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

Recently, increased computational power and data availability, as well as algorithmic
advances, have led machine learning (ML) techniques to impressive results in regression …

This work presents a quantum algorithm for the Monte Carlo pricing of financial derivatives.
We show how the relevant probability distributions can be prepared in quantum …

Monte Carlo methods use random sampling to estimate numerical quantities which are hard
to compute deterministically. One important example is the use in statistical physics of …

Considering recent advancements and successes in the development of efficient quantum
algorithms for electronic structure calculations—alongside impressive results using machine …

Over the last few decades, quantum chemistry has progressed through the development of
computational methods based on modern digital computers. However, these methods can …

We present a quantum algorithm to prepare the thermal Gibbs state of interacting quantum
systems. This algorithm sets a universal upper bound D α on the thermalization time of a …

Quantum computers can be used to simulate nonlinear non-Hamiltonian classical dynamics
on phase space by using the generalized Koopman–von Neumann formulation of classical …

Given a convex function $ f\colon\mathbb {R}^{d}\to\mathbb {R} $, the problem of sampling
from a distribution $\propto e^{-f (x)} $ is called log-concave sampling. This task has wide …

Electric power systems provide the backbone of modern industrial societies. Enabling
scalable grid analytics is the keystone to successfully operating large transmission and …