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Greedy Randomized Search for Scalable Compilation of Quantum Circuits

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Integration of Constraint Programming, Artificial Intelligence, and Operations Research (CPAIOR 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10848))

Abstract

This paper investigates the performances of a greedy randomized algorithm to optimize the realization of nearest-neighbor compliant quantum circuits. Current technological limitations (decoherence effect) impose that the overall duration (makespan) of the quantum circuit realization be minimized. One core contribution of this paper is a lexicographic two-key ranking function for quantum gate selection: the first key acts as a global closure metric to minimize the solution makespan; the second one is a local metric acting as “tie-breaker” for avoiding cycling. Our algorithm has been tested on a set of quantum circuit benchmark instances of increasing sizes available from the recent literature. We demonstrate that our heuristic approach outperforms the solutions obtained in previous research against the same benchmark, both from the CPU efficiency and from the solution quality standpoint.

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Notes

  1. 1.

    It is implicitly supposed that at the beginning, the i-th qstate is initialized at the i-th location.

  2. 2.

    Note that in general, a k-qubit gate occupies k chains.

  3. 3.

    The benchmark is available at: https://ti.arc.nasa.gov/m/groups/asr/planning-and-scheduling/VentCirComp17_data.zip.

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Authors and Affiliations

  1. Institute of Cognitive Sciences and Technologies (ISTC-CNR), Via S. Martino della Battaglia, 44, 00185, Rome, Italy

    Angelo Oddi & Riccardo Rasconi

Authors
  1. Angelo Oddi
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  2. Riccardo Rasconi
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Correspondence to Angelo Oddi .

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Editors and Affiliations

  1. Carnegie Mellon University, Pittsburgh, Pennsylvania, USA

    Willem-Jan van Hoeve

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Oddi, A., Rasconi, R. (2018). Greedy Randomized Search for Scalable Compilation of Quantum Circuits. In: van Hoeve, WJ. (eds) Integration of Constraint Programming, Artificial Intelligence, and Operations Research. CPAIOR 2018. Lecture Notes in Computer Science(), vol 10848. Springer, Cham. https://doi.org/10.1007/978-3-319-93031-2_32

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  • DOI: https://doi.org/10.1007/978-3-319-93031-2_32

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