Changelog#

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

[Unreleased]#

Added a reference implementation of arbitrary order Schrieffer-Wolff transformation to the documentation and a comparison of its convergence radius with Pymablock algorithm.
Added support for bosonic second-quantized Hamiltonians in the operator formalism.

If the Hamiltonian contains only one block, fully_diagonalize can be provided as an array directly.
Included the second quantization documentation.
Updated the dispersive shift tutorial to include the second quantization approach.
Updated the Jaynes-Cummings tutorial to the new second quantization API.

Dropped support for Numpy 1.25, Scipy 1.11, and Sympy 1.12 according to the SPEC-0.

Generalized the algorithm to support an arbitrary number of blocks. To specify multiple blocks, provide either a list with eigenvectors of each block in subspace_eigenvectors, or a list marking to which block each basis state belongs in subspace_indices.
Implemented full diagonalization of the Hamiltonian within blocks except for degenerate eigensubspaces. In case of one block with non-degenerate eigenvalues, this implements the Rayleigh-Schrödinger perturbation theory.
Implemented selective diagonalization of the Hamiltonian within blocks, which can eliminate any subset of the off-diagonal elements within a block.
Implemented functionality for making optimized series algorithms, see series_computation and a domain-specific language to define those. This is an advanced and an experimental feature, subject to change.
Added a function operator_to_BlockSeries to transform operators to the same representation as the Hamiltonian, and illustrated its use in the tutorial.
Included a tutorial on how to manipulate complex symbolic Hamiltonians and demonstrate multi-block diagonalization.
Included a tutorial on how to compute the dispersive shift in a transmon-resonator system.

Auxiliary vectors for the implicit KPM solver should now be passed using solver_options["aux_vectors"] rather than as the last entry in subspace_eigenvectors.

Further reduced the number of matrix products by around 30% for high orders and down to a guaranteed minimum for 3rd order.
Improved the efficiency of the MUMPS solver on real Hamiltonians.
Allowed subspaces to have degenerate eigenvalues if the corresponding energy denominators are never used. This may happen in multiblock perturbation theory.

Switched to the python-mumps wrapper for the direct solver, which is available on all platforms and is more feature-complete.
The implicit KPM solver now guarantees reaching a requested accuracy.
A new algorithm that has optimal scaling while avoiding multiplication by \(H_0\), and supports implicit data. This combines advantages of all previous algorithms, and therefore supersedes them.
Sped up cauchy_dot_product when there are more than 3 series by reusing intermediate results.
Optimized memory usage of ~pymablock.block_diagonalize by deleting intermediate results when they are no longer needed.

A complete description of the algorithm to the documentation, see documentation.
String representation of BlockSeries for readability.

expanded, symbolic, implicit, and general functions and algorithms (functionality taken over by the new general algorithm, with block_diagonalize the main interface).
the algorithm argument from block_diagonalize (there is only one algorithm now).
exclude_last argument of cauchy_dot_product (instead we check whtether other terms lack 0th order).