TY - JOUR
T1 - Application of the Diamond Gate in Quantum Fourier Transformations and Quantum Machine Learning
AU - Bahnsen, E.
AU - Rasmussen, S. E.
AU - Loft, N. J.S.
AU - Zinner, N. T.
N1 - Publisher Copyright:
© 2022 American Physical Society.
PY - 2022/2
Y1 - 2022/2
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85126092321&partnerID=8YFLogxK
U2 - 10.1103/PhysRevApplied.17.024053
DO - 10.1103/PhysRevApplied.17.024053
M3 - Journal article
AN - SCOPUS:85126092321
SN - 2331-7019
VL - 17
JO - Physical Review Applied
JF - Physical Review Applied
IS - 2
M1 - 024053
ER -