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 …

Quantum computers are expected to surpass the computational capabilities of classical
computers and have a transformative impact on numerous industry sectors. We present a …

Variational quantum algorithms (VQAs) optimize the parameters θ of a parametrized
quantum circuit V (θ) to minimize a cost function C. While VQAs may enable practical …

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

Abstract Variational Quantum Algorithms (VQAs) have received considerable attention due
to their potential for achieving near-term quantum advantage. However, more work is …

Previously proposed quantum algorithms for solving linear systems of equations cannot be
implemented in the near term due to the required circuit depth. Here, we propose a hybrid …

Quantum computers are expected to surpass the computational capabilities of classical
computers during this decade and have transformative impact on numerous industry sectors …

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

Data representation is crucial for the success of machine-learning models. In the context of
quantum machine learning with near-term quantum computers, equally important …

In this short review article, we aim to provide physicists not working within the quantum
computing community a hopefully easy-to-read introduction to the state of the art in the field …