Algorithm engineering for a quantum annealing platform

Abstract

Recent advances bring within reach the viability of solving combinatorial problems using a quantum annealing algorithm implemented on a purpose-built platform that exploits quantum properties. However, the question of how to tune the algorithm for most effective use in this framework is not well understood. In this paper we describe some operational parameters that drive performance, discuss approaches for mitigating sources of error, and present experimental results from a D-Wave Two quantum annealing processor.

16 pages. V2: minor edits

References in corpus (4)

• A practical heuristic for finding graph minors
• Consistency Tests of Classical and Quantum Models for a Quantum Annealer
• Quantum annealing correction for random Ising problems
• Comparative Study of the Performance of Quantum Annealing and Simulated Annealing

Cited by in corpus (5)

• Decoherence in adiabatic quantum computation
• A Performance Estimator for Quantum Annealers: Gauge selection and Parameter Setting
• Mapping NP-Hard Problems to Restricted Adiabatic Quantum Architectures
• Benchmarking Embedded Chain Breaking in Quantum Annealing
• Adiabatic Quantum Graph Matching with Permutation Matrix Constraints