to show an example of speedup in the calculation time and required memory size.