API reference¶
Generated from the docstrings of the package. The package exports the main classes at the top level (from torch_vn_algebra import TypeIAlgebra, SpinChain, SU2Chain, ...); the modules are:
| module | contents |
|---|---|
torch_vn_algebra.algebra |
Type I von Neumann algebra M = ⊕_{c=1}^{C} M_{n_c}(ℂ) with subspace dimensions k_c. |
torch_vn_algebra.functional |
Functional calculus on stacks of blocks (n, k, k), at four levels of generality. |
torch_vn_algebra.hilbert_space |
Hilbert space H of dimension n with a distinguished k-dimensional subspace H0. |
torch_vn_algebra.states |
States (density matrices) on M = (+)_c M_{k_c} and information-theoretic quantities. |
torch_vn_algebra.channels |
Quantum channels on a Type I algebra M = (+)_c M_{k_c} that preserve the sectors. |
torch_vn_algebra.composite |
Tensor products of Type I algebras. |
torch_vn_algebra.dynamics |
Time evolution on M = (+)_c M_{k_c} for Hamiltonians and Lindblad generators in M. |
torch_vn_algebra.chains |
Spin-1/2 chains with a conserved number of up spins (U(1) symmetry, total S^z). |
torch_vn_algebra.krylov |
Krylov-subspace methods for large sectors, where dense matrices no longer fit. |
torch_vn_algebra.su2 |
SU(2) symmetry of spin-1/2 chains: spin multiplets, highest-weight vectors and the Wigner-Eckart theorem. |
torch_vn_algebra.decompose |
From symmetries or generators to the algebra: numerical Wedderburn decomposition. |
torch_vn_algebra.inclusions |
Unital inclusions of finite-dimensional von Neumann algebras, conditional expectations and index. |
torch_vn_algebra.divergences |
Quantum Renyi divergences, Petz recovery maps and noncommutative L^p norms on M = (+)_c M_{k_c}. |
torch_vn_algebra.jordan |
Jordan structure of finite-dimensional von Neumann algebras: Jordan products, the quadratic representation, Jordan subalgebras of Hermitian matrices and spin factors. |
torch_vn_algebra.optimize |
Batched gradient-based optimisation over operators of a Type I algebra. |
torch_vn_algebra.cost |
Safeguards against unexpectedly long or memory-hungry computations. |
The tutorial shows every module at work, with tested code.