Quantum machine learning is a rapidly growing field at the intersection of quantum
technology and artificial intelligence. This review provides a two-fold overview of several key …

Superconducting qubits are leading candidates in the race to build a quantum computer
capable of realizing computations beyond the reach of modern supercomputers. The …

Hybrid quantum–classical systems make it possible to utilize existing quantum computers to
their fullest extent. Within this framework, parameterized quantum circuits can be regarded …

The aim of this review is to provide quantum engineers with an introductory guide to the
central concepts and challenges in the rapidly accelerating field of superconducting …

Over the last two decades, tremendous advances have been made for constructing large-
scale quantum computers. In particular, quantum computing platforms based on …

The power of quantum computing technologies is based on the fundamentals of quantum
mechanics, such as quantum superposition, quantum entanglement, or the no-cloning …

We introduce a quantum neural network, QNN, that can represent labeled data, classical or
quantum, and be trained by supervised learning. The quantum circuit consists of a sequence …

Quantum information technologies, on the one hand, and intelligent learning systems, on the
other, are both emergent technologies that are likely to have a transformative impact on our …

Abstract Machine learning algorithms are powerful tools for data-driven tasks such as image
classification and feature detection. However, their vulnerability to adversarial examples …

The great promise of quantum computers comes with the dual challenges of building them
and finding their useful applications. We argue that these two challenges should be …