def test_hilbert_dist():
"""
Metrics: Hilbert distance.
"""
diag1 = np.array([0.5, 0.5, 0, 0])
diag2 = np.array([0, 0, 0.5, 0.5])
r1 = qdiags(diag1, 0)
r2 = qdiags(diag2, 0)
assert_(abs(hilbert_dist(r1, r2) - 1) <= 1e-6)
def test_hilbert_dist():
"""
Metrics: Hilbert distance.
"""
diag1 = np.array([0.5, 0.5, 0, 0])
diag2 = np.array([0, 0, 0.5, 0.5])
r1 = qdiags(diag1, 0)
r2 = qdiags(diag2, 0)
assert_(abs(hilbert_dist(r1, r2)-1) <= 1e-6)
def bloch_redfield_tensor(H, a_ops, spectra_cb=None, c_ops=[], use_secular=True, sec_cutoff=0.1):
"""
Calculate the Bloch-Redfield tensor for a system given a set of operators
and corresponding spectral functions that describes the system's coupling
to its environment.
.. note::
This tensor generation requires a time-independent Hamiltonian.
Parameters
----------
H : :class:`qutip.qobj`
System Hamiltonian.
a_ops : list of :class:`qutip.qobj`
List of system operators that couple to the environment.
spectra_cb : list of callback functions
List of callback functions that evaluate the noise power spectrum
at a given frequency.
c_ops : list of :class:`qutip.qobj`
List of system collapse operators.
use_secular : bool
Flag (True of False) that indicates if the secular approximation should
be used.
sec_cutoff : float {0.1}
Threshold for secular approximation.
Returns
-------
R, kets: :class:`qutip.Qobj`, list of :class:`qutip.Qobj`
R is the Bloch-Redfield tensor and kets is a list eigenstates of the
Hamiltonian.
"""
if not (spectra_cb is None):
warnings.warn("The use of spectra_cb is depreciated.", DeprecationWarning)
_a_ops = []
for kk, a in enumerate(a_ops):
_a_ops.append([a,spectra_cb[kk]])
a_ops = _a_ops
# Sanity checks for input parameters
if not isinstance(H, Qobj):
raise TypeError("H must be an instance of Qobj")
for a in a_ops:
if not isinstance(a[0], Qobj) or not a[0].isherm:
raise TypeError("Operators in a_ops must be Hermitian Qobj.")
if c_ops is None:
c_ops = []
# use the eigenbasis
evals, ekets = H.eigenstates()
N = len(evals)
K = len(a_ops)
#only Lindblad collapse terms
if K==0:
Heb = qdiags(evals,0,dims=H.dims)
L = liouvillian(Heb, c_ops=[c_op.transform(ekets) for c_op in c_ops])
return L, ekets
A = np.array([a_ops[k][0].transform(ekets).full() for k in range(K)])
Jw = np.zeros((K, N, N), dtype=complex)
# pre-calculate matrix elements and spectral densities
# W[m,n] = real(evals[m] - evals[n])
W = np.real(evals[:,np.newaxis] - evals[np.newaxis,:])
for k in range(K):
# do explicit loops here in case spectra_cb[k] can not deal with array arguments
for n in range(N):
for m in range(N):
Jw[k, n, m] = a_ops[k][1](W[n, m])
dw_min = np.abs(W[W.nonzero()]).min()
# pre-calculate mapping between global index I and system indices a,b
Iabs = np.empty((N*N,3),dtype=int)
for I, Iab in enumerate(Iabs):
# important: use [:] to change array values, instead of creating new variable Iab
Iab[0] = I
Iab[1:] = vec2mat_index(N, I)
# unitary part + dissipation from c_ops (if given):
Heb = qdiags(evals,0,dims=H.dims)
L = liouvillian(Heb, c_ops=[c_op.transform(ekets) for c_op in c_ops])
# dissipative part:
rows = []
cols = []
data = []
for I, a, b in Iabs:
# only check use_secular once per I
if use_secular:
# only loop over those indices J which actually contribute
Jcds = Iabs[np.where(np.abs(W[a, b] - W[Iabs[:,1], Iabs[:,2]]) < dw_min * sec_cutoff)]
else:
Jcds = Iabs
for J, c, d in Jcds:
elem = 0+0j
# summed over k, i.e., each operator coupling the system to the environment
elem += 0.5 * np.sum(A[:, a, c] * A[:, d, b] * (Jw[:, c, a] + Jw[:, d, b]))
if b==d:
# sum_{k,n} A[k, a, n] * A[k, n, c] * Jw[k, c, n])
elem -= 0.5 * np.sum(A[:, a, :] * A[:, :, c] * Jw[:, c, :])
if a==c:
# sum_{k,n} A[k, d, n] * A[k, n, b] * Jw[k, d, n])
elem -= 0.5 * np.sum(A[:, d, :] * A[:, :, b] * Jw[:, d, :])
if elem != 0:
rows.append(I)
cols.append(J)
data.append(elem)
R = arr_coo2fast(np.array(data, dtype=complex),
np.array(rows, dtype=np.int32),
np.array(cols, dtype=np.int32), N**2, N**2)
L.data = L.data + R
return L, ekets