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

As we are approaching actual application of quantum technology it is important to exploit the current quantum resources 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 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 with the use of single-qubit gates can work as a controlled-not-swap (\cns) gate. We then show how this \cns gate can be used to 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.