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Cited by 215Quantum Computing Algorithms and ArchitectureQuantum many-body systemsQuantum Information and Cryptography

The DOI matches its Crossref record; 52 of the 57 references it cites were found in a registry or an index.

Estimation of effective temperatures in quantum annealers for sampling applications: A case study with possible applications in deep learning

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Benedetti, Marcello, Realpe-Gómez, John, Biswas, Rupak, Perdomo-Ortiz, Alejandro·2016·Physical Review A
DOI 10.1103/physreva.94.022308
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Abstract

An increase in the efficiency of sampling from Boltzmann distributions would have a significant impact on deep learning and other machine-learning applications. Recently, quantum annealers have been proposed as a potential candidate to speed up this task, but several limitations still bar these state-of-the-art technologies from being used effectively. One of the main limitations is that, while the device may indeed sample from a Boltzmann-like distribution, quantum dynamical arguments suggest it will do so with an instance-dependent effective temperature, different from its physical temperature. Unless this unknown temperature can be unveiled, it might not be possible to effectively use a quantum annealer for Boltzmann sampling. In this work, we propose a strategy to overcome this challenge with a simple effective-temperature estimation algorithm. We provide a systematic study assessing the impact of the effective temperatures in the learning of a special class of a restricted Boltzmann machine embedded on quantum hardware, which can serve as a building block for deep-learning architectures. We also provide a comparison to $k$-step contrastive divergence (CD-$k$) with $k$ up to 100. Although assuming a suitable fixed effective temperature also allows us to outperform one-step contrastive divergence (CD-1), only when using an instance-dependent effective temperature do we find a performance close to that of CD-100 for the case studied here.

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  1. P. M. Long, 2010, Proceedings of the 27th Inter-national Conference on Machine Learning (ICML-10)
  2. P. Smolensky, 1986, Parallel Distributed Processing: Explorations in the Microstructure of Cognition
  3. G. E. Hinton, 1986, Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1
  4. A. Fischer, 2010, Proceedings of the 20th International Conference on Artificial Neural Networks: Part III
  5. M. Gabrié, 2015, Advances in Neural Information Processing Systems 28
  6. G. E. Hinton, 2012, Neural Networks: Tricks of the Trade

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Metadata

Authors
Benedetti, M., Realpe-Gómez, J., Biswas, R., & Perdomo-Ortiz, A.
Affiliations
Ames Research Center (US)
ORCID
Venue
Physical Review A
Publisher
American Physical Society (APS)
Published
2016-08-09
Volume
94
Issue
2
ISSN
  • 0556-2791 (ISSN-L)
  • 2469-9926
  • 2469-9934
DOI
10.1103/physreva.94.022308
OpenAlex
W2517233404
Cited by
215
References
57
Citations by year
2026: 15 · 2025: 17 · 2024: 14 · 2023: 27 · 2022: 27 · 2021: 18 · 2020: 23 · 2019: 27 · 2018: 29 · 2017: 13 · 2016: 5
FWCI
20.06
Type
journal article
Language
en
Open access
Licence
Locations
  • Physical Review A (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
Ames Research Center (NNA14AA60C, NAS2-03144)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
  • Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum many-body systems
  • Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum Information and Cryptography
Keywords
quantum annealers, effective temperature estimation, Boltzmann sampling, restricted Boltzmann machine, quantum machine learning, contrastive divergence, deep learning, quantum computing hardware, sampling efficiency

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