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 …

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 …

A basic idea of quantum computing is surprisingly similar to that of kernel methods in
machine learning, namely, to efficiently perform computations in an intractably large Hilbert …

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 …

Fuelled by increasing computer power and algorithmic advances, machine learning
techniques have become powerful tools for finding patterns in data. Quantum systems …

Quantum annealing algorithms belong to the class of metaheuristic tools, applicable for
solving binary optimization problems. Hardware implementations of quantum annealing …

Adiabatic quantum computing (AQC) started as an approach to solving optimization
problems and has evolved into an important universal alternative to the standard circuit …

We give an algorithm for prediction on a quantum computer which is based on a linear
regression model with least-squares optimization. In contrast to related previous …

An increase in the efficiency of sampling from Boltzmann distributions would have a
significant impact on deep learning and other machine-learning applications. Recently …

We explore the potential of D-Wave's quantum annealers for computing some of the basic
components required for quantum simulations of standard model physics. By implementing …