Automated optimization of large quantum circuits with continuous parameters

• Published in: npj quantum information 2018-05, Vol.4 (1), p.1-12, Article 23
• Main Authors: Nam, Yunseong, Ross, Neil J., Su, Yuan, Childs, Andrew M., Maslov, Dmitri
• Format: Article
• Language: English
• Subjects: 639/705/117; 639/766/483/481; Algorithms; Automation; Circuits; Classical and Quantum Gravitation; Computers; Physics; Physics and Astronomy; Quantum Computing; Quantum Field Theories; Quantum Information Technology; Quantum Physics; Relativity Theory; Spintronics; String Theory
• ISSN: 2056-6387; 2056-6387
• DOI: 10.1038/s41534-018-0072-4

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. Quantum computation: optimizing quantum circuits A new software tool significantly reduces the size of arbitrary quantum circuits, automatically optimizing the number of gates required for running algorithms. Yunseong Nam and colleagues from the University of Maryland developed a set of subroutines which, given a certain quantum circuit, would remove redundant gates by changing the order of individual or multiple operations and combining them. After a pre-processing phase, the execution of these routines in careful order constitutes a powerful automatized approach for reducing the resources required to implement a given algorithm. The heuristic nature of this optimization makes its computational cost scale well with the size of the circuit, as shown by comparisons for the computation of discrete logarithms and Hamiltonian simulations. This makes it applicable to computations that can be run on existing hardware and might outperform classical computers.