Abstract
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.
Highlights
The development of multi-level quantum information processing systems has steadily grown over the past few years, with experimental realizations of multi-level, or qudit logic gates for several widely used photonic degrees of freedom, such as orbital-angular-momentum and path encoding [1,2,3,4]
Such protocols may be realized by using the photonic spatial degrees of freedom as the encoding basis, and suitable unitary operators to switch between bases mutually unbiased with respect to the computational basis
We show in the following the way these two problems can be solved by artificial neural network (ANN), where we denote the two families as non-inferencing and inferencing gates
Summary
The development of multi-level quantum information processing systems has steadily grown over the past few years, with experimental realizations of multi-level, or qudit logic gates for several widely used photonic degrees of freedom, such as orbital-angular-momentum and path encoding [1,2,3,4]. Recent work outlined the relevance of a large class of devices, commonly denoted as “complex” or “multi-mode” [5,6] In these systems, many modes or channels are mixed and controlled at input and readout to realize a target input-output operation. High-dimensional QKD offers an increased information capacity as well as an increased robustness to noise over qubit-based protocols [36,37] Such protocols may be realized by using the photonic spatial degrees of freedom as the encoding (computational) basis, and suitable unitary operators to switch between bases mutually unbiased with respect to the computational basis. Our goal is to use the random medium to perform a given operation denoted by a gate unitary matrix. We show in the following the way these two problems can be solved by ANNs, where we denote the two families as non-inferencing and inferencing gates
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