Investigating the Performance of an Adiabatic Quantum Optimization Processor

TL;DR

This study evaluates the median adiabatic times for quantum optimization of large Ising spin glass problems, demonstrating potential quantum advantage over classical solvers for problem sizes up to 128 qubits.

Contribution

It provides the first detailed analysis of adiabatic quantum optimization times on realistic hardware parameters for large-scale NP-hard problems.

Findings

Adiabatic times are 4 to 6 orders of magnitude shorter than classical solvers.

Quantum advantage appears for problem sizes up to 128 qubits.

Realistic system constraints significantly impact performance.

Abstract

Adiabatic quantum optimization offers a new method for solving hard optimization problems. In this paper we calculate median adiabatic times (in seconds) determined by the minimum gap during the adiabatic quantum optimization for an NP-hard Ising spin glass instance class with up to 128 binary variables. Using parameters obtained from a realistic superconducting adiabatic quantum processor, we extract the minimum gap and matrix elements using high performance Quantum Monte Carlo simulations on a large-scale Internet-based computing platform. We compare the median adiabatic times with the median running times of two classical solvers and find that, for the considered problem sizes, the adiabatic times for the simulated processor architecture are about 4 and 6 orders of magnitude shorter than the two classical solvers' times. This shows that if the adiabatic time scale were to determine the computation time, adiabatic quantum optimization would be significantly superior to those classical solvers for median spin glass problems of at least up to 128 qubits. We also discuss important additional constraints that affect the performance of a realistic system.