English
Related papers

Related papers: Stringent and efficient assessment of Boson-Sampli…

200 papers

The first post-classical computation will most probably be performed not on a universal quantum computer, but rather on a dedicated quantum hardware. A strong candidate for achieving this is represented by the task of sampling from the…

Quantum Physics · Physics 2016-10-06 Borja Peropadre , Gian Giacomo Guerreschi , Joonsuk Huh , Alán Aspuru-Guzik

We study the hardness of classically simulating Gaussian boson sampling at nonzero photon distinguishability. We find that similar to regular boson sampling, distinguishability causes exponential attenuation of the many-photon interference…

Quantum Physics · Physics 2020-07-08 Jelmer J. Renema

In order to substantiate claims of quantum computational advantage, it is crucial to develop efficient methods for validating the experimental data. We propose a test of the correct functioning of a boson sampler with single-photon inputs…

Quantum Physics · Physics 2024-09-25 Benoit Seron , Leonardo Novo , Alex Arkhipov , Nicolas J. Cerf

Boson sampling is a sub-universal model used to show quantum speed-up. However, the methods of validation to prove quantum speedup are not robust and accurate. All verification methods involve additional or little studied assumptions. Here,…

The difficulty of validating large-scale quantum devices, such as Boson Samplers, poses a major challenge for any research program that aims to show quantum advantages over classical hardware. To address this problem, we propose a novel…

Gaussian boson sampling is a model of photonic quantum computing that has attracted attention as a platform for building quantum devices capable of performing tasks that are out of reach for classical devices. There is therefore significant…

Boson sampling can simulate physical problems for which classical simulations are inefficient. However, not all problems simulated by boson sampling are classically intractable. We consider a situation in which it is known that the outcome…

Quantum Physics · Physics 2021-05-03 Wojciech Roga , Masahiro Takeoka

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

Boson Sampling is the problem of sampling from the same distribution as indistinguishable single photons at the output of a linear optical interferometer. It is an example of a non-universal quantum computation which is believed to be…

Quantum Physics · Physics 2019-11-28 Alexandra E. Moylett , Raúl García-Patrón , Jelmer J. Renema , Peter S. Turner

The search for new, application-specific quantum computers designed to outperform any classical computer is driven by the ending of Moore's law and the quantum advantages potentially obtainable. Photonic networks are promising examples,…

Quantum Physics · Physics 2018-04-10 Bogdan Opanchuk , Laura Rosales-Zarate , Margaret D Reid , Peter D Drummond

Achieving a quantum computational advantage regime, and thus providing evidence against the extended Church-Turing thesis, remains one of the key challenges of modern science. Boson sampling seems to be a very promising platform in this…

Quantum Physics · Physics 2024-11-22 A. A. Mazanik , A. N. Rubtsov

Boson Sampling is the problem of sampling from the same output probability distribution as a collection of indistinguishable single photons input into a linear interferometer. It has been shown that, subject to certain computational…

Quantum Physics · Physics 2018-06-21 Alexandra E. Moylett , Peter S. Turner

Boson sampling solves a classically intractable problem by sampling from a probability distribution given by matrix permanents. We propose a scalable implementation of Boson sampling using local transverse phonon modes of trapped ions to…

Quantum Physics · Physics 2014-03-24 Chao Shen , Zhen Zhang , Luming Duan

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

Boson sampling, a computational problem conjectured to be hard to simulate on a classical machine, is a promising candidate for an experimental demonstration of quantum advantage using bosons. However, inevitable experimental noise and…

Quantum Physics · Physics 2022-10-12 Gabriele Bressanini , Hyukjoon Kwon , M. S. Kim

Boson Sampling represents a promising approach to obtain an evidence 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…

Quantum Physics · Physics 2016-11-09 Ludovico Latmiral , Nicolò Spagnolo , Fabio Sciarrino

We present an experimental demonstration of boson sampling as a hardware accelerator for Monte Carlo integration. Our approach leverages importance sampling to factorize an integrand into a distribution that can be sampled using quantum…

Boson sampling is a problem for which quantum devices could prove to go beyond classical computing using only linear optics and photon preparation and counting. While theoretically important, there is a lack of practical applications for…

Quantum Physics · Physics 2019-12-09 Juan Carlos Garcia-Escartin

Analyzing the properties of complex quantum systems is crucial for further development of quantum devices, yet this task is typically challenging and demanding with respect to required amount of measurements. A special attention to this…

Gaussian boson sampling (GBS), a computational problem conjectured to be hard to simulate on a classical machine, has been at the forefront of recent years' experimental and theoretical efforts to demonstrate quantum advantage. The…

Quantum Physics · Physics 2024-02-05 Gabriele Bressanini , Benoit Seron , Leonardo Novo , Nicolas J. Cerf , M. S. Kim