arXiv:2512.17090v2 Announce Type: replace Abstract: Squared tensor networks (TNs) and their extension as computational graphs--squared circuits--have been used as expressive distribution estimators, yet supporting closed-form marginalization. However, the squaring operation introduces additional complexity when computing the partition function or marginalizing variables, which hinders their applicability in ML. To solve this issue, canonical forms of TNs are parameterized via unitary matrices to simplify the computation of marginals. However, these canonical forms do not apply to circuits, as
This paper addresses a known computational complexity issue in advanced machine learning models (tensor networks and squared circuits) by proposing a method to simplify their use.
Improved computational efficiency for these models could expand their applicability in machine learning, potentially enabling more powerful and complex AI systems.
The proposed method aims to make complex tensor network and circuit models more practical for real-world ML applications by reducing computational overhead.
- · AI researchers
- · Machine learning developers
- · Advanced AI model applications
More efficient development and deployment of certain types of AI models with closed-form marginalization capabilities.
Potential for these specific AI architectures to see increased adoption in areas where computational bottlenecks previously limited their use.
Long-term, this could contribute to the development of more advanced and robust AI systems, though the direct impact on the broader AI landscape is incremental.
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Read at arXiv cs.LG