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Experimental statistical signature of many-body quantum interference


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.

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This work was supported by European Research Council (ERC) Starting Grant 3DQUEST (3D-Quantum Integrated Optical Simulation, grant agreement no. 307783;, and by H2020-FETPROACT-2014 Grant QUCHIP (Quantum Simulation on a Photonic Chip, grant agreement no. 641039; A.B. acknowledges financial support through EU Collaborative project QuProCS (Quantum Probes for Complex Systems, grant agreement no. 641277). M.W. acknowledges financial support from European Union Grant QCUMbER (Quantum Controlled Ultrafast Multimode Entanglement and Measurement, grant agreement no. 665148;

Author information

T.G., F.F., M.P., N.V., N.S. and F.S. devised and carried out the quantum experiment with single photons. A.C. and R.O. fabricated and characterized the integrated photonic circuit with classical light. T.G., F.F., M.P., N.S., M.W., A.B. and F.S. carried out analysis of the experimental data. F.F., M.P., T.G., N.S., N.W. and F.S. carried out the analysis with machine learning algorithms. All authors discussed the implementation, the experimental data and the results from the analysis with machine learning techniques. All authors contributed to writing the paper.

Competing interests

The authors declare no competing financial interests.

Correspondence to Fabio Sciarrino.

Supplementary information

  1. Supplementary Information

    Supplementary Notes 1–6; Supplementary Figures 1–7.

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Fig. 1: Scheme of the apparatus.
Fig. 2: Experimental output data samples for indistinguishable particles.
Fig. 3: Assessment of multi-particle interference.
Fig. 4: Dependency of discrimination on output subsets and sample size.
Fig. 5: Importance of summary statistics for classification.