The Boundary for Quantum Advantage in Gaussian Boson Sampling
- URL: http://arxiv.org/abs/2108.01622v1
- Date: Tue, 3 Aug 2021 16:49:40 GMT
- Title: The Boundary for Quantum Advantage in Gaussian Boson Sampling
- Authors: Jacob F. F. Bulmer, Bryn A. Bell, Rachel S. Chadwick, Alex E. Jones,
Diana Moise, Alessandro Rigazzi, Jan Thorbecke, Utz-Uwe Haus, Thomas Van
Vaerenbergh, Raj B. Patel, Ian A. Walmsley, Anthony Laing
- Abstract summary: State-of-the-art quantum photonics experiments would require 600 million years to simulate using the best pre-existing classical algorithms.
We present substantially faster classical GBS simulation methods, including speed and accuracy improvements.
This reduces the run-time of simulating state-of-the-art GBS experiments to several months -- a nine orders of magnitude improvement over previous estimates.
- Score: 44.62475518267084
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Identifying the boundary beyond which quantum machines provide a
computational advantage over their classical counterparts is a crucial step in
charting their usefulness. Gaussian Boson Sampling (GBS), in which photons are
measured from a highly entangled Gaussian state, is a leading approach in
pursuing quantum advantage. State-of-the-art quantum photonics experiments
that, once programmed, run in minutes, would require 600 million years to
simulate using the best pre-existing classical algorithms. Here, we present
substantially faster classical GBS simulation methods, including speed and
accuracy improvements to the calculation of loop hafnians, the matrix function
at the heart of GBS. We test these on a $\sim \! 100,000$ core supercomputer to
emulate a range of different GBS experiments with up to 100 modes and up to 92
photons. This reduces the run-time of classically simulating state-of-the-art
GBS experiments to several months -- a nine orders of magnitude improvement
over previous estimates. Finally, we introduce a distribution that is efficient
to sample from classically and that passes a variety of GBS validation methods,
providing an important adversary for future experiments to test against.
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