[HTML][HTML] The variational quantum eigensolver: a review of methods and best practices

J Tilly, H Chen, S Cao, D Picozzi, K Setia, Y Li, E Grant… - Physics Reports, 2022 - Elsevier
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 …

Quantum machine learning: from physics to software engineering

A Melnikov, M Kordzanganeh, A Alodjants… - Advances in Physics …, 2023 - Taylor & Francis
Quantum machine learning is a rapidly growing field at the intersection of quantum
technology and artificial intelligence. This review provides a two-fold overview of several key …

Continuous-variable quantum neural networks

N Killoran, TR Bromley, JM Arrazola, M Schuld… - Physical Review …, 2019 - APS
We introduce a general method for building neural networks on quantum computers. The
quantum neural network is a variational quantum circuit built in the continuous-variable (CV) …

Solving nonlinear differential equations with differentiable quantum circuits

O Kyriienko, AE Paine, VE Elfving - Physical Review A, 2021 - APS
We propose a quantum algorithm to solve systems of nonlinear differential equations. Using
a quantum feature map encoding, we define functions as expectation values of parametrized …

Recent advances for quantum neural networks in generative learning

J Tian, X Sun, Y Du, S Zhao, Q Liu… - … on Pattern Analysis …, 2023 - ieeexplore.ieee.org
Quantum computers are next-generation devices that hold promise to perform calculations
beyond the reach of classical computers. A leading method towards achieving this goal is …