[HTML][HTML] The variational quantum eigensolver: a review of methods and best practices
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 …
received significant attention from the research community in recent years. It uses the …
Near-term quantum computing techniques: Variational quantum algorithms, error mitigation, circuit compilation, benchmarking and classical simulation
Quantum computing is a game-changing technology for global academia, research centers
and industries including computational science, mathematics, finance, pharmaceutical …
and industries including computational science, mathematics, finance, pharmaceutical …
Training variational quantum algorithms is NP-hard
L Bittel, M Kliesch - Physical review letters, 2021 - APS
Variational quantum algorithms are proposed to solve relevant computational problems on
near term quantum devices. Popular versions are variational quantum eigensolvers and …
near term quantum devices. Popular versions are variational quantum eigensolvers and …
Exploiting symmetry in variational quantum machine learning
Variational quantum machine learning is an extensively studied application of near-term
quantum computers. The success of variational quantum learning models crucially depends …
quantum computers. The success of variational quantum learning models crucially depends …
Theory of overparametrization in quantum neural networks
The prospect of achieving quantum advantage with quantum neural networks (QNNs) is
exciting. Understanding how QNN properties (for example, the number of parameters M) …
exciting. Understanding how QNN properties (for example, the number of parameters M) …
Tensorflow quantum: A software framework for quantum machine learning
M Broughton, G Verdon, T McCourt, AJ Martinez… - ar** of
hybrid quantum-classical models for classical or quantum data. This framework offers high …
hybrid quantum-classical models for classical or quantum data. This framework offers high …
Exploring entanglement and optimization within the hamiltonian variational ansatz
Quantum variational algorithms are one of the most promising applications of near-term
quantum computers; however, recent studies have demonstrated that unless the variational …
quantum computers; however, recent studies have demonstrated that unless the variational …
Recurrent quantum neural networks
J Bausch - Advances in neural information processing …, 2020 - proceedings.neurips.cc
Recurrent neural networks are the foundation of many sequence-to-sequence models in
machine learning, such as machine translation and speech synthesis. With applied quantum …
machine learning, such as machine translation and speech synthesis. With applied quantum …
SU (2) hadrons on a quantum computer via a variational approach
Quantum computers have the potential to create important new opportunities for ongoing
essential research on gauge theories. They can provide simulations that are unattainable on …
essential research on gauge theories. They can provide simulations that are unattainable on …
Estimating the gradient and higher-order derivatives on quantum hardware
For a large class of variational quantum circuits, we show how arbitrary-order derivatives
can be analytically evaluated in terms of simple parameter-shift rules, ie, by running the …
can be analytically evaluated in terms of simple parameter-shift rules, ie, by running the …