Variational learning of quantum ground states on spiking neuromorphic hardware

• Verfasst von: Klassert, Robert [VerfasserIn]
• Verfasst von: Baumbach, Andreas [VerfasserIn]
• Verfasst von: Petrovici, Mihai A. [VerfasserIn]
• Verfasst von: Gärttner, Martin [VerfasserIn]
• Titel: Variational learning of quantum ground states on spiking neuromorphic hardware
• Verf.angabe: Robert Klassert, Andreas Baumbach, Mihai A. Petrovici, and Martin Gärttner
• E-Jahr: 2021
• Jahr: November 29, 2021
• Umfang: 13 S.
• Fussnoten: Gesehen am 13.07.2022
• Titel Quelle: Enthalten in: De.arxiv.org
• Ort Quelle: [S.l.] : Arxiv.org, 1991
• Jahr Quelle: 2021
• Band/Heft Quelle: (2021), Artikel-ID 2109.15169, Seite 1-13
• DOI: doi:10.48550/arXiv.2109.15169
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Computer Science - Emerging Technologies
• Sach-SW: Computer Science - Neural and Evolutionary Computing
• Sach-SW: Condensed Matter - Disordered Systems and Neural Networks
• Sach-SW: Quantum Physics
• K10plus-PPN: 1810071453

Abstract

Recent 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 ($N\leq 10$). A systematic hyperparameter study shows that scalability to larger system sizes mainly 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.