Probabilistic Modeling with Matrix Product States

James Stokes; John Terilla

doi:10.3390/e21121236

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arXiv · 1902.06888

Probabilistic Modeling with Matrix Product States

James Stokes·John Terilla

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Abstract

Inspired by the possibility that generative models based on quantum circuits can provide a useful inductive bias for sequence modeling tasks, we propose an efficient training algorithm for a subset of classically simulable quantum circuit models. The gradient-free algorithm, presented as a sequence of exactly solvable effective models, is a modification of the density matrix renormalization group procedure adapted for learning a probability distribution. The conclusion that circuit-based models offer a useful inductive bias for classical datasets is supported by experimental results on the parity learning problem.

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quant-ph cs.LG stat.ML

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James Stokes, John Terilla. 2019-02-19. Probabilistic Modeling with Matrix Product States. https://doi.org/10.3390/e21121236

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