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

## TL;DR

This paper demonstrates how TensorFlow can be used to design multi-level quantum gates in disordered optical media, leveraging machine learning to control complex quantum systems.

## Contribution

It introduces a novel approach to programming multi-level quantum gates in disordered reservoirs using TensorFlow and machine learning techniques.

## Key findings

- TensorFlow effectively trains quantum gates in disordered media.
- The method scales with reservoir size, showing promising results.
- Versatile for different optical media and modulation levels.

## 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.