Researchers draw parallels between Boltzmann machines and quantum physics path integrals

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

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

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

在 arXiv cs.LG 阅读 →

• neural networks
• quantum mechanics
• quantum statistical mechanics
• Srinivasan Iyengar
• Boltzmann machines
• Feynman path integrals

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