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Superconducting Qubits Quantum Networks Quantum Machine Learning

Design and Benchmarking of a Quantum Photonic Chip

arXiv
Authors: Gabriele De Angelis, Nicolò Leone, Alessandro Luongo, Alberto Montanaro, Matteo Sanna, Roberto Siagri, Vito Sorianello, Luigi Tallone, Fabrizio Tamburini, Marco Venere

Year

2026

Paper ID

72308

Status

Preprint

Abstract Read

~2 min

Abstract Words

97

Citations

N/A

Abstract

We present the design and benchmarking of RP000, a quantum photonic processor capable of encoding a quantum system in the degrees of freedom of single photons, based on standard CMOS-compatible manufacturing processes, and working at room temperature. We benchmark it against machine learning tasks, evaluating three quantum-classical architectures of increasing complexity. Our experimental results and simulations show that RP000 achieves higher accuracy than classical networks of comparable size in multiple use cases. Compared to a superconducting quantum processor, RP000 exhibits superior noise tolerance. These findings demonstrate that RP000 can provide a scalable route toward efficient quantum applications.

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  • This paper contributes to the Quantum Networks research area in the Quantum Articles archive.
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  • We present the design and benchmarking of RP000, a quantum photonic processor capable of encoding a quantum system in the degrees of freedom of single photons, based on...

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2607.06488 [2] arXiv https://arxiv.org/abs/2607.06488 [3] Publisher https://arxiv.org/abs/2607.06488

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