ArticleEntropy (Basel, Switzerland)2020
Hybrid Quantum-Classical Neural Network for Calculating Ground State Energies of Molecules.
Article in Entropy (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Targeted Energy Transfer Dynamics and Chemical Reactions.Entropy (Basel, Switzerland) · 2024Article
- Quantum neural network cost function concentration dependency on the parametrization expressivity.Scientific reports · 2023Article
- A quantum Hopfield associative memory implemented on an actual quantum processor.Scientific reports · 2021Article
- A co-design framework of neural networks and quantum circuits towards quantum advantage.Nature communications · 2021Article
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2 authors.
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Abstract
We present a hybrid quantum-classical neural network that can be trained to perform electronic structure calculation and generate potential energy curves of simple molecules. The method is based on the combination of parameterized quantum circuits and measurements. With unsupervised training, the neural network can generate electronic potential energy curves based on training at certain bond lengths. To demonstrate the power of the proposed new method, we present the results of using the quantum-classical hybrid neural network to calculate ground state potential energy curves of simple molecules such as H2, LiH, and BeH2. The results are very accurate and the approach could potentially be used to generate complex molecular potential energy surfaces.
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