Programming multi-level quantum gates in disordered computing reservoirs via machine learning

doi:10.1364/OE.389432

Programming multi-level quantum gates in disordered computing reservoirs via machine learning

2020-04-24

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AI

DOI：10.1364/OE.389432

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Novel machine learning computational tools open new perspectives for quantum information systems. Here we adopt the open-source programming library TensorFlow to design multi-level quantum gates, including a computing reservoir represented by a random unitary matrix. In optics, the reservoir is a disordered medium or a multi-modal fiber. We show that trainable operators at the input and the readout enable one to realize multi-level gates. We study various qudit gates, including the scaling properties of the algorithms with the size of the reservoir. Despite an initial low slop learning stage, TensorFlow turns out to be an extremely versatile resource for designing gates with complex media, including different models that use spatial light modulators with quantized modulation levels. (C) 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Keywords:

NEURAL-NETWORKS

SCATTERING

LIGHT

Programming multi-level quantum gates in disordered computing reservoirs via machine learning

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