# Parallel Weighted Model Counting with Tensor Networks

arXiv: https://arxiv.org/abs/2006.15512 · HTML: https://mlsanity.com/paper/2006.15512/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jun 2020.

## Built on by (most cited first, 2 of 2)

- [GPU accelerated program synthesis: Enumerate semantics, not syntax!](https://mlsanity.com/paper/2504.18943/) (2025)
- [State Algebra for Probabilistic Logic](https://mlsanity.com/paper/2603.13574/) (2026)

## Abstract (arXiv)

A promising new algebraic approach to weighted model counting makes use of tensor networks, following a reduction from weighted model counting to tensor-network contraction. Prior work has focused on analyzing the single-core performance of this approach, and demonstrated that it is an effective addition to the current portfolio of weighted-model-counting algorithms. In this work, we explore the impact of multi-core and GPU use on tensor-network contraction for weighted model counting. To leverage multiple cores, we implement a parallel portfolio of tree-decomposition solvers to find an order to contract tensors. To leverage a GPU, we use TensorFlow to perform the contractions. We compare the resulting weighted model counter on 1914 standard weighted model counting benchmarks and show that it significantly improves the virtual best solver.