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Experimental statistical signature of many-body quantum interference
by
Pompili, Matteo
, Wiebe, Nathan
, Sciarrino, Fabio
, Spagnolo, Nicolò
, Buchleitner, Andreas
, Osellame, Roberto
, Crespi, Andrea
, Flamini, Fulvio
, Viggianiello, Niko
, Giordani, Taira
, Walschaers, Mattia
in
Computer applications
/ Data processing
/ Information processing
/ Interference
/ Learning algorithms
/ Machine learning
/ Photonics
/ Quantum computing
/ Quantum mechanics
/ Quantum phenomena
/ Quantum theory
/ Signatures
/ Statistics
2018
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Experimental statistical signature of many-body quantum interference
by
Pompili, Matteo
, Wiebe, Nathan
, Sciarrino, Fabio
, Spagnolo, Nicolò
, Buchleitner, Andreas
, Osellame, Roberto
, Crespi, Andrea
, Flamini, Fulvio
, Viggianiello, Niko
, Giordani, Taira
, Walschaers, Mattia
in
Computer applications
/ Data processing
/ Information processing
/ Interference
/ Learning algorithms
/ Machine learning
/ Photonics
/ Quantum computing
/ Quantum mechanics
/ Quantum phenomena
/ Quantum theory
/ Signatures
/ Statistics
2018
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While trying to remove the title from your shelf something went wrong :( Kindly try again later!
Do you wish to request the book?
Experimental statistical signature of many-body quantum interference
by
Pompili, Matteo
, Wiebe, Nathan
, Sciarrino, Fabio
, Spagnolo, Nicolò
, Buchleitner, Andreas
, Osellame, Roberto
, Crespi, Andrea
, Flamini, Fulvio
, Viggianiello, Niko
, Giordani, Taira
, Walschaers, Mattia
in
Computer applications
/ Data processing
/ Information processing
/ Interference
/ Learning algorithms
/ Machine learning
/ Photonics
/ Quantum computing
/ Quantum mechanics
/ Quantum phenomena
/ Quantum theory
/ Signatures
/ Statistics
2018
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Experimental statistical signature of many-body quantum interference
Journal Article
Experimental statistical signature of many-body quantum interference
2018
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Overview
Multi-particle interference is an essential ingredient for fundamental quantum mechanics phenomena and for quantum information processing to provide a computational advantage, as recently emphasized by boson sampling experiments. Hence, developing a reliable and efficient technique to witness its presence is pivotal in achieving the practical implementation of quantum technologies. Here, we experimentally identify genuine many-body quantum interference via a recent efficient protocol, which exploits statistical signatures at the output of a multimode quantum device. We successfully apply the test to validate three-photon experiments in an integrated photonic circuit, providing an extensive analysis on the resources required to perform it. Moreover, drawing upon established techniques of machine learning, we show how such tools help to identify the—a priori unknown—optimal features to witness these signatures. Our results provide evidence on the efficacy and feasibility of the method, paving the way for its adoption in large-scale implementations.
Publisher
Nature Publishing Group
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