Best-case performance of quantum annealers on native spin-glass benchmarks: How chaos can affect success probabilities

TL;DR

This paper analyzes the best-case performance of quantum annealers on spin-glass benchmarks, showing how noise and chaos influence success probabilities and proposing strategies for robustness and improved benchmarking.

Contribution

It provides classical upper bounds on success probabilities for quantum annealers under realistic noise models and discusses implications for scaling and error correction.

Findings

Success probabilities are limited by noise and disorder.

Increasing qubits requires error correction or noise reduction.

Strategies for robust benchmarks are proposed.

Abstract

Recent tests performed on the D-Wave Two quantum annealer have revealed no clear evidence of speedup over conventional silicon-based technologies. Here, we present results from classical parallel-tempering Monte Carlo simulations combined with isoenergetic cluster moves of the archetypal benchmark problem-an Ising spin glass-on the native chip topology. Using realistic uncorrelated noise models for the D-Wave Two quantum annealer, we study the best-case resilience, i.e., the probability that the ground-state configuration is not affected by random fields and random-bond fluctuations found on the chip. We thus compute classical upper-bound success probabilities for different types of disorder used in the benchmarks and predict that an increase in the number of qubits will require either error correction schemes or a drastic reduction of the intrinsic noise found in these devices. We outline strategies to develop robust, as well as hard benchmarks for quantum annealing devices, as well as any other computing paradigm affected by noise.