## Tensor Network Rewriting Strategies for Satisfiability and Counting

 Niel de Beaudrap (Quantum Group, Department of Computer Science, University of Oxford) Aleks Kissinger (Quantum Group, Department of Computer Science, University of Oxford) Konstantinos Meichanetzidis (Quantum Group, Department of Computer Science, University of Oxford and Cambridge Quantum Computing)

 We provide a graphical treatment of SAT and #SAT on equal footing. Instances of #SAT can be represented as tensor networks in a standard way. These tensor networks are interpreted by diagrams of the ZH-calculus: a system to reason about tensors over C in terms of diagrams built from simple generators, in which computation may be carried out by transformations of diagrams alone. In general, nodes of ZH diagrams take parameters over C which determine the tensor coefficients; for the standard representation of #SAT instances, the coefficients take the value 0 or 1. Then, by choosing the coefficients of a diagram to range over B, we represent the corresponding instance of SAT. Thus, by interpreting a diagram either over the boolean semiring or the complex numbers, we instantiate either the decision or counting version of the problem. We find that for classes known to be in P, such as 2SAT and #XORSAT, the existence of appropriate rewrite rules allows for efficient simplification of the diagram, producing the solution in polynomial time. In contrast, for classes known to be NP-complete, such as 3SAT, or #P-complete, such as #2SAT, the corresponding rewrite rules introduce hyperedges to the diagrams, in numbers which are not easily bounded above by a polynomial. This diagrammatic approach unifies the diagnosis of the complexity of CSPs and #CSPs and shows promise in aiding tensor network contraction-based algorithms.

In Benoît Valiron, Shane Mansfield, Pablo Arrighi and Prakash Panangaden: Proceedings 17th International Conference on Quantum Physics and Logic (QPL 2020), Paris, France, June 2 - 6, 2020, Electronic Proceedings in Theoretical Computer Science 340, pp. 46–59.
Published: 6th September 2021.

 ArXived at: http://dx.doi.org/10.4204/EPTCS.340.3 bibtex PDF
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