Application of the Diamond Gate in Quantum Fourier Transformations and Quantum Machine Learning

TL;DR

This paper explores the use of the native diamond gate in superconducting quantum circuits to efficiently implement quantum Fourier transforms and machine learning tasks, potentially improving quantum algorithm performance.

Contribution

It demonstrates how the diamond gate can be decomposed into standard gates and utilized for quantum Fourier transforms and machine learning, offering a novel approach with native hardware compatibility.

Findings

Diamond gate can be decomposed into standard gates.

Diamond gate enables efficient quantum Fourier transform implementation.

Application of diamond gate in quantum machine learning tasks.

Abstract

As we are approaching actual application of quantum technology, it is essential to exploit the current quantum resources in the best possible way. With this in mind, it might not be beneficial to use the usual standard gate sets, inspired from classical logic gates, when compiling quantum algorithms when other less standardized gates currently perform better. We, therefore, consider a promising native gate, which occurs naturally in superconducting circuits, known as the diamond gate. We show how the diamond gate can be decomposed into standard gates and, using single-qubit gates, can work as a controlled-not-swap (CNS) gate. We then show how this CNS gate can create a controlled phase gate. Controlled phase gates are the backbone of the quantum Fourier transform algorithm, and we, therefore, show how to use the diamond gate to perform this algorithm. We also show how to use the diamond gate in quantum machine learning; namely, we use it to approximate non-linear functions and classify two-dimensional data.