Another paper, demonstrating some interesting techniques for overcoming practical problems in using D-Wave hardware. (Apologies Diana for the continuing lack of interpretation of these results :-) ). These techniques were applied to Low Density Parity Check problems.

# Discrete optimization using quantum annealing on sparse Ising models

^{1}D-Wave Systems, Burnaby, BC, Canada^{2}Department of Computer Science, Joint Center for Quantum Information and Computer Science, University of Maryland, College Park, MD, USA

This paper discusses techniques for solving discrete optimization problems using quantum annealing. Practical issues likely to affect the computation include precision limitations, finite temperature, bounded energy range, sparse connectivity, and small numbers of qubits. To address these concerns we propose a way of finding energy representations with large classical gaps between ground and first excited states, efficient algorithms for mapping non-compatible Ising models into the hardware, and the use of decomposition methods for problems that are too large to fit in hardware. We validate the approach by describing experiments with D-Wave quantum hardware for low density parity check decoding with up to 1000 variables.