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

arXiv: https://arxiv.org/abs/1905.05264 · HTML: https://mlsanity.com/paper/1905.05264/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 May 2019.

## Built on by (most cited first, 2 of 2)

- [Gaussian boson sampling and multi-particle event optimization by machine learning in the quantum phase space](https://mlsanity.com/paper/2102.12142/) (2021)
- [Variational quantum algorithm for Gaussian discrete solitons and their boson sampling](https://mlsanity.com/paper/2110.12379/) (2021)

## Abstract (arXiv)

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.