Programming multi-level quantum gates in disordered computing reservoirs via machine learning. (arXiv:1905.05264v2 [quant-ph] UPDATED)

Novel computational tools in machine learning open new perspectives in
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 multimodal fiber. We show that by using trainable
operators at the input and at the readout, it is possible to realize
multi-level gates. We study single and qudit gates, including the scaling
properties of the algorithms with the size of the reservoir.

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