lime/oqs.py
def correlation_3op_2t(self, rho0, ops, dt, Nt, Ntau):
        """
        Internal function for calculating the three-operator two-time
        correlation function:
        <A(t)B(t+tau)C(t)>
        using the Linblad master equation solver.
        """

        # the solvers only work for positive time differences and the correlators
        # require positive tau
        # if state0 is None:
        #     rho0 = steadystate(H, c_ops)
        #     tlist = [0]
        # elif isket(state0):
        #     rho0 = ket2dm(state0)
        # else:
        #     rho0 = state0
        H = self.H
        c_ops = self.c_ops
        rho_t = _lindblad(H, rho0, c_ops, dt=dt, Nt=Nt, return_result=True).rholist

        a_op, b_op, c_op = ops

        corr_mat = np.zeros([Nt, Ntau], dtype=complex)

        for t_idx, rho in enumerate(rho_t):

            corr_mat[t_idx, :] = _lindblad(H, rho0=c_op @ rho @ a_op, \
                                           dt=dt, Nt=Ntau, c_ops=c_ops,\
                e_ops=[b_op], return_result=True).observables[:,0]

        return corr_mat

        Internal function for calculating the three-operator two-time
        correlation function:
        <A(t)B(t+tau)C(t)>
        using the Linblad master equation solver.
        
Internal function for calculating the three-operator two-time
correlation function:

using the Linblad master equation solver.
Internal function for calculating the three - operator two - time correlation function : using the Linblad master equation solver .
def correlation_3op_2t(self, rho0, ops, dt, Nt, Ntau):
        H = self.H
        c_ops = self.c_ops
        rho_t = _lindblad(H, rho0, c_ops, dt=dt, Nt=Nt, return_result=True).rholist
        a_op, b_op, c_op = ops
        corr_mat = np.zeros([Nt, Ntau], dtype=complex)
        for t_idx, rho in enumerate(rho_t):
            corr_mat[t_idx, :] = _lindblad(H, rho0=c_op @ rho @ a_op, \
                                           dt=dt, Nt=Ntau, c_ops=c_ops,\
                e_ops=[b_op], return_result=True).observables[:,0]
        return corr_mat
def correlation_3op_2t ( self , rho0 , ops , dt , Nt , Ntau ) : H = self . H c_ops = self . c_ops rho_t = _lindblad ( H , rho0 , c_ops , dt = dt , Nt = Nt , return_result = True ) . rholist a_op , b_op , c_op = ops corr_mat = np . zeros ( [ Nt , Ntau ] , dtype = complex ) for t_idx , rho in enumerate ( rho_t ) : corr_mat [ t_idx , : ] = _lindblad ( H , rho0 = c_op @ rho @ a_op , dt = dt , Nt = Ntau , c_ops = c_ops , e_ops = [ b_op ] , return_result = True ) . observables [ : , 0 ] return corr_mat
Internal function for calculating the three-operator two-time
correlation function:
<A(t)B(t+tau)C(t)>
using the Linblad master equation solver.
Internal function for calculating the three - operator two - time correlation function : <A ( t ) B ( t + tau ) C ( t ) > using the Linblad master equation solver .
"""
        Internal function for calculating the three-operator two-time
        correlation function:
        <A(t)B(t+tau)C(t)>
        using the Linblad master equation solver.
        """ # the solvers only work for positive time differences and the correlators # require positive tau # if state0 is None: #     rho0 = steadystate(H, c_ops) #     tlist = [0] # elif isket(state0): #     rho0 = ket2dm(state0) # else: #     rho0 = state0
self
rho0
ops
dt
Nt
Ntau