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13 March 2024 Machine learning for efficient generation of universal hybrid quantum computing resources
Olivier Pfister, Amanuel Anteneh
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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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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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KEYWORDS
Photonic quantum computing
Quantum communications
Quantum machine learning
Quantum resources
Hybrid quantum computing systems
Ocean optics
Optical computing
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