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- W4287235192 abstract "Efficient characterization of quantum devices is a significant challenge critical for the development of large scale quantum computers. We consider an experimentally motivated situation, in which we have a decent estimate of the Hamiltonian, and its parameters need to be characterized and fine-tuned frequently to combat drifting experimental variables. We use a machine learning technique known as meta-learning to learn a more efficient optimizer for this task. We consider training with the nearest-neighbor Ising model and study the trained model's generalizability to other Hamiltonian models and larger system sizes. We observe that the meta-optimizer outperforms other optimization methods in average loss over test samples. This advantage follows from the meta-optimizer being less likely to get stuck in local minima, which highly skews the distribution of the final loss of the other optimizers. In general, meta-learning decreases the number of calls to the experiment and reduces the needed classical computational resources." @default.
- W4287235192 created "2022-07-25" @default.
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- W4287235192 date "2021-04-09" @default.
- W4287235192 modified "2023-09-24" @default.
- W4287235192 title "Meta Hamiltonian Learning" @default.
- W4287235192 doi "https://doi.org/10.48550/arxiv.2104.04453" @default.
- W4287235192 hasPublicationYear "2021" @default.
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