Automated optimization of large quantum circuits with continuous parameters

TL;DR

This paper introduces automated methods for optimizing large quantum circuits with continuous parameters, achieving significant gate count reductions efficiently to help realize quantum advantage over classical computing.

Contribution

It presents novel fast algorithms for optimizing large-scale quantum circuits with continuous parameters, improving efficiency and preserving circuit structure.

Findings

Substantial gate count reductions achieved

Optimization is faster than previous methods

Minimal structural changes to quantum algorithms

Abstract

We develop and implement automated methods for optimizing quantum circuits of the size and type expected in quantum computations that outperform classical computers. We show how to handle continuous gate parameters and report a collection of fast algorithms capable of optimizing large-scale quantum circuits. For the suite of benchmarks considered, we obtain substantial reductions in gate counts. In particular, we provide better optimization in significantly less time than previous approaches, while making minimal structural changes so as to preserve the basic layout of the underlying quantum algorithms. Our results help bridge the gap between the computations that can be run on existing hardware and those that are expected to outperform classical computers.