Exact simulation of Gaussian Boson Sampling in polynomial space and exponential time
arXiv:1908.08068 · doi:10.1103/PhysRevResearch.2.023005
Abstract
We introduce an exact classical algorithm for simulating Gaussian Boson Sampling (GBS). The complexity of the algorithm is exponential in the number of photons detected, which is itself a random variable. For a fixed number of modes, the complexity is in fact equivalent to that of calculating output probabilities, up to constant prefactors. The simulation algorithm can be extended to other models such as GBS with threshold detectors, GBS with displacements, and sampling linear combinations of Gaussian states. In the specific case of encoding non-negative matrices into a GBS device, our method leads to an approximate sampling algorithm with polynomial runtime. We implement the algorithm, making the code publicly available as part of Xanadu's The Walrus library, and benchmark its performance on GBS with random Haar interferometers and with encoded Erdős-Renyi graphs.
arXiv commentThe algorithms presented in this paper can be accessed using The Walrus library available at https://github.com/XanaduAI/thewalrus
References in corpus (27)
- Gaussian Quantum Information
- Boson Sampling on a Photonic Chip
- Experimental Boson Sampling
- Photonic Boson Sampling in a Tunable Circuit
- Gaussian Boson Sampling
- Boson Sampling for Molecular Vibronic Spectra
- Boson Sampling from Gaussian States
- Experimental Scattershot Boson Sampling
- Noise-free high-efficiency photon-number-resolving detectors
- Generation and sampling of quantum states of light in a silicon chip
- A detailed study of Gaussian Boson Sampling
- No imminent quantum supremacy by boson sampling
- Using Gaussian Boson Sampling to Find Dense Subgraphs
- Gaussian Boson Sampling using threshold detectors
- A quantum hardware-induced graph kernel based on Gaussian Boson Sampling
- Experimental Gaussian Boson Sampling
- Simulating realistic non-Gaussian state preparation
- Simple factorization of unitary transformations
- Quantum approximate optimization with Gaussian boson sampling
- Boson sampling with Gaussian measurements
- Point Processes with Gaussian Boson Sampling
- Franck-Condon factors by counting perfect matchings of graphs with loops
- Approximating Vibronic Spectroscopy with Imperfect Quantum Optics
- Classically simulating near-term partially-distinguishable and lossy boson sampling
- Driven Boson Sampling
- Hafnians, perfect matchings and Gaussian matrices
- Exact Boson Sampling using Gaussian continuous variable measurements
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