Training microwave pulses using quantum machine learning

TL;DR

This paper demonstrates how quantum machine learning can optimize microwave pulses to replace multiple single-qubit gates, potentially reducing circuit complexity and noise in quantum computing.

Contribution

It introduces a method to use quantum machine learning for condensing multiple quantum gates into a single microwave pulse, enhancing efficiency in quantum circuit implementation.

Findings

Machine learning can learn parameters for a single pulse replacing multiple gates.

Potential to reduce the number of single-qubit operations by about one-third.

Improves quantum circuit efficiency and reduces noise and decoherence.

Abstract

A gate sequence of single-qubit transformations may be condensed into a single microwave pulse that maps a qubit from an initialized state directly into the desired state of the composite transformation. Here, machine learning is used to learn the parameterized values for a single driving pulse associated with a transformation of three sequential gate operations on a qubit. This implies that future quantum circuits may contain roughly a third of the number of single-qubit operations performed, greatly reducing the problems of noise and decoherence. There is a potential for even greater condensation and efficiency using the methods of quantum machine learning.