Sampling scheme for neuromorphic simulation of entangled quantum systems
Due to the complexity of the space of quantum many-body states, the computation of expectation values by statistical sampling is, in general, a hard task. Neural network representations of such quantum states, which can be physically implemented by neuromorphic hardware, could enable efficient sampl...
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| Main Authors: | , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
13 November 2019
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| In: |
Physical review
Year: 2019, Volume: 100, Issue: 19 |
| ISSN: | 2469-9969 |
| DOI: | 10.1103/PhysRevB.100.195120 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1103/PhysRevB.100.195120 Verlag, lizenzpflichtig, Volltext: https://link.aps.org/doi/10.1103/PhysRevB.100.195120 
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| Author Notes: | Stefanie Czischek, Jan M. Pawlowski, Thomas Gasenzer, and Martin Gärttner |
| Summary: | Due to the complexity of the space of quantum many-body states, the computation of expectation values by statistical sampling is, in general, a hard task. Neural network representations of such quantum states, which can be physically implemented by neuromorphic hardware, could enable efficient sampling. A scheme is proposed that leverages this capability to speed up sampling from so-called neural quantum states encoded by a restricted Boltzmann machine. Due to the complex network parameters, a direct hardware implementation is not feasible. We overcome this problem by considering a phase-reweighting scheme for sampling expectation values of observables. Applying our method to a set of paradigmatic entangled quantum states we find that, in general, the phase-reweighted sampling is subject to a form of sign problem, which renders the sampling computationally costly. The use of neuromorphic chips could allow reducing computation times and thereby extend the range of tractable system sizes. |
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| Item Description: | Gesehen am 11.03.2020 |
| Physical Description: | Online Resource |
| ISSN: | 2469-9969 |
| DOI: | 10.1103/PhysRevB.100.195120 |