Tutorial: Calibration refinement in quantum annealing

Abstract

Quantum annealing has emerged as a powerful platform for simulating and optimizing classical and quantum Ising models. Quantum annealers, like other quantum and/or analog computing devices, are susceptible to nonidealities including crosstalk, device variation, and environmental noise. Compensating for these effects through calibration refinement or "shimming" can significantly improve performance, but often relies on ad-hoc methods that exploit symmetries in both the problem being solved and the quantum annealer itself. In this tutorial we attempt to demystify these methods. We introduce methods for finding exploitable symmetries in Ising models, and discuss how to use these symmetries to suppress unwanted bias. We work through several examples of increasing complexity, and provide complete Python code. We include automated methods for two important tasks: finding copies of small subgraphs in the qubit connectivity graph, and automatically finding symmetries of an Ising model via generalized graph automorphism. Code is available at https://github.com/dwavesystems/shimming-tutorial.

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• Scaling advantage in quantum simulation of geometrically frustrated magnets
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• Qubit spin ice
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Cited by in corpus (7)

• Beyond-classical computation in quantum simulation
• Using quantum annealing to design lattice proteins
• Quantum Quench Dynamics of Geometrically Frustrated Ising Models
• Classical Criticality via Quantum Annealing
• Charging a quantum spin network towards Heisenberg-limited precision
• Boltzmann Sampling of Frustrated J1 - J2 Ising Models with Programmable Quantum Annealers
• Observation of Magnetic Devil's Staircase-Like Behavior in Quasiperiodic Qubit Lattices