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  2. Sequant Framework For Symbolic And Numerical Tensor Algebra. I. Core Capabilities.
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  2. Sequant Framework For Symbolic And Numerical Tensor Algebra. I. Core Capabilities.

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SeQuant framework for symbolic and numerical tensor algebra. I. Core capabilities.

Bimal Gaudel1, Robert G Adam2, Ajay Melekamburath1

  • 1Department of Chemistry, Virginia Tech, Blacksburg, Virginia 24061, USA.

The Journal of Chemical Physics
|April 13, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

SeQuant is an open-source library for symbolic algebra of tensors. Its graph-theoretic tensor network canonicalizer efficiently handles symmetries, optimizing tensor expression simplification and numerical evaluation.

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Area of Science:

  • Computational Physics
  • Quantum Chemistry
  • Data Science

Background:

  • Symbolic algebra of tensors is crucial for various scientific domains.
  • Existing methods for tensor network (TN) manipulation can be computationally intensive, especially with symmetries.

Purpose of the Study:

  • Introduce SeQuant, an open-source library for symbolic tensor algebra.
  • Enhance the efficiency of tensor expression simplification and numerical evaluation.

Main Methods:

  • Developed a graph-theoretic tensor network (TN) canonicalizer.
  • Implemented support for non-covariant TNs and parametrically dependent tensor modes.
  • Integrated compiler-like components for optimization and direct numerical interpretation.

Main Results:

  • Achieved faster handling of TNs with symmetries compared to group-theoretic methods.
  • Enabled routine simplification of conventional tensor expressions.
  • Optimized the application of Wick's theorem for canonicalizing tensor products.
  • Facilitated manipulation of intermediate representations for numerical evaluation.

Conclusions:

  • SeQuant offers a novel approach to symbolic tensor algebra with significant performance improvements.
  • The library effectively bridges symbolic manipulation and numerical evaluation.
  • Provides advanced features for complex tensor structures relevant to modern simulations and data science.