Monte Carlo Graph Search for Quantum Circuit Optimization

TL;DR

This paper introduces a Monte Carlo graph search algorithm for optimizing quantum circuit design, automating the discovery of effective gate sequences for quantum algorithms, especially in quantum machine learning.

Contribution

It presents a novel quantum architecture search method combining Monte Carlo graph search with importance sampling, applicable to both discrete and continuous quantum gates.

Findings

Successfully optimized quantum gate sequences in numerical experiments.

Demonstrated applicability to quantum circuit discovery tasks.

Enhanced automation in quantum algorithm design.

Abstract

The building blocks of quantum algorithms and software are quantum gates, with the appropriate combination of quantum gates leading to a desired quantum circuit. Deep expert knowledge is necessary to discover effective combinations of quantum gates to achieve a desired quantum algorithm for solving a specific task. This is especially challenging for quantum machine learning and signal processing. For example, it is not trivial to design a quantum Fourier transform from scratch. This work proposes a quantum architecture search algorithm which is based on a Monte Carlo graph search and measures of importance sampling. It is applicable to the optimization of gate order, both for discrete gates, as well as gates containing continuous variables. Several numerical experiments demonstrate the applicability of the proposed method for the automatic discovery of quantum circuits.