Gaussian Boson Sampling (GBS) is a non-universal model for quantum computing inspired by the original formulation of the Boson Sampling (BS) problem. Nowadays, it represents a paradigmatic quantum platform to reach the quantum advantage regime in a specific computational model. Indeed, thanks to the implementation in photonics-based processors, the latest GBS experiments have reached a level of complexity where the quantum apparatus has solved the task faster than currently up-to-date classical strategies. In addition, recent studies have identified possible applications beyond the inherent sampling task. In particular, a direct connection between photon counting of a genuine GBS device and the number of perfect matchings in a graph has been established. In this work, we propose to exploit such a connection to benchmark GBS experiments. We interpret the properties of the feature vectors of the graph encoded in the device as a signature of correct sampling from the true input state. Within this framework, two approaches are presented. The first method exploits the distributions of graph feature vectors and classification via neural networks. The second approach investigates the distributions of graph kernels. Our results provide a novel approach to the actual need for tailored algorithms to benchmark large-scale Gaussian Boson Samplers.

Certification of Gaussian Boson Sampling via graphs feature vectors and kernels / Giordani, Taira; Mannucci, Valerio; Spagnolo, Nicolo'; Fumero, Marco; Rampini, Arianna; Rodola', Emanuele; Sciarrino, Fabio. - In: QUANTUM SCIENCE AND TECHNOLOGY. - ISSN 2058-9565. - 8:1(2023). [10.1088/2058-9565/ac969b]

Certification of Gaussian Boson Sampling via graphs feature vectors and kernels

Taira Giordani;Nicolo Spagnolo;Marco Fumero;Arianna Rampini;Emanuele Rodola;Fabio Sciarrino
2023

Abstract

Gaussian Boson Sampling (GBS) is a non-universal model for quantum computing inspired by the original formulation of the Boson Sampling (BS) problem. Nowadays, it represents a paradigmatic quantum platform to reach the quantum advantage regime in a specific computational model. Indeed, thanks to the implementation in photonics-based processors, the latest GBS experiments have reached a level of complexity where the quantum apparatus has solved the task faster than currently up-to-date classical strategies. In addition, recent studies have identified possible applications beyond the inherent sampling task. In particular, a direct connection between photon counting of a genuine GBS device and the number of perfect matchings in a graph has been established. In this work, we propose to exploit such a connection to benchmark GBS experiments. We interpret the properties of the feature vectors of the graph encoded in the device as a signature of correct sampling from the true input state. Within this framework, two approaches are presented. The first method exploits the distributions of graph feature vectors and classification via neural networks. The second approach investigates the distributions of graph kernels. Our results provide a novel approach to the actual need for tailored algorithms to benchmark large-scale Gaussian Boson Samplers.
2023
Gaussian Boson Sampling; graph theory; quantum information; validation methods
01 Pubblicazione su rivista::01a Articolo in rivista
Certification of Gaussian Boson Sampling via graphs feature vectors and kernels / Giordani, Taira; Mannucci, Valerio; Spagnolo, Nicolo'; Fumero, Marco; Rampini, Arianna; Rodola', Emanuele; Sciarrino, Fabio. - In: QUANTUM SCIENCE AND TECHNOLOGY. - ISSN 2058-9565. - 8:1(2023). [10.1088/2058-9565/ac969b]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1662287
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