Programming multi-level quantum gates in disordered computing reservoirs via machine learning and TensorFlow
This work addresses the challenge of programming quantum gates in disordered systems for quantum information processing, representing an incremental advance in applying machine learning tools to quantum computing.
The researchers tackled the problem of designing multi-level quantum gates using disordered computing reservoirs by employing TensorFlow for machine learning, achieving the realization of various qudit gates and analyzing algorithm scaling with reservoir size.
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.