pytenet¶
PyTeNet¶
Python implementation of quantum tensor network operations and simulations within the matrix product state framework.
Modules
Operator state automaton, can be converted to an MPO via an operator graph. |
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Implementation of the Hopcroft-Karp algorithm, based on https://en.wikipedia.org/wiki/Hopcroft%E2%80%93Karp_algorithm |
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utility functions for handling block-sparse tensors with quantum number conservation. |
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Functions concerning virtual bonds. |
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Higher-level tensor network operations on a chain topology. |
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DMRG algorithm. |
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Construct the Bose-Hubbard Hamiltonian as a matrix product operator (MPO). |
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Construct the Fermi-Hubbard Hamiltonian as a matrix product operator (MPO). |
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Construct the Heisenberg Hamiltonian as a matrix product operator (MPO). |
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Construct the Ising Hamiltonian as a matrix product operator (MPO). |
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Represent a sum of fermionic creation or annihilation operators as a matrix product operator (MPO). |
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Construct a molecular Hamiltonian as a matrix product operator (MPO). |
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Represent a product of sums of fermionic creation and annihilation operators as a matrix product operator (MPO). |
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Construct a spin molecular Hamiltonian as a matrix product operator (MPO). |
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Construct a Hamiltonian as an MPO based on local operator chains, which are shifted along a 1D lattice. |
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Krylov subspace algorithms. |
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Matrix product operator (MPO) class and associated functionality. |
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Matrix product state (MPS) class and associated functionality. |
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Symbolic operator chain. |
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Operator graph internal data structure for generating MPO representations. |
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Symbolic operator tree. |
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Quantum number utility functions. |
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TDVP time integration algorithms for MPS, based on a Lanczos iteration for the local time evolution steps. |
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Tensor hypercontraction (THC) representation of molecular Hamiltonians. |
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Generic utility functions. |