Practical challenges in simulating quantum systems on classical computers have been
widely recognized in the quantum physics and quantum chemistry communities over the …

Machine learning algorithms learn a desired input-output relation from examples in order to
interpret new inputs. This is important for tasks such as image and speech recognition or …

One of the key applications for quantum computers will be the simulation of other quantum
systems that arise in chemistry, materials science, etc., in order to accelerate the process of …

Solving linear systems of equations is a common problem that arises both on its own and as
a subroutine in more complex problems: given a matrix A and a vector b→, find a vector x→ …

We discuss how quantum computation can be applied to financial problems, providing an
overview of current approaches and potential prospects. We review quantum optimization …

An n-qubit quantum circuit performs a unitary operation on an exponentially large, 2 n-
dimensional, Hilbert space, which is a major source of quantum speed-ups. We develop a …

This article outlines our point of view regarding the applicability, state-of-the-art, and
potential of quantum computing for problems in finance. We provide an introduction to …

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

Abstract Machine learning algorithms based on parametrized quantum circuits are prime
candidates for near-term applications on noisy quantum computers. In this direction, various …

Quantum computers are designed to outperform standard computers by running quantum
algorithms. Areas in which quantum algorithms can be applied include cryptography, search …