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 …
A Montanaro - Proceedings of the Royal Society A …, 2015 - royalsocietypublishing.org
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 …
R Xia, S Kais - Nature communications, 2018 - nature.com
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 …