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A Survey of Programming Tools for D-Wave Quantum-Annealing Processors

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High Performance Computing (ISC High Performance 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10876))

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Abstract

The rapid growth in the realized performance of D-Wave Systems’ annealing-based quantum processing units (QPUs) has sparked a surge in tools development to deliver the anticipated performance to application developers. In this survey we describe the tools that are available, their goals (e.g., performance or ease of use), the programming abstractions they expose, and their use for application development. The existing tools confirm the need for interfaces at a variety of points on the continuum between complexity and simplicity in using the QPU. Most of the current tools abstract the hardware’s native topology but generally not using existing interfaces that are familiar to typical programmers. To date, only a small number of applications have been implemented for QPUs. Our survey finds that tools provide potentially great leverage to enable more applications as long as the tools expose the appropriate abstractions and deliver the anticipated performance.

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Notes

  1. 1.

    Note the possibility of confusion between this definition of unconstrained BQP and the definition of BQP as bounded-error quantum polynomial time [4], a computational complexity class. To avoid this confusion, we refer to the problem as a QUBO throughout this paper.

  2. 2.

    Chain strength is the relative strength of couplings between qubits corresponding to the same problem variable to couplings between qubits corresponding to different problem variables.

  3. 3.

    A chain is considered broken if its qubits, which correspond to the same problem variable, are assigned different values.

  4. 4.

    Section 2 mentions the difference between the Ising-model Hamiltonian and the QUBO. For consistency with the rest of this paper we use QUBO terminology here.

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Acknowledgments

Thanks to the developers of the tools for helping us understand them: Timothy Goodrich of North Carolina State University for the virtual-hardware embedder; Denny Dahl of D-Wave Systems for dw, Quantum Apprentice, and the screenshot of Quantum Apprentice in action (Fig. 1); Matt Johnson, Peter McMahon, and David Hyde of QC Ware for QC Ware Platform; Andrew Fursman of 1QBit for QDK; Mark Hodson and John Kelly of QxBranch for QxLib; Alex McCaskey, Keith Britt, and Kathleen Hamilton of Oak Ridge National Laboratory for XACC; Carleton Coffrin and Dan O’Malley of Los Alamos National Laboratory for bqpsolvers and ThreeQ, respectively.

Los Alamos National Laboratory is operated by Los Alamos National Security LLC for the US Department of Energy under contract DE-AC52-06NA25396.

Any use of trade names or trademarks herein is purely nominative and is not meant to suggest an endorsement or association between any person and the authors (or the authors’ employers).

Author information

Authors and Affiliations

  1. Computer, Computational and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA

    Scott Pakin

  2. D-Wave Systems Inc., Burnaby, BC, V5G-4M9, Canada

    Steven P. Reinhardt

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  1. Scott Pakin
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Correspondence to Scott Pakin .

Editor information

Editors and Affiliations

  1. Tokyo Institute of Technology, Tokyo, Japan

    Rio Yokota

  2. University of Edinburgh, Edinburgh, United Kingdom

    Michèle Weiland

  3. King Abdullah University of Science and Technology, Thuwal, Saudi Arabia

    David Keyes

  4. Technische Universität München, Garching bei München, Germany

    Carsten Trinitis

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Pakin, S., Reinhardt, S.P. (2018). A Survey of Programming Tools for D-Wave Quantum-Annealing Processors. In: Yokota, R., Weiland, M., Keyes, D., Trinitis, C. (eds) High Performance Computing. ISC High Performance 2018. Lecture Notes in Computer Science(), vol 10876. Springer, Cham. https://doi.org/10.1007/978-3-319-92040-5_6

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  • DOI: https://doi.org/10.1007/978-3-319-92040-5_6

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