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Researchers draw parallels between Boltzmann machines and quantum physics path integrals

By PulseAugur Editorial · [1 sources] ·

This paper draws an analogy between Boltzmann machines used in machine learning and Feynman path integrals from quantum physics. The authors suggest that hidden layers in neural networks can be viewed as discrete versions of path elements within the Feynman path-integral formalism. This connection allows for the development of general quantum circuit models applicable to both Boltzmann machines and Feynman path integrals, and offers a method for defining interpretable hidden layers by relating them to inverse quantum scattering problems. AI

IMPACT Explores theoretical links between machine learning models and quantum physics, potentially inspiring new model architectures.

RANK_REASON This is a research paper exploring theoretical connections between machine learning and quantum physics. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Researchers draw parallels between Boltzmann machines and quantum physics path integrals

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This is a research paper exploring theoretical connections between machine learning and quantum physics. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Srinivasan S. Iyengar, Sabre Kais ·

    Analogy between Boltzmann machines and Feynman path integrals

    arXiv:2301.06217v1 Announce Type: cross Abstract: We provide a detailed exposition of the connections between Boltzmann machines commonly utilized in machine learning problems and the ideas already well known in quantum statistical mechanics through Feynman's description of the s…

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