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Related papers: Experimental Gaussian Boson Sampling

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While universal quantum computers ideally solve problems such as factoring integers exponentially more efficiently than classical machines, the formidable challenges in building such devices motivate the demonstration of simpler,…

Hard optimization problems are often approached by finding approximate solutions. Here, we highlight the concept of proportional sampling and discuss how it can be used to improve the performance of stochastic algorithms for optimization.…

Quantum Physics · Physics 2018-08-01 Juan Miguel Arrazola , Thomas R. Bromley , Patrick Rebentrost

Boson Sampling is a task that is conjectured to be computationally hard for a classical computer, but which can be efficiently solved by linear-optical interferometers with Fock state inputs. Significant advances have been reported in the…

Quantum Physics · Physics 2023-04-14 Nicolò Spagnolo , Daniel J. Brod , Ernesto F. Galvão , Fabio Sciarrino

We show how phase-space simulations of Gaussian quantum states in a photonic network permit verification of measurable correlations of Gaussian boson sampling (GBS) quantum computers. Our results agree with experiments for up to 100-th…

Quantum Physics · Physics 2022-02-09 Peter D. Drummond , Bogdan Opanchuk , Alexander Dellios , Margaret D. Reid

Gaussian boson sampling (GBS) is a prominent candidate for the experimental demonstration of quantum advantage. However, while the current implementations of GBS are unavoidably subject to noise, the robustness of the classical…

Quantum Physics · Physics 2025-11-12 Byeongseon Go , Changhun Oh , Hyunseok Jeong

BosonSampling is an intermediate model of quantum computation where linear-optical networks are used to solve sampling problems expected to be hard for classical computers. Since these devices are not expected to be universal for quantum…

Quantum Physics · Physics 2016-01-27 Scott Aaronson , Daniel J. Brod

We analyze the impact of photon loss on the photon-number statistics of Gaussian states. Specifically, we propose and carefully evaluate several methods to mitigate deviations in the photon-number distributions of lossy (displaced) squeezed…

Quantum Physics · Physics 2026-03-24 Hendrik Ellenberg , René Sondenheimer

We describe an efficient, scalable Gaussian boson sampler based on a classical description of squeezed quantum light and a deterministic model of single-photon detectors that click when the incident amplitude falls above a given threshold.…

Quantum Physics · Physics 2025-07-24 Sarvesh Raghuraman , Aditya Patwardhan , Brian La Cour

Gaussian boson sampling is a promising scheme for demonstrating a quantum computational advantage using photonic states that are accessible in a laboratory and, thus, offer scalable sources of quantum light. In this contribution, we study…

Quantum Physics · Physics 2019-02-22 D. S. Phillips , M. Walschaers , J. J. Renema , I. A. Walmsley , N. Treps , J. Sperling

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…

Although the Schr{\"o}dinger and Heisenberg pictures are equivalent formulations of quantum mechanics, simulations performed choosing one over the other can greatly impact the computational resources required to solve a problem. Here we…

Quantum Physics · Physics 2024-02-27 Dario Cilluffo , Nicola Lorenzoni , Martin B. Plenio

We suggest a novel scheme for generating multimode squeezed states for the boson sampling implementation. The idea is to replace a commonly used linear interferometer by a multimode resonator containing a passive optical element consisting…

Quantum Physics · Physics 2024-09-16 Sergey V. Tarasov , Vladimir V. Kocharovsky

Boson sampling is a simple model for non-universal linear optics quantum computing using far fewer physical resources than universal schemes. An input state comprising vacuum and single photon states is fed through a Haar-random linear…

Bosonic Gaussian states are ubiquitous in quantum optics and condensed matter physics. While they are efficiently handled within the Gaussian formalism, sampling requires calculating amplitudes in the boson occupation basis. This step,…

Quantum Physics · Physics 2026-05-12 Tong Liu , Hui-Ke Jin , Tao Xiang , Hong-Hao Tu

Squeezed states of light have been improving the sensitivity of gravitational-wave observatories and are nonclassical resources of quantum cryptography and envisioned photonic quantum computers. The higher the squeeze factor is, the higher…

Quantum Physics · Physics 2023-01-11 Stephan Grebien , Julian Goettsch , Boris Hage , Jaromir Fiurasek , Roman Schnabel

Boson Sampling represents a promising witness of the supremacy of quantum systems as a resource for the solution of computational problems. The classical hardness of Boson Sampling has been related to the so called Permanent-of-Gaussians…

Quantum Physics · Physics 2015-12-01 Ludovico Latmiral

Gaussian Boson Sampling (GBS) provides a route toward demonstrating quantum computational advantage. However, optical loss, which reduces the entanglement in the system, can render GBS results classically simulable. We propose a nonlinear…

Quantum Physics · Physics 2026-05-29 Yukuan Zhao , Xiao-Ye Xu , Chuan-Feng Li , Guang-Can Guo

Quantum advantage, benchmarking the computational power of quantum machines outperforming all classical computers in a specific task, represents a crucial milestone in developing quantum computers and has been driving different physical…

Boson sampling is a problem intractable for classical computers, but can be naturally solved on a specialized photonic quantum simulator which requires less resources than building a universal quantum computer. The biggest challenge to…

Boson sampling is expected to be one of an important milestones that will demonstrate quantum supremacy. The present work establishes the benchmarking of Gaussian boson sampling (GBS) with threshold detection based on the Sunway TaihuLight…

Distributed, Parallel, and Cluster Computing · Computer Science 2020-09-03 Yuxuan Li , Mingcheng Chen , Yaojian Chen , Haitian Lu , Lin Gan , Chaoyang Lu , Jianwei Pan , Haohuan Fu , Guangwen Yang