def countstat_current_noise(L, c_ops, wlist=None, rhoss=None, J_ops=None,
sparse=True, method='direct'):
"""
Compute the cross-current noise spectrum for a list of collapse operators
`c_ops` corresponding to monitored currents, given the system
Liouvillian `L`. The current collapse operators `c_ops` should be part
of the dissipative processes in `L`, but the `c_ops` given here does not
necessarily need to be all collapse operators contributing to dissipation
in the Liouvillian. Optionally, the steadystate density matrix `rhoss`
and the current operators `J_ops` correpsonding to the current collapse
operators `c_ops` can also be specified. If either of
`rhoss` and `J_ops` are omitted, they will be computed internally.
'wlist' is an optional list of frequencies at which to evaluate the noise
spectrum.
Note:
The default method is a direct solution using dense matrices, as sparse
matrix methods fail for some examples of small systems.
For larger systems it is reccomended to use the sparse solver
with the direct method, as it avoids explicit calculation of the
pseudo-inverse, as described in page 67 of "Electrons in nanostructures"
C. Flindt, PhD Thesis, available online:
http://orbit.dtu.dk/fedora/objects/orbit:82314/datastreams/file_4732600/content
Parameters
----------
L : :class:`qutip.Qobj`
Qobj representing the system Liouvillian.
c_ops : array / list
List of current collapse operators.
rhoss : :class:`qutip.Qobj` (optional)
The steadystate density matrix corresponding the system Liouvillian
`L`.
wlist : array / list (optional)
List of frequencies at which to evaluate (if none are given, evaluates
at zero frequency)
J_ops : array / list (optional)
List of current superoperators.
sparse : bool
Flag that indicates whether to use sparse or dense matrix methods when
computing the pseudo inverse. Default is false, as sparse solvers
can fail for small systems. For larger systems the sparse solvers
are reccomended.
Returns
--------
I, S : tuple of arrays
The currents `I` corresponding to each current collapse operator
`c_ops` (or, equivalently, each current superopeator `J_ops`) and the
zero-frequency cross-current correlation `S`.
"""
if rhoss is None:
rhoss = steadystate(L, c_ops)
if J_ops is None:
J_ops = [sprepost(c, c.dag()) for c in c_ops]
N = len(J_ops)
I = np.zeros(N)
if wlist is None:
S = np.zeros((N, N,1))
wlist=[0.]
else:
S = np.zeros((N, N,len(wlist)))
if sparse == False:
rhoss_vec = mat2vec(rhoss.full()).ravel()
for k,w in enumerate(wlist):
R = pseudo_inverse(L, rhoss=rhoss, w= w, sparse = sparse, method=method)
for i, Ji in enumerate(J_ops):
for j, Jj in enumerate(J_ops):
if i == j:
I[i] = expect_rho_vec(Ji.data, rhoss_vec, 1)
S[i, j,k] = I[i]
S[i, j,k] -= expect_rho_vec((Ji * R * Jj
+ Jj * R * Ji).data,
rhoss_vec, 1)
else:
if method == "direct":
N = np.prod(L.dims[0][0])
rhoss_vec = operator_to_vector(rhoss)
tr_op = tensor([identity(n) for n in L.dims[0][0]])
tr_op_vec = operator_to_vector(tr_op)
Pop = sp.kron(rhoss_vec.data, tr_op_vec.data.T, format='csr')
Iop = sp.eye(N*N, N*N, format='csr')
Q = Iop - Pop
for k,w in enumerate(wlist):
if w != 0.0:
L_temp = 1.0j*w*spre(tr_op) + L
else: #At zero frequency some solvers fail for small systems.
#Adding a small finite frequency of order 1e-15
#helps prevent the solvers from throwing an exception.
