pith. sign in

arxiv: 2512.06939 · v2 · pith:BK2AXNL2new · submitted 2025-12-07 · 🧮 math.AG · math.OC· physics.chem-ph

Numerical Algebraic Geometry for Energy Computations on Tensor Train Varieties

classification 🧮 math.AG math.OCphysics.chem-ph
keywords criticalpointstensortrainrayleigh-ritzvarietiesalgebraicdegree
0
0 comments X
read the original abstract

We study energy minimization problems in quantum chemistry through the lens of computational algebraic geometry. We focus on minimizing the Rayleigh quotient of a Hamiltonian over a tensor train variety. The complex critical points of this problem approximate eigenstates of the quantum system, with the global minimum approximating the ground state. We call the number of critical points the Rayleigh-Ritz degree. We first study the Rayleigh-Ritz degree and introduce the Rayleigh-Ritz discriminant, which describes Hamiltonians that lead to a deficient number of critical points. We then specialize this framework to tensor train varieties: we identify instances when they are Segre products of projective spaces, report what we know about their defining ideals, and present a birational parametrization from products of Grassmannians. We use homotopy continuation to compute all critical points of this optimization problem over various tensor train and determinantal varieties. Finally, we use these results to benchmark state-of-the-art methods, the Alternating Linear Scheme and Density Matrix Renormalization Group.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Copositive Matrices with Ordered Off-Diagonal Entries

    math.OC 2026-05 unverdicted novelty 7.0

    Copositive matrices with nondecreasing off-diagonal entries admit a PSD plus nonnegative decomposition, which implies exactness of a natural relaxation for separable quadratic optimization over the simplex.

  2. Algebraic Networks and Architectural Degenerations

    math.AG 2026-06 unverdicted novelty 6.0

    Develops algebraic geometry tools for monomial neural networks and proves the singular locus of neurovarieties is contained in the architectural degeneracy locus for fully connected networks with non-increasing widths...