Skip to main content
Ctrl+K

SeeMPS 2.1 documentation

  • Getting started
  • Quantum objects
  • Index of algorithms
  • Quantum registers
  • Quantum-inspired numerical analysis
    • Reading and writing
    • Other tools
    • Examples and Bibliography
  • Getting started
  • Quantum objects
  • Index of algorithms
  • Quantum registers
  • Quantum-inspired numerical analysis
  • Reading and writing
  • Other tools
  • Examples and Bibliography

Section Navigation

Contents:

  • Tensor splitting
    • Schmidt decomposition
    • Creating an MPS from a state vector
    • Canonical form
    • MPS update
      • seemps.state.CanonicalMPS.update_2site_right
      • seemps.state.CanonicalMPS.update_2site_left
  • MPS simplification
    • seemps.truncate.simplify
    • seemps.truncate.simplify_mpo.simplify_mpo
  • Gradient descent
    • seemps.optimization.gradient_descent
  • Conjugate gradient descent
    • seemps.cgs.cgs
  • Restarted Arnoldi iteration
    • seemps.optimization.arnoldi
      • seemps.optimization.arnoldi.arnoldi_eigh
      • seemps.optimization.arnoldi.MPSArnoldiRepresentation
    • seemps.evolution.arnoldi
  • Density-Matrix Renormalization Group
    • seemps.optimization.dmrg
  • Runge-Kutta methods
    • seemps.evolution.euler.euler
    • seemps.evolution.euler.euler2
    • seemps.evolution.runge_kutta.runge_kutta
    • seemps.evolution.runge_kutta.runge_kutta_fehlberg
  • Crank-Nicolson method
    • seemps.evolution.crank_nicolson.crank_nicolson
  • TEBD Time evolution
    • seemps.evolution.trotter.Trotter2ndOrder
    • seemps.evolution.trotter.Trotter3rdOrder
  • Chebyshev Approximation
    • seemps.analysis.chebyshev.projection_coefficients
    • seemps.analysis.chebyshev.interpolation_coefficients
    • seemps.analysis.chebyshev.estimate_order
    • seemps.analysis.chebyshev.cheb2mps
    • seemps.analysis.chebyshev.cheb2mpo
  • Tensor-train cross-interpolation (TT-Cross)
  • Multiscale interpolative constructions
    • seemps.analysis.lagrange.lagrange_basic
    • seemps.analysis.lagrange.lagrange_rank_revealing
    • seemps.analysis.lagrange.lagrange_local_rank_revealing
  • Index of algorithms
  • Tensor splitting

Tensor splitting#

One of the most basic algorithms for tensor network manipulation is to approximate a tensor with multiple legs, by a contraction of tensors with smaller numbers of legs. There are various criteria to do this, but in this section we will discuss thosed based on the Schmidt decomposition.

Contents:

  • Schmidt decomposition
  • Creating an MPS from a state vector
  • Canonical form
  • MPS update
    • seemps.state.CanonicalMPS.update_2site_right
    • seemps.state.CanonicalMPS.update_2site_left

previous

Index of algorithms

next

Schmidt decomposition

Show Source

© Copyright 2019, Juan Jose Garcia-Ripoll.

Created using Sphinx 7.4.4.

Built with the PyData Sphinx Theme 0.15.4.