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

The difficulty of simulating quantum systems, well known to quantum chemists, prompted the
idea of quantum computation. One can avoid the steep scaling associated with the exact …

Implementing a gate-based quantum algorithm on an noisy intermediate scale quantum
(NISQ) device has several challenges that arise from the fact that such devices are noisy …

Quantum machine learning is one of the most promising applications of quantum computing
in the noisy intermediate-scale quantum (NISQ) era. We propose a quantum convolutional …

Variational quantum circuits are becoming tools of choice in quantum optimization and
machine learning. In this paper we investigate a class of variational circuits for the purposes …

Artificial neural networks are the heart of machine learning algorithms and artificial
intelligence. Historically, the simplest implementation of an artificial neuron traces back to …

Machine-learning tasks frequently involve problems of manipulating and classifying large
numbers of vectors in high-dimensional spaces. Classical algorithms for solving such …

Lately, much attention has been given to quantum algorithms that solve pattern recognition
tasks in machine learning. Many of these quantum machine learning algorithms try to …

A hybrid quantum–classical approach to model continuous classical probability distributions
using a variational quantum circuit is proposed. The architecture of this quantum generator …

We describe a quantum algorithm that generalizes the quantum linear system algorithm
[Harrow et al., Phys. Rev. Lett. 103, 150502 (2009)] to arbitrary problem specifications. We …