Classical Modeling of a Lossy Gaussian Bosonic Sampler.

Entropy (Basel)

Department of Physics, Lomonosov Moscow State University, Leninskie Gory 1, 119991 Moscow, Russia.

Published: June 2024

AI Article Synopsis

  • Gaussian boson sampling (GBS) is a problem that could show how quantum computing is better than classical computing.
  • An algorithm is introduced to simulate GBS classically using a Taylor series expansion, achieving higher accuracy with more terms.
  • The conditions for optimal performance of the algorithm are identified, which match those in recent experiments claiming quantum advantage, allowing for classical simulation of those experiments.

Article Abstract

Gaussian boson sampling (GBS) is considered a candidate problem for demonstrating quantum advantage. We propose an algorithm for the approximate classical simulation of a lossy GBS instance. The algorithm relies on the Taylor series expansion, and increasing the number of terms of the expansion that are used in the calculation yields greater accuracy. The complexity of the algorithm is polynomial in the number of modes given the number of terms is fixed. We describe conditions for the input state squeezing parameter and loss level that provide the best efficiency for this algorithm (by efficient, we mean that the Taylor series converges quickly). In recent experiments that claim to have demonstrated quantum advantage, these conditions are satisfied; thus, this algorithm can be used to classically simulate these experiments.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11202939PMC
http://dx.doi.org/10.3390/e26060493DOI Listing

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