The variational quantum eigensolver (or VQE), first developed by Peruzzo et al.(2014), has
received significant attention from the research community in recent years. It uses the …

Applications such as simulating complicated quantum systems or solving large-scale linear
algebra problems are very challenging for classical computers, owing to the extremely high …

Computational chemistry is an essential tool in the pharmaceutical industry. Quantum
computing is a fast evolving technology that promises to completely shift the computational …

We study the performance of classical and quantum machine learning (ML) models in
predicting outcomes of physical experiments. The experiments depend on an input …

A quantum generalization of Natural Gradient Descent is presented as part of a general-
purpose optimization framework for variational quantum circuits. The optimization dynamics …

Suppressing noise in physical systems is of fundamental importance. As quantum
computers mature, quantum error correcting codes (QECs) will be adopted in order to …

We consider the problem of jointly estimating expectation values of many Pauli observables,
a crucial subroutine in variational quantum algorithms. Starting with randomized …

An important measure of the development of quantum computing platforms has been the
simulation of increasingly complex physical systems. Before fault-tolerant quantum …

We study the power of quantum memory for learning properties of quantum systems and
dynamics, which is of great importance in physics and chemistry. Many state-of-the-art …

We propose a tomographic protocol for estimating any k-body reduced density matrix (k-
RDM) of an n-mode fermionic state, a ubiquitous step in near-term quantum algorithms for …