Improvement in Variational Quantum Algorithms by Measurement Simplification
This work addresses computational bottlenecks for researchers and practitioners using VQAs, though it appears incremental as it builds on existing quantum circuit rules.
The authors tackled the computational inefficiency of Variational Quantum Algorithms (VQAs) by proposing Measurement Simplification, a method that simplifies quantum circuit measurement expressions, resulting in large improvements in calculation time and required memory size for algorithms like VQLS and VQE.
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.