[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 …

Recent advances for quantum classifiers

W Li, DL Deng - Science China Physics, Mechanics & Astronomy, 2022 - Springer
Abstract Machine learning has achieved dramatic success in a broad spectrum of
applications. Its interplay with quantum physics may lead to unprecedented perspectives for …

Effect of data encoding on the expressive power of variational quantum-machine-learning models

M Schuld, R Sweke, JJ Meyer - Physical Review A, 2021 - APS
Quantum computers can be used for supervised learning by treating parametrized quantum
circuits as models that map data inputs to predictions. While a lot of work has been done to …

Tensorcircuit: a quantum software framework for the nisq era

SX Zhang, J Allcock, ZQ Wan, S Liu, J Sun, H Yu… - Quantum, 2023 - quantum-journal.org
TensorCircuit is an open source quantum circuit simulator based on tensor network
contraction, designed for speed, flexibility and code efficiency. Written purely in Python, and …

Reinforcement learning for optimization of variational quantum circuit architectures

M Ostaszewski, LM Trenkwalder… - Advances in …, 2021 - proceedings.neurips.cc
Abstract The study of Variational Quantum Eigensolvers (VQEs) has been in the spotlight in
recent times as they may lead to real-world applications of near-term quantum devices …

Filtering variational quantum algorithms for combinatorial optimization

D Amaro, C Modica, M Rosenkranz… - Quantum Science …, 2022 - iopscience.iop.org
Current gate-based quantum computers have the potential to provide a computational
advantage if algorithms use quantum hardware efficiently. To make combinatorial …