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

E. Bahnsen; S. E. Rasmussen; N. J.S. Loft; N. T. Zinner

doi:10.1103/PhysRevApplied.17.024053

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

E. Bahnsen, S. E. Rasmussen, N. J.S. Loft, N. T. Zinner^*

^*Corresponding author for this work

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

5 Citations (Scopus)

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 by classical logic gates, while 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 nonlinear functions and classify two-dimensional data.

Original language	English
Article number	024053
Journal	Physical Review Applied
Volume	17
Issue	2
ISSN	2331-7019
DOIs	https://doi.org/10.1103/PhysRevApplied.17.024053
Publication status	Published - Feb 2022

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10.1103/PhysRevApplied.17.024053

https://arxiv.org/abs/2103.08605

Cite this

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title = "Application of the Diamond Gate in Quantum Fourier Transformations and Quantum Machine Learning",

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 by classical logic gates, while 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 nonlinear functions and classify two-dimensional data.",

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AU - Bahnsen, E.

AU - Rasmussen, S. E.

AU - Loft, N. J.S.

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

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