Applications such as simulating complicated quantum systems or solving large-scale linear
algebra problems are very challenging for classical computers, owing to the extremely high …

A universal fault-tolerant quantum computer that can efficiently solve problems such as
integer factorization and unstructured database search requires millions of qubits with low …

Scaling up to a large number of qubits with high-precision control is essential in the
demonstrations of quantum computational advantage to exponentially outpace the classical …

At the intersection of machine learning and quantum computing, quantum machine learning
has the potential of accelerating data analysis, especially for quantum data, with …

It is unknown whether near-term quantum computers are advantageous for machine
learning tasks. In this work we address this question by trying to understand how powerful …

The use of quantum computing for machine learning is among the most exciting prospective
applications of quantum technologies. However, machine learning tasks where data is …

Quantum-mechanical effects at the macroscopic level were first explored in Josephson-
junction-based superconducting circuits in the 1980s. In recent decades, the emergence of …

Deep-learning models have become pervasive tools in science and engineering. However,
their energy requirements now increasingly limit their scalability. Deep-learning …

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 …

Programmable quantum simulators and quantum computers are opening unprecedented
opportunities for exploring and exploiting the properties of highly entangled complex …