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Classical simulations of Gaussian boson sampling

Rachel S. Chadwick

Jan 1, 20220 citations

Simulating Gaussian boson sampling on graphs in polynomial time

Konrad Anand, Zongchen Chen, Mary Cryan +4 more

We show that a distribution related to Gaussian Boson Sampling (GBS) on graphs can be sampled classically in polynomial time. Graphical applications of GBS typically sample from this distribution, and thus quantum algorithms do not provide exponential speedup for these applications. We also show that another distribution related to Boson sampling can be sampled classically in polynomial time.

quant-pharXiv:2511.16558Nov 21, 20250 citations

Classical algorithm for simulating experimental Gaussian boson sampling

Changhun Oh, Minzhao Liu, Yuri Alexeev +2 more

arXiv:2306.03709Nature PhysicsJun 25, 202457 citations

Extended validations on photon number resolving detector based Gaussian boson sampling with low noises

Ji Yang, Wu, Yongzheng, Shi Wang +3 more

Gaussian boson sampling (GBS) is a variety of boson sampling overcoming the stable single-photon preparation difficulty of the later. However, like those in the original version, noises in GBS will also result in the deviation of output patterns and the reduction of classical simulation complexity. We extend the pattern recognition validation, together with the correlation approach as a comparison, on GBS using photon number resolving detectors, with noises of both photon loss and distinguishability, to quantificationally evaluate noise levels. As for the classical simulation with noises to be used during validations, it is actually a simulation of mixed states where we employ an existing photon-pair strategy to realize polynomial speedup locally. Furthermore, we use an output-binning strategy to realize validation speedup. Our simulation indicates that the pattern recognition protocol is useful for noise evaluations of GBS even when noises are sufficiently low.Gaussian boson sampling (GBS) is a variety of boson sampling overcoming the stable single-photon preparation difficulty of the later. However, like those in the original version, noises in GBS will also result in the deviation of output patterns and the reduction of classical simulation complexity. We extend the pattern recognition validation, together with the correlation approach as a comparison, on GBS using photon number resolving detectors, with noises of both photon loss and distinguishability, to quantificationally evaluate noise levels. As for the classical simulation with noises to be used during validations, it is actually a simulation of mixed states where we employ an existing photon-pair strategy to realize polynomial speedup locally. Furthermore, we use an output-binning strategy to realize validation speedup. Our simulation indicates that the pattern recognition protocol is useful for noise evaluations of GBS even when noises are sufficiently low.

arXiv:2510.06300Oct 7, 20250 citations

Extended validations on photon number resolving detector based Gaussian boson sampling to evaluate noise levels

Yang Ji, Yongzheng Wu, Shi Wang +3 more

Abstract Gaussian boson sampling (GBS) is a variety of boson sampling overcoming the stable single-photon preparation difficulty of the later. However, like those in the original version, noises in GBS will also result in the deviation of output distributions and the reduction of classical simulation complexity. We extend the pattern recognition validation, together with the correlation validation as a comparison, on GBS using photon number resolving detectors, with noises of both photon loss and distinguishability, to quantificationally evaluate noise levels. As for the classical simulation with noises to be used during validations, it is actually a simulation of mixed states where noises have been proved to be possibly utilized to realize computation speedup. Furthermore, we use an output-binning strategy to realize validation speedup. Our work indicates that the pattern recognition protocol is useful for noise evaluations of GBS even when noises are sufficiently low.Abstract Gaussian boson sampling (GBS) is a variety of boson sampling overcoming the stable single-photon preparation difficulty of the later. However, like those in the original version, noises in GBS will also result in the deviation of output distributions and the reduction of classical simulation complexity. We extend the pattern recognition validation, together with the correlation validation as a comparison, on GBS using photon number resolving detectors, with noises of both photon loss and distinguishability, to quantificationally evaluate noise levels. As for the classical simulation with noises to be used during validations, it is actually a simulation of mixed states where noises have been proved to be possibly utilized to realize computation speedup. Furthermore, we use an output-binning strategy to realize validation speedup. Our work indicates that the pattern recognition protocol is useful for noise evaluations of GBS even when noises are sufficiently low.

Physica ScriptaSep 16, 20260 citations

Experimental Gaussian Boson sampling

Han-Sen Zhong, Li-Chao Peng, Yuan Li +15 more

arXiv:1905.00170中国科学通报：英文版Apr 1, 201972 citations

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