Quantum annealing correction for random Ising problems

Abstract

We demonstrate that the performance of a quantum annealer on hard random Ising optimization problems can be substantially improved using quantum annealing correction (QAC). Our error correction strategy is tailored to the D-Wave Two device. We find that QAC provides a statistically significant enhancement in the performance of the device over a classical repetition code, improving as a function of problem size as well as hardness. Moreover, QAC provides a mechanism for overcoming the precision limit of the device, in addition to correcting calibration errors. Performance is robust even to missing qubits. We present evidence for a constructive role played by quantum effects in our experiments by contrasting the experimental results with the predictions of a classical model of the device. Our work demonstrates the importance of error correction in appropriately determining the performance of quantum annealers.

17 pages, 14 figures. v2: updated to published version

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• Decoherence in adiabatic quantum computation
• Quantum annealing correction at finite temperature: ferromagnetic $p$-spin models
• Algorithm engineering for a quantum annealing platform
• Kibble-Zurek scaling in quantum speed limits for shortcuts to adiabaticity
• The three phases of quantum annealing: fast, slow, and very slow
• Fair sampling of ground-state configurations of binary optimization problems
• Patch-planting spin-glass solution for benchmarking
• Quantum annealing with a nonvanishing final value of the transverse field
• Mapping NP-Hard Problems to Restricted Adiabatic Quantum Architectures
• Benchmarking Embedded Chain Breaking in Quantum Annealing
• Assessing the performance of quantum annealing with nonlinear driving