Supervised quantum gate "teaching" for quantum hardware design

TL;DR

This paper introduces a supervised learning approach to train quantum networks of interacting qubits, enabling the implementation of target algorithms with minimal classical control, aiding quantum hardware design.

Contribution

It presents a novel supervised training method for quantum networks to facilitate quantum hardware development with reduced classical intervention.

Findings

Successfully trains quantum networks to perform specific algorithms

Reduces reliance on external classical control in quantum hardware

Provides a framework for designing more autonomous quantum computers

Abstract

We show how to train a quantum network of pairwise interacting qubits such that its evolution implements a target quantum algorithm into a given network subset. Our strategy is inspired by supervised learning and is designed to help the physical construction of a quantum computer which operates with minimal external classical control.