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A Novel Ant Colony Optimization Strategy for the Quantum Circuit Compilation Problem

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Evolutionary Computation in Combinatorial Optimization (EvoCOP 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12692))

Abstract

Quantum Computing represents the most promising technology towards speed boost in computation, opening the possibility of major breakthroughs in several disciplines including Artificial Intelligence. This paper investigates the performance of a novel Ant Colony Optimization (ACO) algorithm for the realization (compilation) of nearest-neighbor compliant quantum circuits of minimum duration. In fact, current technological limitations (e.g., decoherence effect) impose that the overall duration (makespan) of the quantum circuit realization be minimized, and therefore the production of minimum-makespan compiled circuits for present and future quantum machines is of paramount importance. In our ACO algorithm (QCC-ACO), we introduce a novel pheromone model, and we leverage a heuristic-based Priority Rule to control the iterative selection of the quantum gates to be inserted in the solution.

The proposed QCC-ACO algorithm has been tested on a set of quantum circuit benchmark instances of increasing sizes available from the recent literature. We demonstrate that the QCC-ACO obtains results that outperform the current best solutions in the literature against the same benchmark, succeeding in significantly improving the makespan values for a great number of instances and demonstrating the scalability of the approach.

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Notes

  1. 1.

    A set of benchmark instances of different size belonging to the Quantum Approximate Optimization Algorithm (QAOA) class [10, 11] tailored for the MaxCut problem and devised to be executed on top of a hardware architecture proposed by Rigetti Computing Inc. [20].

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Authors and Affiliations

  1. University of Perugia, Perugia, Italy

    Marco Baioletti

  2. Institute of Cognitive Sciences and Technologies (ISTC-CNR), Rome, Italy

    Riccardo Rasconi & Angelo Oddi

Authors
  1. Marco Baioletti
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  2. Riccardo Rasconi
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  3. Angelo Oddi
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Corresponding author

Correspondence to Marco Baioletti .

Editor information

Editors and Affiliations

  1. Aberystwyth University, Aberystwyth, UK

    Christine Zarges

  2. Université du Littoral Côte d'Opale, Calais, France

    Sébastien Verel

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Baioletti, M., Rasconi, R., Oddi, A. (2021). A Novel Ant Colony Optimization Strategy for the Quantum Circuit Compilation Problem. In: Zarges, C., Verel, S. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2021. Lecture Notes in Computer Science(), vol 12692. Springer, Cham. https://doi.org/10.1007/978-3-030-72904-2_1

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  • DOI: https://doi.org/10.1007/978-3-030-72904-2_1

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  • Print ISBN: 978-3-030-72903-5

  • Online ISBN: 978-3-030-72904-2

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