Quantum Computation with Machine-Learning-Controlled Quantum Stuff

TL;DR

This paper proposes a method to perform universal quantum computation on arbitrary physical systems by using machine learning to identify the settings that implement quantum gates, enabling programmable quantum processing with diverse materials.

Contribution

It introduces a machine learning approach to tomographically learn and control arbitrary quantum systems for universal quantum computation.

Findings

Machine learning can identify settings for universal quantum gates.

Arbitrary quantum programs can be implemented on diverse physical systems.

The method enables programmable quantum computation with 'quantum stuff'.

Abstract

We describe how one may go about performing quantum computation with arbitrary "quantum stuff", as long as it has some basic physical properties. Imagine a long strip of stuff, equipped with regularly spaced wires to provide input settings and to read off outcomes. After showing how the corresponding map from settings to outcomes can be construed as a quantum circuit, we provide a machine learning algorithm to tomographically "learn" which settings implement the members of a universal gate set. At optimum, arbitrary quantum gates, and thus arbitrary quantum programs, can be implemented using the stuff.