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Trapped Ion Quantum Computing Quantum Machine Learning Quantum Chemistry

Improvement in Variational Quantum Algorithms by Measurement Simplification

arXiv
Authors: Jaehoon Hahm, Hayeon Kim, Young June Park

Year

2023

Paper ID

52629

Status

Preprint

Abstract Read

~2 min

Abstract Words

110

Citations

N/A

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.

Why This Paper Matters

  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2023 reference point for readers tracking recent quantum research.
  • Variational Quantum Algorithms (VQAs) are expected to be promising algorithms with quantum advantages that can be run at quantum computers in the close future.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2312.06176 [2] arXiv https://arxiv.org/abs/2312.06176 [3] Publisher https://arxiv.org/abs/2312.06176

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