文章基本信息

标题：Variational learning of quantum ground states on spiking neuromorphic hardware
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作者：Robert Klassert ; Andreas Baumbach ; Mihai A. Petrovici 等
期刊名称：iScience
印刷版ISSN：2589-0042
出版年度：2022
卷号：25
期号：8
页码：1-23
DOI：10.1016/j.isci.2022.104707
语种：English
出版社：Elsevier
摘要：SummaryRecent research has demonstrated the usefulness of neural networks as variational ansatz functions for quantum many-body states. However, high-dimensional sampling spaces and transient autocorrelations confront these approaches with a challenging computational bottleneck. Compared to conventional neural networks, physical model devices offer a fast, efficient and inherently parallel substrate capable of related forms of Markov chain Monte Carlo sampling. Here, we demonstrate the ability of a neuromorphic chip to represent the ground states of quantum spin models by variational energy minimization. We develop a training algorithm and apply it to the transverse field Ising model, showing good performance at moderate system sizes (N≤10). A systematic hyperparameter study shows that performance depends on sample quality, which is limited by temporal parameter variations on the analog neuromorphic chip. Our work thus provides an important step towards harnessing the capabilities of neuromorphic hardware for tackling the curse of dimensionality in quantum many-body problems.Graphical abstractDisplay OmittedHighlights•Variational scheme for representing quantum ground states with neuromorphic hardware•Accelerated physical system yields system-size independent sample generation time•Accurate learning of ground states across a quantum phase transition•Detailed analysis of algorithmic and technical limitationsQuantum mechanics; Hardware implemented algorithm; Electrical materials