2021/03/31 by E. Bahnsen, S. E. Rasmussen, N. J. S. Loft +2
Computer Science · Physics and Astronomy · #Composite material #Computer science #Diamond #Fourier transform #Materials science #Physics #Quantum #Quantum Computing Algorithms and Architecture #Quantum Fourier transform #Quantum Information and Cryptography #Quantum algorithm #Quantum and electron transport phenomena #Quantum gate #Quantum mechanics #quant-ph
paper · pdf · doi:10.1103/physrevapplied.17.024053
published as Phys. Rev. Applied 17, 024053 (2022) · 12 pages, 10 figures + Appendix: 2 pages, 2 figures
openalex publication_date 2022/02/18 · arxiv created 2022/03/09 · arxiv updated 2022/03/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.