Performance of a Quantum Annealer for Ising Ground State Computations on Chimera Graphs

Abstract

Quantum annealing is getting increasing attention in combinatorial optimization. The quantum processing unit by D-Wave is constructed to approximately solve Ising models on so-called Chimera graphs. Ising models are equivalent to quadratic unconstrained binary optimization (QUBO) problems and maximum cut problems on the associated graphs. We have tailored branch-and-cut as well as semidefinite programming algorithms for solving Ising models for Chimera graphs to provable optimality and use the strength of these approaches for comparing our solution values to those obtained on the current quantum annealing machine D-Wave 2000Q. This allows for the assessment of the quality of solutions produced by the D-Wave hardware. It has been a matter of discussion in the literature how well the D-Wave hardware performs at its native task, and our experiments shed some more light on this issue.

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• Approaching Collateral Optimization for NISQ and Quantum-Inspired Computing
• Analyzing the quantum approximate optimization algorithm: ansätze, symmetries, and Lie algebras
• Optimization of ionic configurations in battery materials by quantum annealing
• QUBO Decision Tree: Annealing Machine Extends Decision Tree Splitting
• From Classical to quantum stochastic process
• Quantum Software Ecosystem Design
• Enhancing Quantum Algorithms for Quadratic Unconstrained Binary Optimization via Integer Programming
• Optimal Sufficient Requirements on the Embedded Ising Problem in Polynomial Time
• Curve fitting on a quantum annealer for an advanced navigation method