A Quantum Information Theoretic Approach to Tractable Probabilistic Models
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Abstract (arXiv)
By recursively nesting sums and products, probabilistic circuits have emerged in recent years as an attractive class of generative models as they enjoy, for instance, polytime marginalization of random variables. In this work we study these machine learning models using the framework of quantum information theory, leading to the introduction of positive unital circuits (PUnCs), which generalize circuit evaluations over positive real-valued probabilities to circuit evaluations over positive semi-definite matrices. As a consequence, PUnCs strictly generalize probabilistic circuits as well as recently introduced circuit classes such as PSD circuits.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 19 of its 51 references that are in the map. First posted on arXiv 2 Jun 2025.
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19 of 51 references.
References · 19
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7 more
- 2021HyperSPNs: Compact and Expressive Probabilistic Circuitscited by 14
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