Application-Oriented Performance Benchmarks for Quantum Computing

Abstract

In this work we introduce an open source suite of quantum application-oriented performance benchmarks that is designed to measure the effectiveness of quantum computing hardware at executing quantum applications. These benchmarks probe a quantum computer's performance on various algorithms and small applications as the problem size is varied, by mapping out the fidelity of the results as a function of circuit width and depth using the framework of volumetric benchmarking. In addition to estimating the fidelity of results generated by quantum execution, the suite is designed to benchmark certain aspects of the execution pipeline in order to provide end-users with a practical measure of both the quality of and the time to solution. Our methodology is constructed to anticipate advances in quantum computing hardware that are likely to emerge in the next five years. This benchmarking suite is designed to be readily accessible to a broad audience of users and provides benchmarks that correspond to many well-known quantum computing algorithms.CONTENTS * This work was sponsored by the Quantum Economic Development Consortium (QED-C) and was performed under the auspices of the QED-C Technical Advisory Committee on Standards and Performance Benchmarks. The authors acknowledge many committee members for their input to and feedback on the project and this manuscript.

Better-than-classical Grover search via quantum error detection and suppression

“…As circuit depth increases with the number of queries and the problem size, there is a trade-off between the added decoherence and the increase in the success probability. Most experimental implementations of Grover's algorithm have focused on a single query [5][6][7][8] , but this strategy does not scale well, as both p C s ð1; NÞ and p Q s ð1; NÞ decrease exponentially with n. We adopt an empirical approach to identify the optimal number of queries such that p s is maximized. We set q = 2 for all problem sizes other than n = 2 where q opt = 1.…”

“…As one of the first algorithms with a provable quantum speedup, Grover search is often used as a subroutine for other quantum algorithms 3,4 . Over the last two decades, Grover search has been implemented on various quantum computing platforms [5][6][7][8][9][10] , albeit for relatively small N.…”

“…The list can be queried classically or using quantum queries; in both cases, one finds the marked element with some probability, which we refer to as the classical or quantum success probability. The largest implementation of Grover's algorithm to date is for n = 8 qubits, but without demonstrating a better-than-classical quantum success probability 5 . Such better-than-classical performance has been achieved for n = 3 6,7 and n = 4 8 qubits.…”

Benchmarking Simulated and Physical Quantum Processing Units Using Quantum and Hybrid Algorithms

“…While ref. [42] utilizes volumetric benchmarks as a backdrop to measure a suite of quantum algorithms, such as the quantum Fourier transform and Grover's search. These algorithms are tested many times at different depths and widths.…”

Qubit Condensation for Assessing Efficacy of Molecular Simulation on Quantum Computers

“…Currently, either component-level qubit and gate errors − or system-level metrics such as quantum volume (QV) , are seen as the conventional means of comparing various quantum devices against each other. While component-level metrics are useful while building quantum systems, they often fail to capture the behavior and errors of large quantum circuits on a given device. − Thus, a system-level measure such as our metric or the QV is desirable. Unlike quantum volume, however, our qubit condensation metric is a specific measure of how well a quantum device can prepare a highly correlated state, making it a better predictive tool for comparing quantum devices for molecular simulations.…”