Learning ground states of gapped quantum Hamiltonians with Kernel Methods
Neural network approaches to approximate the ground state of quantum hamiltonians require the numerical solution of a highly nonlinear optimization problem. We introduce a statistical learning approach that makes the optimization trivial by using kernel methods. Our scheme is an approximate realizat...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften
2023-08-01
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| Series: | Quantum |
| Online Access: | https://quantum-journal.org/papers/q-2023-08-29-1096/pdf/ |