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Quantum Machine Learning

Information Perspective to Probabilistic Modeling: Boltzmann Machines versus Born Machines

arXiv
Authors: Song Cheng, Jing Chen, Lei Wang

Year

2017

Paper ID

24571

Status

Preprint

Abstract Read

~2 min

Abstract Words

93

Citations

N/A

Abstract

We compare and contrast the statistical physics and quantum physics inspired approaches for unsupervised generative modeling of classical data. The two approaches represent probabilities of observed data using energy-based models and quantum states respectively.Classical and quantum information patterns of the target datasets therefore provide principled guidelines for structural design and learning in these two approaches. Taking the restricted Boltzmann machines (RBM) as an example, we analyze the information theoretical bounds of the two approaches. We verify our reasonings by comparing the performance of RBMs of various architectures on the standard MNIST datasets.

Why This Paper Matters

  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2017 reference point for readers tracking recent quantum research.
  • We compare and contrast the statistical physics and quantum physics inspired approaches for unsupervised generative modeling of classical data.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:1712.04144 [2] arXiv https://arxiv.org/abs/1712.04144 [3] Publisher https://arxiv.org/abs/1712.04144

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