ArticleiScience2022
Variational learning of quantum ground states on spiking neuromorphic hardware.
Article in iScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Spike-based alignment learning solves the weight transport problem.Nature communications · 2026Article
- Ultrafast neural sampling with spiking nanolasers.Nature communications · 2025Article
- Enhanced quantum secret sharing protocol for anonymous secure communication utilizing W states.iScience · 2024Article
- Three learning stages and accuracy-efficiency tradeoff of restricted Boltzmann machines.Nature communications · 2022Article
- The BrainScaleS-2 Accelerated Neuromorphic System With Hybrid Plasticity.Frontiers in neuroscience · 2022Article
- A Scalable Approach to Modeling on Accelerated Neuromorphic Hardware.Frontiers in neuroscience · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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 (
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Registered trials
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