Gaussian Boson Sampling with Pseudo-Photon-Number Resolving Detectors
and Quantum Computational Advantage
- URL: http://arxiv.org/abs/2304.12240v3
- Date: Fri, 1 Sep 2023 18:38:13 GMT
- Title: Gaussian Boson Sampling with Pseudo-Photon-Number Resolving Detectors
and Quantum Computational Advantage
- Authors: Yu-Hao Deng, Yi-Chao Gu, Hua-Liang Liu, Si-Qiu Gong, Hao Su, Zhi-Jiong
Zhang, Hao-Yang Tang, Meng-Hao Jia, Jia-Min Xu, Ming-Cheng Chen, Jian Qin,
Li-Chao Peng, Jiarong Yan, Yi Hu, Jia Huang, Hao Li, Yuxuan Li, Yaojian Chen,
Xiao Jiang, Lin Gan, Guangwen Yang, Lixing You, Li Li, Han-Sen Zhong, Hui
Wang, Nai-Le Liu, Jelmer J. Renema, Chao-Yang Lu, Jian-Wei Pan
- Abstract summary: We report new Gaussian boson sampling experiments with pseudo-photon-number-resolving detection.
We develop a more complete model for the characterization of the noisy Gaussian boson sampling.
- Score: 28.449634706456898
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We report new Gaussian boson sampling experiments with
pseudo-photon-number-resolving detection, which register up to 255 photon-click
events. We consider partial photon distinguishability and develop a more
complete model for the characterization of the noisy Gaussian boson sampling.
In the quantum computational advantage regime, we use Bayesian tests and
correlation function analysis to validate the samples against all current
classical mockups. Estimating with the best classical algorithms to date,
generating a single ideal sample from the same distribution on the
supercomputer Frontier would take ~ 600 years using exact methods, whereas our
quantum computer, Jiuzhang 3.0, takes only 1.27 us to produce a sample.
Generating the hardest sample from the experiment using an exact algorithm
would take Frontier ~ 3.1*10^10 years.
Related papers
- Gaussian Boson Sampling to Accelerate NP-Complete Vertex-Minor Graph
Classification [0.9935277311162707]
We propose a hybrid quantum-classical algorithm for the NP-complete problem of determining if two graphs are minor of one another.
We find a graph embedding that allows trading between the one-shot classification accuracy and the amount of input squeezing.
We introduce a new classical algorithm based on graph spectra, which we show outperforms various well-known graph-similarity algorithms.
arXiv Detail & Related papers (2024-02-05T21:24:11Z) - Testing of on-cloud Gaussian Boson Sampler "Borealis'' via graph theory [0.0]
photonic-based sampling machines solving the Gaussian Boson Sampling problem play a central role in the experimental demonstration of a quantum computational advantage.
In this work, we test the performances of the recently developed photonic machine Borealis as a sampling machine and its possible use cases in graph theory.
arXiv Detail & Related papers (2023-06-21T09:02:55Z) - Classical algorithm for simulating experimental Gaussian boson sampling [2.1684911254068906]
Gaussian boson sampling is a promising candidate for showing experimental quantum advantage.
Despite a high photon loss rate and the presence of noise, they are currently claimed to be hard to classically simulate with the best-known classical algorithm.
We present a classical tensor-network algorithm that simulates Gaussian boson sampling and whose complexity can be significantly reduced when the photon loss rate is high.
arXiv Detail & Related papers (2023-06-06T14:19:48Z) - Gaussian boson sampling with click-counting detectors [4.437382576172235]
We investigate the problem of sampling from a general multi-mode Gaussian state using click-counting detectors.
We show that the probability of obtaining a given outcome is related to a new matrix function dubbed as the Kensingtonian.
arXiv Detail & Related papers (2023-05-01T14:48:54Z) - Importance sampling for stochastic quantum simulations [68.8204255655161]
We introduce the qDrift protocol, which builds random product formulas by sampling from the Hamiltonian according to the coefficients.
We show that the simulation cost can be reduced while achieving the same accuracy, by considering the individual simulation cost during the sampling stage.
Results are confirmed by numerical simulations performed on a lattice nuclear effective field theory.
arXiv Detail & Related papers (2022-12-12T15:06:32Z) - Validation tests of GBS quantum computers give evidence for quantum
advantage with a decoherent target [62.997667081978825]
We use positive-P phase-space simulations of grouped count probabilities as a fingerprint for verifying multi-mode data.
We show how one can disprove faked data, and apply this to a classical count algorithm.
arXiv Detail & Related papers (2022-11-07T12:00:45Z) - Sensing Cox Processes via Posterior Sampling and Positive Bases [56.82162768921196]
We study adaptive sensing of point processes, a widely used model from spatial statistics.
We model the intensity function as a sample from a truncated Gaussian process, represented in a specially constructed positive basis.
Our adaptive sensing algorithms use Langevin dynamics and are based on posterior sampling (textscCox-Thompson) and top-two posterior sampling (textscTop2) principles.
arXiv Detail & Related papers (2021-10-21T14:47:06Z) - Bosonic field digitization for quantum computers [62.997667081978825]
We address the representation of lattice bosonic fields in a discretized field amplitude basis.
We develop methods to predict error scaling and present efficient qubit implementation strategies.
arXiv Detail & Related papers (2021-08-24T15:30:04Z) - The Boundary for Quantum Advantage in Gaussian Boson Sampling [44.62475518267084]
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.
arXiv Detail & Related papers (2021-08-03T16:49:40Z) - Simulating complex networks in phase space: Gaussian boson sampling [62.997667081978825]
We show how phase-space simulations of Gaussian quantum states in a photonic network permit verification of measurable correlations.
We extend this to more than 16,000 modes, and describe how to simulate genuine multipartite entanglement.
arXiv Detail & Related papers (2021-02-20T13:12:51Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.