ArticleNature communications2018
Quantum machine learning for electronic structure calculations.
Article in Nature communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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22 citing papers in PubMed, 138 citations in OpenAlex.
- On machine learnability of local contributions to interatomic potentials from density functional theory calculations.Scientific reports · 2024Article
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- Development and validation of machine learning models for diagnosis and prognosis of lung adenocarcinoma, and immune infiltration analysis.Scientific reports · 2024Article
- Quantum circuit for high order perturbation theory corrections.Scientific reports · 2024Article
- On the additive artificial intelligence-based discovery of nanoparticle neurodegenerative disease drug delivery systems.Beilstein journal of nanotechnology · 2024Article
- Taking advantage of noise in quantum reservoir computing.Scientific reports · 2023Article
- Toward perturbation theory methods on a quantum computer.Science advances · 2023Article
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- Characterizing quantum circuits with qubit functional configurations.Scientific reports · 2023Article
- Three learning stages and accuracy-efficiency tradeoff of restricted Boltzmann machines.Nature communications · 2022Article
- Optimization two-qubit quantum gate by two optical control methods in molecular pendular states.Scientific reports · 2022Article
- Variational quantum support vector machine based on [Formula: see text] matrix expansion and variational universal-quantum-state generator.Scientific reports · 2022Article
- Quantum Chemistry Calculations for Metabolomics.Chemical reviews · 2021Review
- Massively parallel classical logic via coherent dynamics of an ensemble of quantum systems with dispersion in size.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Hybrid Quantum-Classical Neural Network for Calculating Ground State Energies of Molecules.Entropy (Basel, Switzerland) · 2020Article
- Fermionic neural-network states for ab-initio electronic structure.Nature communications · 2020Article
- Entropy, Free Energy, and Work of Restricted Boltzmann Machines.Entropy (Basel, Switzerland) · 2020Article
- A quantum algorithm for evolving open quantum dynamics on quantum computing devices.Scientific reports · 2020Article
- Machine learning enables long time scale molecular photodynamics simulations.Chemical science · 2019Article
- An adaptive variational algorithm for exact molecular simulations on a quantum computer.Nature communications · 2019Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Considering recent advancements and successes in the development of efficient quantum algorithms for electronic structure calculations-alongside impressive results using machine learning techniques for computation-hybridizing quantum computing with machine learning for the intent of performing electronic structure calculations is a natural progression. Here we report a hybrid quantum algorithm employing a restricted Boltzmann machine to obtain accurate molecular potential energy surfaces. By exploiting a quantum algorithm to help optimize the underlying objective function, we obtained an efficient procedure for the calculation of the electronic ground state energy for a small molecule system. Our approach achieves high accuracy for the ground state energy for H
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