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

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.

Automated summary

As the application of quantum technology approaches, leveraging current quantum resources becomes crucial. Utilizing the diamond gate instead of standard gates has shown to be more efficient in compiling quantum algorithms. These gates can be decomposed into standard gates and have a wide range of applications in quantum machine learning.