The boundary for quantum advantage in Gaussian boson sampling

TLDR

Identifying the boundary where quantum machines surpass classical ones is essential, and Gaussian boson sampling—measuring photons from a highly entangled Gaussian state—is a leading approach to achieving quantum advantage. The authors aim to present faster classical GBS simulation methods, improving loop hafnian calculations. They implement the methods on a ~100,000‑core supercomputer to emulate GBS experiments with up to 100 modes and 92 photons, and introduce a classically samplable distribution that passes GBS validation tests. The new methods cut simulation time from an estimated 600 million years to several months, a nine‑order‑of‑magnitude speedup for state‑of‑the‑art GBS experiments.

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 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.