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Classical benchmarking of Gaussian Boson Sampling on the Titan supercomputer

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Abstract

Gaussian Boson Sampling (GBS) is a model of photonic quantum computing where single-mode squeezed states are sent through linear-optical interferometers and measured using single-photon detectors. In this work, we employ a recent exact sampling algorithm for GBS with threshold detectors to perform classical simulations on the Titan supercomputer. We determine the time and memory resources as well as the amount of computational nodes required to produce samples for different numbers of modes and detector clicks. It is possible to simulate a system with 800 optical modes postselected on outputs with 20 detector clicks, producing a single sample in roughly 2 h using 40% of the available nodes of Titan. Additionally, we benchmark the performance of GBS when applied to dense subgraph identification, even in the presence of photon loss. We perform sampling for several graphs containing as many as 200 vertices. Our findings indicate that large losses can be tolerated and that the use of threshold detectors is preferable over using photon-number-resolving detectors postselected on collision-free outputs.

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Acknowledgements

The authors thank Nathan Killoran, Joshua Izaac, Patrick Rebentrost, and Christian Weedbrook for useful discussions and valuable feedback. This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725.

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  1. Xanadu, Toronto, ON, M5G 2C8, Canada

    Brajesh Gupt, Juan Miguel Arrazola, Nicolás Quesada & Thomas R. Bromley

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  1. Brajesh Gupt
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  2. Juan Miguel Arrazola
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  3. Nicolás Quesada
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  4. Thomas R. Bromley
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Correspondence to Nicolás Quesada.

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Gupt, B., Arrazola, J.M., Quesada, N. et al. Classical benchmarking of Gaussian Boson Sampling on the Titan supercomputer. Quantum Inf Process 19, 249 (2020). https://doi.org/10.1007/s11128-020-02713-6

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