Inversion symmetry of singular values and a new orbital ordering method in tensor train approximations for quantum chemistry

02/18/2020
by   Mi-Song Dupuy, et al.
0

The tensor train approximation of electronic wave functions lies at the core of the QC-DMRG (Quantum Chemistry Density Matrix Renormalization Group) method, a recent state-of-the-art method for numerically solving the N-electron Schrödinger equation. It is well known that the accuracy of TT approximations is governed by the tail of the associated singular values, which in turn strongly depends on the ordering of the one-body basis. Here we find that the singular values s_1> s_2> ... > s_d of tensors representing ground states of noninteracting Hamiltonians possess a surprising inversion symmetry, s_1s_d=s_2s_d-1=s_3s_d-2=..., thus reducing the tail behaviour to a single hidden invariant, which moreover depends explicitly on the ordering of the basis. For correlated wavefunctions, we find that the tail is upper bounded by a suitable superposition of the invariants. Optimizing the invariants or their superposition thus provides a new ordering scheme for QC-DMRG. Numerical tests on simple examples, i.e. linear combinations of a few Slater determinants, show that the new scheme reduces the tail of the singular values by several orders of magnitudes over existing methods, including the widely used Fiedler order.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
04/13/2021

Nondegeneracy of eigenvectors and singular vector tuples of tensors

In this article, nondegeneracy of singular vector tuples, Z-eigenvectors...
research
01/05/2023

A tensor bidiagonalization method for higher-order singular value decomposition with applications

The need to know a few singular triplets associated with the largest sin...
research
09/23/2021

A note on perturbation analysis for T-product based tensor singular values

In this note, we present perturbation analysis for the T-product based t...
research
07/09/2022

Error Analysis of Tensor-Train Cross Approximation

Tensor train decomposition is widely used in machine learning and quantu...
research
11/05/2018

Spectrally stable defect qubits with no inversion symmetry for robust spin-to-photon interface

Scalable spin-to-photon interfaces require quantum emitters with strong ...
research
08/30/2020

Fast inversion, preconditioned quantum linear system solvers, and fast evaluation of matrix functions

Preconditioning is the most widely used and effective way for treating i...

Please sign up or login with your details

Forgot password? Click here to reset