L_temp = 1.0j*(1e-15)*spre(tr_op) + L
if not settings.has_mkl:
A = L_temp.data.tocsc()
else:
A = L_temp.data.tocsr()
A.sort_indices()
rhoss_vec = mat2vec(rhoss.full()).ravel()
for j, Jj in enumerate(J_ops):
Qj = Q.dot( Jj.data.dot( rhoss_vec))
try:
if settings.has_mkl:
X_rho_vec_j = mkl_spsolve(A,Qj)
else:
X_rho_vec_j = sp.linalg.splu(A, permc_spec
='COLAMD').solve(Qj)
except:
X_rho_vec_j = sp.linalg.lsqr(A,Qj)[0]
for i, Ji in enumerate(J_ops):
Qi = Q.dot( Ji.data.dot(rhoss_vec))
try:
if settings.has_mkl:
X_rho_vec_i = mkl_spsolve(A,Qi)
else:
X_rho_vec_i = sp.linalg.splu(A, permc_spec
='COLAMD').solve(Qi)
except:
X_rho_vec_i = sp.linalg.lsqr(A,Qi)[0]
if i == j:
I[i] = expect_rho_vec(Ji.data,
rhoss_vec, 1)
S[j, i, k] = I[i]
S[j, i, k] -= (expect_rho_vec(Jj.data * Q,
X_rho_vec_i, 1)
+ expect_rho_vec(Ji.data * Q,
X_rho_vec_j, 1))
else:
rhoss_vec = mat2vec(rhoss.full()).ravel()
for k,w in enumerate(wlist):
R = pseudo_inverse(L,rhoss=rhoss, w= w, sparse = sparse,
method=method)
for i, Ji in enumerate(J_ops):
for j, Jj in enumerate(J_ops):
if i == j:
I[i] = expect_rho_vec(Ji.data, rhoss_vec, 1)
S[i, j, k] = I[i]
S[i, j, k] -= expect_rho_vec((Ji * R * Jj
+ Jj * R * Ji).data,
rhoss_vec, 1)
return I, S
def countstat_current_noise(L,
c_ops,
wlist=None,
rhoss=None,
J_ops=None,
sparse=True,
method='direct'):
"""
Compute the cross-current noise spectrum for a list of collapse operators
`c_ops` corresponding to monitored currents, given the system
Liouvillian `L`. The current collapse operators `c_ops` should be part
of the dissipative processes in `L`, but the `c_ops` given here does not
necessarily need to be all collapse operators contributing to dissipation
in the Liouvillian. Optionally, the steadystate density matrix `rhoss`
and the current operators `J_ops` correpsonding to the current collapse
operators `c_ops` can also be specified. If either of
`rhoss` and `J_ops` are omitted, they will be computed internally.
'wlist' is an optional list of frequencies at which to evaluate the noise
spectrum.
Note:
The default method is a direct solution using dense matrices, as sparse
matrix methods fail for some examples of small systems.
For larger systems it is reccomended to use the sparse solver
with the direct method, as it avoids explicit calculation of the
pseudo-inverse, as described in page 67 of "Electrons in nanostructures"
C. Flindt, PhD Thesis, available online:
http://orbit.dtu.dk/fedora/objects/orbit:82314/datastreams/file_4732600/content
Parameters
----------
L : :class:`qutip.Qobj`
Qobj representing the system Liouvillian.
c_ops : array / list
List of current collapse operators.
rhoss : :class:`qutip.Qobj` (optional)
The steadystate density matrix corresponding the system Liouvillian
`L`.
wlist : array / list (optional)
List of frequencies at which to evaluate (if none are given, evaluates
at zero frequency)
J_ops : array / list (optional)
List of current superoperators.
sparse : bool
Flag that indicates whether to use sparse or dense matrix methods when
computing the pseudo inverse. Default is false, as sparse solvers
can fail for small systems. For larger systems the sparse solvers
are reccomended.
Returns
--------
I, S : tuple of arrays
The currents `I` corresponding to each current collapse operator
`c_ops` (or, equivalently, each current superopeator `J_ops`) and the
zero-frequency cross-current correlation `S`.
"""
if rhoss is None:
rhoss = steadystate(L, c_ops)
if J_ops is None:
J_ops = [sprepost(c, c.dag()) for c in c_ops]
N = len(J_ops)
I = np.zeros(N)
if wlist is None:
S = np.zeros((N, N, 1))
wlist = [0.]
else:
S = np.zeros((N, N, len(wlist)))
if sparse == False:
rhoss_vec = mat2vec(rhoss.full()).ravel()
for k, w in enumerate(wlist):
R = pseudo_inverse(L,
rhoss=rhoss,
w=w,
sparse=sparse,
method=method)
for i, Ji in enumerate(J_ops):
for j, Jj in enumerate(J_ops):
if i == j:
I[i] = expect_rho_vec(Ji.data, rhoss_vec, 1)
S[i, j, k] = I[i]
S[i, j, k] -= expect_rho_vec(
(Ji * R * Jj + Jj * R * Ji).data, rhoss_vec, 1)
else:
if method == "direct":
N = np.prod(L.dims[0][0])
rhoss_vec = operator_to_vector(rhoss)
tr_op = tensor([identity(n) for n in L.dims[0][0]])
tr_op_vec = operator_to_vector(tr_op)
Pop = sp.kron(rhoss_vec.data, tr_op_vec.data.T, format='csr')
Iop = sp.eye(N * N, N * N, format='csr')
Q = Iop - Pop
for k, w in enumerate(wlist):
if w != 0.0:
L_temp = 1.0j * w * spre(tr_op) + L
else: #At zero frequency some solvers fail for small systems.
#Adding a small finite frequency of order 1e-15
#helps prevent the solvers from throwing an exception.
