Determination and correction of persistent biases in quantum annealers

Alejandro Perdomo-Ortiz,Bryan O’Gorman,Vadim N Smelyanskiy,Rupak Biswas,Joseph Fluegemann

doi:10.1038/srep18628

Abstract

Calibration of quantum computers is essential to the effective utilisation of their quantum resources. Specifically, the performance of quantum annealers is likely to be significantly impaired by noise in their programmable parameters, effectively misspecification of the computational problem to be solved, often resulting in spurious suboptimal solutions. We developed a strategy to determine and correct persistent, systematic biases between the actual values of the programmable parameters and their user-specified values. We applied the recalibration strategy to two D-Wave Two quantum annealers, one at NASA Ames Research Center in Moffett Field, California, and another at D-Wave Systems in Burnaby, Canada. We show that the recalibration procedure not only reduces the magnitudes of the biases in the programmable parameters but also enhances the performance of the device on a set of random benchmark instances.

Highlights

Quantum annealing (QA) is a metaheuristic for solving combinatorial optimization problems[1]
This model has been used in an attempt to explain the failure rate of D-Wave devices as partly due to misspecification of the programmable values
The variances we report in this paper are in a sense incomparable to those just mentioned, and relevant only within the context of the experiments described below

Summary

Results

Because actual quantum annealers operate at non-zero temperature, there exists some threshold for the values of the fields {hi} and couplings {Jij} below which thermal effects dominate the annealing process. The distribution of the residual biases from the second iteration, while narrower than that from the first, is not centered around zero We believe this is due to overcorrection; that is, the estimates of the biases from the first iteration have a high degree of uncertainty, and so subtracting their values from the intended value introduces some amount of bias itself. This is consistent with the overall small magnitudes of the J biases relative to those of the h biases, especially as compared to the corresponding noise levels.

Range rJ Greedy Elite mean

Discussion

Additional Information