Predicting Ground State Properties: Constant Sample Complexity and Deep Learning Algorithms
<|reference_start|>Predicting Ground State Properties: Constant Sample Complexity and Deep Learning Algorithms: A fundamental problem in quantum many-body physics is that of finding ground states of local Hamiltonians. A number of recent works gave provably efficient machine learning (ML) algorithms for learning ground states. Specifically, [Huang et al. Science 2022], introduced an approach for learning properties of the ground state of an $n$-qubit gapped local Hamiltonian $H$ from only $n^{\mathcal{O}(1)}$ data points sampled from Hamiltonians in the same phase of matter. This was subsequently improved by [Lewis et al. Nature Communications 2024], to $\mathcal{O}(\log n)$ samples when the geometry of the $n$-qubit system is known. In this work, we introduce two approaches that achieve a constant sample complexity, independent of system size $n$, for learning ground state properties. Our first algorithm consists of a simple modification of the ML model used by Lewis et al. and applies to a property of interest known beforehand. Our second algorithm, which applies even if a description of the property is not known, is a deep neural network model. While empirical results showing the performance of neural networks have been demonstrated, to our knowledge, this is the first rigorous sample complexity bound on a neural network model for predicting ground state properties. We also perform numerical experiments that confirm the improved scaling of our approach compared to earlier results.<|reference_end|>
@article{wanner2024predicting,
    title={Predicting Ground State Properties: Constant Sample Complexity and Deep
  Learning Algorithms},
    author={Marc Wanner, Laura Lewis, Chiranjib Bhattacharyya, Devdatt Dubhashi,
  Alexandru Gheorghiu},
    journal={arXiv preprint arXiv:2405.18489},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.18489},
    primaryClass={quant-ph cs.LG physics.comp-ph}
}