All Title Author
Keywords Abstract


Discrete optimization using Quantum Annealing on sparse Ising models

DOI: 10.3389/fphy.2014.00056

Keywords: Quantum Annealing, discrete optimization, penalty functions, Ising Model, constraint satisfaction

Full-Text   Cite this paper   Add to My Lib

Abstract:

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.

Full-Text

comments powered by Disqus