Quantum-inspired tempering for ground state approximation using artificial neural networks
A large body of work has demonstrated that parameterized artificial neural networks (ANNs) can efficiently describe ground states of numerous interesting quantum many-body Hamiltonians. However, the standard variational algorithms used to update or train the ANN parameters can get trapped in local m...
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| Format: | Article |
| Language: | English |
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SciPost
2023-05-01
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| Series: | SciPost Physics |
| Online Access: | https://scipost.org/SciPostPhys.14.5.121 |