Photonic quantum extreme reservoir computation

Kae Nemoto; Akitada Sakurai; Aoi Hayashi; William Munro

doi:10.1117/12.3027415

4 October 2024 Photonic quantum extreme reservoir computation

Kae Nemoto, Akitada Sakurai, Aoi Hayashi, William Munro

Author Affiliations +

Proceedings Volume PC13148, Quantum Communications and Quantum Imaging XXII; PC1314807 (2024) https://doi.org/10.1117/12.3027415
Event: Optical Engineering + Applications, 2024, San Diego, California, United States

Abstract

Quantum circuits based on single photons and linear optical elements (PhQC) are an ideal candidate to enable quantum information processing at room temperature. Although PhQC cannot achieve universality without post-selection, boson sampling (a well-known sampling problem) has a non-trivial computational complexity related to #P-hard. The key question now is how to utilize that complexity for practical problems? Here we present a new hybrid quantum / classical neural network model for image reorganization that achieves a 96.6% testing accuracy only 4 photons and 16 modes without PhQC optimization. We also show it outperforms the situation where coherent light sources are used.

Conference Presentation

Citation Download Citation

Kae Nemoto, Akitada Sakurai, Aoi Hayashi, and William Munro "Photonic quantum extreme reservoir computation", Proc. SPIE PC13148, Quantum Communications and Quantum Imaging XXII, PC1314807 (4 October 2024); https://doi.org/10.1117/12.3027415

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