| Autor: |
Bulmer JFF; Quantum Engineering Technology Labs, University of Bristol, Bristol, UK., Bell BA; Ultrafast Quantum Optics Group, Department of Physics, Imperial College London, London, UK., Chadwick RS; Quantum Engineering Technology Labs, University of Bristol, Bristol, UK.; Quantum Engineering Centre for Doctoral Training, University of Bristol, Bristol, UK., Jones AE; Quantum Engineering Technology Labs, University of Bristol, Bristol, UK., Moise D; Hewlett Packard Enterprise, Zurich, Switzerland., Rigazzi A; Hewlett Packard Enterprise, Zurich, Switzerland., Thorbecke J; Hewlett Packard Enterprise, Amstelveen, Netherlands., Haus UU; HPE HPC/AI EMEA Research Lab, Wallisellen, Switzerland., Van Vaerenbergh T; Hewlett Packard Labs, HPE Belgium, Diegem, Belgium., Patel RB; Ultrafast Quantum Optics Group, Department of Physics, Imperial College London, London, UK.; Department of Physics, University of Oxford, Oxford, UK., Walmsley IA; Ultrafast Quantum Optics Group, Department of Physics, Imperial College London, London, UK., Laing A; Quantum Engineering Technology Labs, University of Bristol, Bristol, UK. |
| Abstrakt: |
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 GBS experiments that run in minutes would require 600 million years to simulate using the best preexisting classical algorithms. Here, we present faster classical GBS simulation methods, including speed and accuracy improvements to the calculation of loop hafnians. We test these on a ∼100,000-core supercomputer to emulate GBS experiments with up to 100 modes and up to 92 photons. This reduces the simulation time for state-of-the-art GBS experiments to several months, a nine-orders of magnitude improvement over previous estimates. Last, we introduce a distribution that is efficient to sample from classically and that passes a variety of GBS validation methods. |
