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Efficient Hamiltonian learning from Gibbs states
by Adam Artymowicz
Submission summary
| Authors (as registered SciPost users): | Adam Artymowicz |
| Submission information | |
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| Preprint Link: | scipost_202409_00028v1 (pdf) |
| Code repository: | https://github.com/artymowicz/hamiltonian-learning |
| Date submitted: | 2024-09-23 20:38 |
| Submitted by: | Artymowicz, Adam |
| Submitted to: | SciPost Physics |
| Ontological classification | |
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| Academic field: | Physics |
| Specialties: |
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| Approaches: | Theoretical, Computational |
Abstract
We describe a novel algorithm that learns a Hamiltonian from local expectations of its Gibbs state using the free energy variational principle. The algorithm avoids the need to compute the free energy directly, instead using efficient estimates of the derivatives of the free energy with respect to perturbations of the state. These estimates are based on a new entropy bound for Lindblad evolutions, which is of independent interest. We benchmark the algorithm by performing black-box learning of a nearest-neighbour Hamiltonian on a 100-qubit spin chain. A implementation of the algorithm with a Python front-end is made available for use.
Author indications on fulfilling journal expectations
- Provide a novel and synergetic link between different research areas.
- Open a new pathway in an existing or a new research direction, with clear potential for multi-pronged follow-up work
- Detail a groundbreaking theoretical/experimental/computational discovery
- Present a breakthrough on a previously-identified and long-standing research stumbling block
Current status:
In refereeing