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Machine learning for efficient generation of universal hybrid quantum computing resources

Amanuel Anteneh, Olivier Pfister

Published Jan 1, 2024• 0 citations
Publisher

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%, outperforming all other similar proposals.
DOI
10.1364/cleo_fs.2024.ff1h.6

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Cubic-phase states are a sufficient gate resource for universal quantum computing over continuous variables. We present results from numerical experiments in which deep neural networks are trained via reinforcement learning to control a quantum optical circuit for generating cubic-phase states by leveraging quantum interference in phase space, with an unprecedented average success rate of 96 % . The only non-Gaussian resource required is photon-number-resolving measurements. We also show that the exact same resources enable the direct generation of a quartic-phase gate, with no need for a cubic gate decomposition.
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