def __init__( self, H_sys, bath, max_depth, options=None, progress_bar=None, ): self.H_sys = self._convert_h_sys(H_sys) self.options = Options() if options is None else options self._is_timedep = isinstance(self.H_sys, QobjEvo) self._H0 = self.H_sys.to_list()[0] if self._is_timedep else self.H_sys self._is_hamiltonian = self._H0.type == "oper" self._L0 = liouvillian(self._H0) if self._is_hamiltonian else self._H0 self._sys_shape = (self._H0.shape[0] if self._is_hamiltonian else int( np.sqrt(self._H0.shape[0]))) self._sup_shape = self._L0.shape[0] self._sys_dims = (self._H0.dims if self._is_hamiltonian else self._H0.dims[0]) self.ados = HierarchyADOs( self._combine_bath_exponents(bath), max_depth, ) self._n_ados = len(self.ados.labels) self._n_exponents = len(self.ados.exponents) # pre-calculate identity matrix required by _grad_n self._sId = fast_identity(self._sup_shape) # pre-calculate superoperators required by _grad_prev and _grad_next: Qs = [exp.Q for exp in self.ados.exponents] self._spreQ = [spre(op).data for op in Qs] self._spostQ = [spost(op).data for op in Qs] self._s_pre_minus_post_Q = [ self._spreQ[k] - self._spostQ[k] for k in range(self._n_exponents) ] self._s_pre_plus_post_Q = [ self._spreQ[k] + self._spostQ[k] for k in range(self._n_exponents) ] self._spreQdag = [spre(op.dag()).data for op in Qs] self._spostQdag = [spost(op.dag()).data for op in Qs] self._s_pre_minus_post_Qdag = [ self._spreQdag[k] - self._spostQdag[k] for k in range(self._n_exponents) ] self._s_pre_plus_post_Qdag = [ self._spreQdag[k] + self._spostQdag[k] for k in range(self._n_exponents) ] if progress_bar is None: self.progress_bar = BaseProgressBar() if progress_bar is True: self.progress_bar = TextProgressBar() self._configure_solver()
def _check_progress_bar(progress_bar): """ Check instance of progress_bar and return the object. """ if progress_bar is None: pbar = BaseProgressBar() if progress_bar is True: pbar = TextProgressBar() return pbar
def serial_map(task, values, task_args=tuple(), task_kwargs={}, **kwargs): """ Serial mapping function with the same call signature as parallel_map, for easy switching between serial and parallel execution. This is functionally equivalent to: result = [task(value, *task_args, **task_kwargs) for value in values] This function work as a drop-in replacement of :func:`qutip.parallel_map`. Parameters ---------- task: a Python function The function that is to be called for each value in ``task_vec``. values: array / list The list or array of values for which the ``task`` function is to be evaluated. task_args: list / dictionary The optional additional argument to the ``task`` function. task_kwargs: list / dictionary The optional additional keyword argument to the ``task`` function. progress_bar: ProgressBar Progress bar class instance for showing progress. Returns -------- result : list The result list contains the value of ``task(value, *task_args, **task_kwargs)`` for each value in ``values``. """ try: progress_bar = kwargs['progress_bar'] if progress_bar is True: progress_bar = TextProgressBar() except: progress_bar = BaseProgressBar() progress_bar.start(len(values)) results = [] for n, value in enumerate(values): progress_bar.update(n) result = task(value, *task_args, **task_kwargs) results.append(result) progress_bar.finished() return results
def __init__(self, H_sys, coup_op, coup_strength, temperature, N_cut, N_exp, cut_freq, planck=1.0, boltzmann=1.0, renorm=True, bnd_cut_approx=True, options=None, progress_bar=None, stats=None): self.reset() if options is None: self.options = Options() else: self.options = options self.progress_bar = False if progress_bar is None: self.progress_bar = BaseProgressBar() elif progress_bar == True: self.progress_bar = TextProgressBar() # the other attributes will be set in the configure method self.configure(H_sys, coup_op, coup_strength, temperature, N_cut, N_exp, cut_freq, planck=planck, boltzmann=boltzmann, renorm=renorm, bnd_cut_approx=bnd_cut_approx, stats=stats)
def propagator(H, t, c_op_list, args=None, options=None, sparse=False, progress_bar=None): """ Calculate the propagator U(t) for the density matrix or wave function such that :math:`\psi(t) = U(t)\psi(0)` or :math:`\\rho_{\mathrm vec}(t) = U(t) \\rho_{\mathrm vec}(0)` where :math:`\\rho_{\mathrm vec}` is the vector representation of the density matrix. Parameters ---------- H : qobj or list Hamiltonian as a Qobj instance of a nested list of Qobjs and coefficients in the list-string or list-function format for time-dependent Hamiltonians (see description in :func:`qutip.mesolve`). t : float or array-like Time or list of times for which to evaluate the propagator. c_op_list : list List of qobj collapse operators. args : list/array/dictionary Parameters to callback functions for time-dependent Hamiltonians