Machine learning for efficient generation of universal photonic quantum computing resources

Olivier Pfister; Amanuel Anteneh; Léandre Brunel

doi:10.1117/12.3030556

4 October 2024 Machine learning for efficient generation of universal photonic quantum computing resources

Olivier Pfister, Amanuel Anteneh, Léandre Brunel

Proceedings Volume PC13148, Quantum Communications and Quantum Imaging XXII; PC131480A (2024) https://doi.org/10.1117/12.3030556
Event: Optical Engineering + Applications, 2024, San Diego, California, United States

Abstract

We present numerical results from simulations using deep reinforcement learning to control a measurement-based quantum processor—a time-multiplexed optical circuit sampled by photon- number-resolving detection—and find it generates squeezed cat states quasi-deterministically, with an average success rate of 98%, far outperforming all other proposals. Since squeezed cat states are deterministic precursors to the Gottesman-Kitaev-Preskill (GKP) bosonic error code, this is a key result for enabling fault tolerant photonic quantum computing. Informed by these simulations, we also discovered a one-step quantum circuit of constant parameters that can generate GKP states with high probability, though not deterministically.

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Olivier Pfister, Amanuel Anteneh, and Léandre Brunel "Machine learning for efficient generation of universal photonic quantum computing resources", Proc. SPIE PC13148, Quantum Communications and Quantum Imaging XXII, PC131480A (4 October 2024); https://doi.org/10.1117/12.3030556

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KEYWORDS

Photonic quantum computing

Quantum resources

Quantum communications

Quantum machine learning

Quantum nongaussianity

Quantum computing

Quantum information

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