Testing of on-cloud Gaussian Boson Sampler "Borealis'' via graph theory
- URL: http://arxiv.org/abs/2306.12120v1
- Date: Wed, 21 Jun 2023 09:02:55 GMT
- Title: Testing of on-cloud Gaussian Boson Sampler "Borealis'' via graph theory
- Authors: Denis Stanev, Taira Giordani, Nicol\`o Spagnolo, Fabio Sciarrino
- Abstract summary: 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.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Quantum photonic processors are emerging as promising platforms to prove
preliminary evidence of quantum computational advantage towards the realization
of universal quantum computers. In the context of non-universal noisy
intermediate quantum devices, photonic-based sampling machines solving the
Gaussian Boson Sampling problem currently play a central role in the
experimental demonstration of a quantum computational advantage. In particular,
the recently developed photonic machine Borealis, a large-scale instance of a
programmable Gaussian Boson Sampling device encoded in the temporal modes of
single photons, is available online for external users. In this work, we test
the performances of Borealis as a sampling machine and its possible use cases
in graph theory. We focused on the validation problem of the sampling process
in the presence of experimental noise, such as photon losses, that could
undermine the hardness of simulating the experiment. To this end, we used a
recent protocol that exploits the connection between Guassian Boson Sampling
and graphs perfect match counting. Such an approach to validation also provides
connections with the open question on the effective advantage in using noisy
Gaussian Boson Sampling devices for graphs similarity and isomorphism problems.
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