Designing Quantum Annealing Schedules using Bayesian Optimization

Abstract

We propose and analyze the use of Bayesian optimization techniques to design quantum annealing schedules with minimal user and resource requirements. We showcase our scheme with results for two paradigmatic spin models. We find that Bayesian optimization is able to identify schedules resulting in fidelities several orders of magnitude better than standard protocols for both quantum and reverse annealing, as applied to the $p$-spin model. We also show that our scheme can help improve the design of hybrid quantum algorithms for hard combinatorial optimization problems, such as the maximum independent set problem, and illustrate these results via experiments on a neutral atom quantum processor available on Amazon Braket.

arXiv comment20 pages, 15 figures

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Cited by in corpus (13)

• Post-processing variationally scheduled quantum algorithm for constrained combinatorial optimization problems
• Beyond Quantum Annealing: Optimal control solutions to MaxCut problems
• Counterdiabatic Driving with Performance Guarantees
• From quantum-enhanced to quantum-inspired Monte Carlo
• Long-lived oscillations of metastable states in neutral atom systems
• Implementing transferable annealing protocols for combinatorial optimisation on neutral atom quantum processors: a case study on smart-charging of electric vehicles
• Improving the efficiency of quantum annealing with controlled diagonal catalysts
• Universal resources for quantum approximate optimization algorithm and quantum annealing
• Hardness-dependent quantum adiabatic schedules for the maximum-independent-set problem
• Generalized Probabilistic Approximate Optimization Algorithm
• Generation of quantum phases of matter and finding a maximum-weight independent set of unit-disk graphs using Rydberg atoms
• Efficient Online Quantum Circuit Learning with No Upfront Training
• Quantum combinatorial optimization beyond the variational paradigm: simple schedules for hard problems