and collapse operators. options : :class:`qutip.Options` with options for the ODE solver. progress_bar: BaseProgressBar Optional instance of BaseProgressBar, or a subclass thereof, for showing the progress of the simulation. By default no progress bar is used, and if set to True a TextProgressBar will be used. Returns ------- a : qobj Instance representing the propagator :math:`U(t)`. """ if progress_bar is None: progress_bar = BaseProgressBar() elif progress_bar is True: progress_bar = TextProgressBar() if options is None: options = Options() options.rhs_reuse = True rhs_clear() if isinstance(t, (int, float, np.integer, np.floating)): tlist = [0, t] else: tlist = t if isinstance(H, (types.FunctionType, types.BuiltinFunctionType, functools.partial)): H0 = H(0.0, args) elif isinstance(H, list): H0 = H[0][0] if isinstance(H[0], list) else H[0] else: H0 = H if len(c_op_list) == 0 and H0.isoper: # calculate propagator for the wave function N = H0.shape[0] dims = H0.dims u = np.zeros([N, N, len(tlist)], dtype=complex) progress_bar.start(N) for n in range(0, N): progress_bar.update(n) psi0 = basis(N, n) output = sesolve(H, psi0, tlist, [], args, options) for k, t in enumerate(tlist): u[:, n, k] = output.states[k].full().T progress_bar.finished() # todo: evolving a batch of wave functions: # psi_0_list = [basis(N, n) for n in range(N)] # psi_t_list = mesolve(H, psi_0_list, [0, t], [], [], args, options) # for n in range(0, N): # u[:,n] = psi_t_list[n][1].full().T elif len(c_op_list) == 0 and H0.issuper: # calculate the propagator for the vector representation of the # density matrix (a superoperator propagator) N = H0.shape[0] dims = H0.dims u = np.zeros([N, N, len(tlist)], dtype=complex) progress_bar.start(N) for n in range(0, N): progress_bar.update(n) psi0 = basis(N, n) rho0 = Qobj(vec2mat(psi0.full())) output = mesolve(H, rho0, tlist, [], [], args, options) for k, t in enumerate(tlist): u[:, n, k] = mat2vec(output.states[k].full()).T progress_bar.finished() else: # calculate the propagator for the vector representation of the # density matrix (a superoperator propagator) N = H0.shape[0] dims = [H0.dims, H0.dims] u = np.zeros([N * N, N * N, len(tlist)], dtype=complex) if sparse: progress_bar.start(N * N) for n in range(N * N): progress_bar.update(n) psi0 = basis(N * N, n) psi0.dims = [dims[0], 1] rho0 = vector_to_operator(psi0) output = mesolve(H, rho0, tlist, c_op_list, [], args, options) for k, t in enumerate(tlist): u[:, n, k] = operator_to_vector(output.states[k]).full(squeeze=True) progress_bar.finished() else: progress_bar.start(N * N) for n in range(N * N): progress_bar.update(n) psi0 = basis(N * N, n) rho0 = Qobj(vec2mat(psi0.full())) output = mesolve(H, rho0, tlist, c_op_list, [], args, options) for k, t in enumerate(tlist): u[:, n, k] = mat2vec(output.states[k].full()).T progress_bar.finished() if len(tlist) == 2: return Qobj(u[:, :, 1], dims=dims) else: return [Qobj(u[:, :, k], dims=dims) for k in range(len(tlist))]
def mesolve(H, rho0, tlist, c_ops=None, e_ops=None, args=None, options=None, progress_bar=None, _safe_mode=True): """ Master equation evolution of a density matrix for a given Hamiltonian and set of collapse operators, or a Liouvillian. Evolve the state vector or density matrix (`rho0`) using a given Hamiltonian or Liouvillian (`H`) and an optional set of collapse operators (`c_ops`), by integrating the set of ordinary differential equations that define the system. In the absence of collapse operators the system is evolved according to the unitary evolution of the Hamiltonian. The output is either the state vector at arbitrary points in time (`tlist`), or the expectation values of the supplied operators (`e_ops`). If e_ops is a callback function, it is invoked for each time in `tlist` with time and the state as arguments, and the function does not use any return values. If either `H` or the Qobj elements in `c_ops` are superoperators, they will be treated as direct contributions to the total system Liouvillian. This allows the solution of master equations that are not in standard Lindblad form. **Time-dependent operators** For time-dependent problems, `H` and `c_ops` can be a specified in a nested-list format where each element in the list is a list of length 2, containing an operator (:class:`qutip.qobj`) at the first element and where the second element is either a string (*list string format*), a callback function (*list callback format*) that evaluates to the time-dependent coefficient for the corresponding operator, or a NumPy array (*list array format*) which specifies the value of the coefficient to the corresponding operator for each value of t in `tlist`. Alternatively, `H` (but not `c_ops`) can be a callback function with the signature `f(t, args) -> Qobj` (*callback format*), which can return