L_temp = 1.0j * (1e-15) * spre(tr_op) + L
if not settings.has_mkl:
A = L_temp.data.tocsc()
else:
A = L_temp.data.tocsr()
A.sort_indices()
rhoss_vec = mat2vec(rhoss.full()).ravel()
for j, Jj in enumerate(J_ops):
Qj = Q.dot(Jj.data.dot(rhoss_vec))
try:
if settings.has_mkl:
X_rho_vec_j = mkl_spsolve(A, Qj)
else:
X_rho_vec_j = sp.linalg.splu(
A, permc_spec='COLAMD').solve(Qj)
except:
X_rho_vec_j = sp.linalg.lsqr(A, Qj)[0]
for i, Ji in enumerate(J_ops):
Qi = Q.dot(Ji.data.dot(rhoss_vec))
try:
if settings.has_mkl:
X_rho_vec_i = mkl_spsolve(A, Qi)
else:
X_rho_vec_i = sp.linalg.splu(
A, permc_spec='COLAMD').solve(Qi)
except:
X_rho_vec_i = sp.linalg.lsqr(A, Qi)[0]
if i == j:
I[i] = expect_rho_vec(Ji.data, rhoss_vec, 1)
S[j, i, k] = I[i]
S[j, i,
k] -= (expect_rho_vec(Jj.data * Q, X_rho_vec_i, 1) +
expect_rho_vec(Ji.data * Q, X_rho_vec_j, 1))
else:
rhoss_vec = mat2vec(rhoss.full()).ravel()
for k, w in enumerate(wlist):
R = pseudo_inverse(L,
rhoss=rhoss,
w=w,
sparse=sparse,
method=method)
for i, Ji in enumerate(J_ops):
for j, Jj in enumerate(J_ops):
if i == j:
I[i] = expect_rho_vec(Ji.data, rhoss_vec, 1)
S[i, j, k] = I[i]
S[i, j, k] -= expect_rho_vec(
(Ji * R * Jj + Jj * R * Ji).data, rhoss_vec, 1)
return I, S
def countstat_current_noise(L, c_ops, rhoss=None, J_ops=None, R=False):
"""
Compute the cross-current noise spectrum for a list of collapse operators
`c_ops` corresponding to monitored currents, given the system
Liouvillian `L`. The current collapse operators `c_ops` should be part
of the dissipative processes in `L`, but the `c_ops` given here does not
necessarily need to be all collapse operators contributing to dissipation
in the Liouvillian. Optionally, the steadystate density matrix `rhoss`
and/or the pseudo inverse `R` of the Liouvillian `L`, and the current
operators `J_ops` correpsonding to the current collapse operators `c_ops`
can also be specified. If `R` is not given, the cross-current correlations
will be computed directly without computing `R` explicitly. If either of
`rhoss` and `J_ops` are omitted, they will be computed internally.
Parameters
----------
L : :class:`qutip.Qobj`
Qobj representing the system Liouvillian.
c_ops : array / list
List of current collapse operators.
rhoss : :class:`qutip.Qobj` (optional)
The steadystate density matrix corresponding the system Liouvillian
`L`.
J_ops : array / list (optional)
List of current superoperators.
R : :class:`qutip.Qobj` (optional)
Qobj representing the pseudo inverse of the system Liouvillian `L`.
Returns
--------
I, S : tuple of arrays
The currents `I` corresponding to each current collapse operator
`c_ops` (or, equivalently, each current superopeator `J_ops`) and the
zero-frequency cross-current correlation `S`.
"""
if rhoss is None:
rhoss = steadystate(L, c_ops)
if J_ops is None:
J_ops = [sprepost(c, c.dag()) for c in c_ops]
rhoss_vec = mat2vec(rhoss.full()).ravel()
N = len(J_ops)
I = np.zeros(N)
S = np.zeros((N, N))
if R:
if R is True:
R = pseudo_inverse(L, rhoss)
for i, Ji in enumerate(J_ops):
for j, Jj in enumerate(J_ops):
if i == j:
I[i] = expect_rho_vec(Ji.data, rhoss_vec, 1)
S[i, j] = I[i]
S[i, j] -= expect_rho_vec((Ji * R * Jj + Jj * R * Ji).data,
rhoss_vec, 1)
else:
N = np.prod(L.dims[0][0])
rhoss_vec = operator_to_vector(rhoss)
tr_op = tensor([identity(n) for n in L.dims[0][0]])
tr_op_vec = operator_to_vector(tr_op)
Pop = sp.kron(rhoss_vec.data, tr_op_vec.data.T, format='csc')
Iop = sp.eye(N*N, N*N, format='csc')
Q = Iop - Pop
A = L.data.tocsc()
rhoss_vec = mat2vec(rhoss.full()).ravel()
for j, Jj in enumerate(J_ops):
Qj = Q * Jj.data * rhoss_vec
X_rho_vec = sp.linalg.splu(A, permc_spec='COLAMD').solve(Qj)
for i, Ji in enumerate(J_ops):
if i == j:
S[i, i] = I[i] = expect_rho_vec(Ji.data, rhoss_vec, 1)
S[i, j] -= expect_rho_vec(Ji.data * Q, X_rho_vec, 1)
return I, S
