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

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

As we begin to reach the limits of classical computing, quantum computing has emerged as
a technology that has captured the imagination of the scientific world. While for many years …

The accurate computation of Hamiltonian ground, excited and thermal states on quantum
computers stands to impact many problems in the physical and computer sciences, from …

We present the problem of approximating the time-evolution operator $ e^{-i\hat {H} t} $ to
error $\epsilon $, where the Hamiltonian $\hat {H}=(\langle G|\otimes\hat {\mathcal {I}})\hat …

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 …

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

Computational models are an essential tool for the design, characterization, and discovery
of novel materials. Computationally hard tasks in materials science stretch the limits of …

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 …