Machine learning for efficient generation of universal hybrid quantum computing resources

Olivier Pfister; Amanuel Anteneh

doi:10.1117/12.3010076

13 March 2024 Machine learning for efficient generation of universal hybrid quantum computing resources

Olivier Pfister, Amanuel Anteneh

Proceedings Volume PC12911, Quantum Computing, Communication, and Simulation IV; PC129110S (2024) https://doi.org/10.1117/12.3010076
Event: SPIE Quantum West, 2024, San Francisco, California, United States

Abstract

We present numerical simulations of deep reinforcement learning on a measurement-based quantum processor--a time-multiplexed optical circuit sampled by photon-number-resolving detection--and find it generates squeezed cat states with an average success rate of 98%, far outperforming all other similar proposals. Since squeezed cat states are deterministic precursors to the Gottesman-Kitaev-Preskill bosonic error code, this is a key result for enabling fault tolerant photonic quantum computing.

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Citation Download Citation

Olivier Pfister and Amanuel Anteneh "Machine learning for efficient generation of universal hybrid quantum computing resources", Proc. SPIE PC12911, Quantum Computing, Communication, and Simulation IV, PC129110S (13 March 2024); https://doi.org/10.1117/12.3010076

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