Source code for qutip.simdiag

__all__ = ['simdiag']

import numpy as np
import scipy.linalg as la
from . import Qobj
from .core import data as _data

def _degen(tol, vecs, ops, i=0):
    Private function that finds eigen vals and vecs for degenerate matrices..
    if len(ops) == i:
        return vecs

    # New eigenvectors are sometime not orthogonal.
    for j in range(1, vecs.shape[1]):
        for k in range(j):
            dot = vecs[:, j].dot(vecs[:, k].conj())
            if np.abs(dot) > tol:
                vecs[:, j] = ((vecs[:, j] - dot * vecs[:, k])
                              / (1 - np.abs(dot)**2)**0.5)

    subspace = vecs.conj().T @ ops[i].full() @ vecs
    eigvals, eigvecs = la.eig(subspace)

    perm = np.argsort(eigvals)
    eigvals = eigvals[perm]

    vecs_new = vecs @ eigvecs[:, perm]
    for k in range(len(eigvals)):
        vecs_new[:, k] = vecs_new[:, k] / la.norm(vecs_new[:, k])

    k = 0
    while k < len(eigvals):
        ttol = max(tol, tol * abs(eigvals[k]))
        inds, = np.where(abs(eigvals - eigvals[k]) < ttol)
        if len(inds) > 1:  # if at least 2 eigvals are degenerate
            vecs_new[:, inds] = _degen(tol, vecs_new[:, inds], ops, i+1)
        k = inds[-1] + 1
    return vecs_new

[docs]def simdiag(ops, evals: bool = True, *, tol: float = 1e-14, safe_mode: bool = True): """Simultaneous diagonalization of commuting Hermitian matrices. Parameters ---------- ops : list/array ``list`` or ``array`` of qobjs representing commuting Hermitian operators. evals : bool [True] Whether to return the eigenvalues for each ops and eigenvectors or just the eigenvectors. tol : float [1e-14] Tolerance for detecting degenerate eigenstates. safe_mode : bool [True] Whether to check that all ops are Hermitian and commuting. If set to ``False`` and operators are not commuting, the eigenvectors returned will often be eigenvectors of only the first operator. Returns ------- eigs : tuple Tuple of arrays representing eigvecs and eigvals of quantum objects corresponding to simultaneous eigenvectors and eigenvalues for each operator. """ if not ops: raise ValueError("No input matrices.") N = ops[0].shape[0] num_ops = len(ops) if safe_mode else 0 for jj in range(num_ops): A = ops[jj] shape = A.shape if shape[0] != shape[1]: raise TypeError('Matricies must be square.') if shape[0] != N: raise TypeError('All matrices. must be the same shape') if not A.isherm: raise TypeError('Matricies must be Hermitian') for kk in range(jj): B = ops[kk] if (A * B - B * A).norm() / (A * B).norm() > tol: raise TypeError('Matricies must commute.') # TODO: rewrite using Data object eigvals, eigvecs = _data.eigs(ops[0].data, True, True) eigvecs = eigvecs.to_array() k = 0 while k < N: # find degenerate eigenvalues, get indicies of degenerate eigvals ttol = max(tol, tol * abs(eigvals[k])) inds, = np.where(abs(eigvals - eigvals[k]) < ttol) if len(inds) > 1: # if at least 2 eigvals are degenerate eigvecs[:, inds] = _degen(tol, eigvecs[:, inds], ops, 1) k = inds[-1] + 1 for k in range(N): eigvecs[:, k] = eigvecs[:, k] / la.norm(eigvecs[:, k]) kets_out = [ Qobj(eigvecs[:, j], dims=[ops[0].dims[0], [1]]) for j in range(N) ] eigvals_out = np.zeros((len(ops), N), dtype=np.float64) if not evals: return kets_out else: for kk in range(len(ops)): for j in range(N): eigvals_out[kk, j] = ops[kk].matrix_element(kets_out[j], kets_out[j]).real return eigvals_out, kets_out