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Novel machine learning computational tools open new perspectives for quantuminformation systems. Here we adopt the open-source programming libraryTensorFlow to design multi-level quantum gates including a computing reservoirrepresented by a random unitary matrix. In optics, the reservoir is adisordered medium or a multi-modal fiber. We show that trainable operators atthe input and the readout enable one to realize multi-level gates. We studyvarious qudit gates, including the scaling properties of the algorithms withthe size of the reservoir. Despite an initial low slop learning stage,TensorFlow turns out to be an extremely versatile resource for designing gateswith complex media, including different models that use spatial lightmodulators with quantized modulation levels.

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