the Hamiltonian or Liouvillian superoperator at any point in time. If the equation cannot be put in standard Lindblad form, then this time-dependence format must be used. *Examples* H = [[H0, 'sin(w*t)'], [H1, 'sin(2*w*t)']] H = [[H0, f0_t], [H1, f1_t]] where f0_t and f1_t are python functions with signature f_t(t, args). H = [[H0, np.sin(w*tlist)], [H1, np.sin(2*w*tlist)]] In the *list string format* and *list callback format*, the string expression and the callback function must evaluate to a real or complex number (coefficient for the corresponding operator). In all cases of time-dependent operators, `args` is a dictionary of parameters that is used when evaluating operators. It is passed to the callback functions as their second argument. **Additional options** Additional options to mesolve can be set via the `options` argument, which should be an instance of :class:`qutip.solver.Options`. Many ODE integration options can be set this way, and the `store_states` and `store_final_state` options can be used to store states even though expectation values are requested via the `e_ops` argument. .. note:: If an element in the list-specification of the Hamiltonian or the list of collapse operators are in superoperator form it will be added to the total Liouvillian of the problem without further transformation. This allows for using mesolve for solving master equations that are not in standard Lindblad form. .. note:: On using callback functions: mesolve transforms all :class:`qutip.Qobj` objects to sparse matrices before handing the problem to the integrator function. In order for your callback function to work correctly, pass all :class:`qutip.Qobj` objects that are used in constructing the Hamiltonian via `args`. mesolve will check for :class:`qutip.Qobj` in `args` and handle the conversion to sparse matrices. All other :class:`qutip.Qobj` objects that are not passed via `args` will be passed on to the integrator in scipy which will raise a NotImplemented exception. Parameters ---------- H : :class:`qutip.Qobj` System Hamiltonian, or a callback function for time-dependent Hamiltonians, or alternatively a system Liouvillian. rho0 : :class:`qutip.Qobj` initial density matrix or state vector (ket). tlist : *list* / *array* list of times for :math:`t`. c_ops : None / list of :class:`qutip.Qobj` single collapse operator, or list of collapse operators, or a list of Liouvillian superoperators. e_ops : None / list / callback function, optional A list of operators as `Qobj` and/or callable functions (can be mixed) or a single callable function. For operators, the result's expect will be computed by :func:`qutip.expect`. For callable functions, they are called as ``f(t, state)`` and return the expectation value. A single callback's expectation value can be any type, but a callback part of a list must return a number as the expectation value. args : None / *dictionary* dictionary of parameters for time-dependent Hamiltonians and collapse operators. options : None / :class:`qutip.Options` with options for the solver. progress_bar : None / BaseProgressBar Optional instance of BaseProgressBar, or a subclass thereof, for showing the progress of the simulation. Returns ------- result: :class:`qutip.Result` An instance of the class :class:`qutip.Result`, which contains either an *array* `result.expect` of expectation values for the times specified by `tlist`, or an *array* `result.states` of state vectors or density matrices corresponding to the times in `tlist` [if `e_ops` is an empty list], or nothing if a callback function was given in place of operators for which to calculate the expectation values. """ if c_ops is None: c_ops = [] if isinstance(c_ops, (Qobj, QobjEvo)): c_ops = [c_ops] if e_ops is None: e_ops = [] if isinstance(e_ops, Qobj): e_ops = [e_ops] if isinstance(e_ops, dict): e_ops_dict = e_ops e_ops = [e for e in e_ops.values()] else: e_ops_dict = None if progress_bar is None: progress_bar = BaseProgressBar() if progress_bar is True: progress_bar = TextProgressBar() # check if rho0 is a superoperator, in which case e_ops argument should # be empty, i.e., e_ops = [] # TODO: e_ops for superoperator if issuper(rho0) and not e_ops == []: raise TypeError("Must have e_ops = [] when initial condition rho0 is" + " a superoperator.") if options is None: options = Options() if options.rhs_reuse and not isinstance(H, SolverSystem): # TODO: deprecate when going to class based solver. if "mesolve" in solver_safe: # print(" ") H = solver_safe["mesolve"] else: pass # raise Exception("Could not find the Hamiltonian to reuse.") if args is None: args = {} check_use_openmp(options) use_mesolve = ((c_ops and len(c_ops) > 0) or (not isket(rho0)) or (isinstance(H, Qobj) and issuper(H)) or (isinstance(H, QobjEvo) and issuper(H.cte)) or (isinstance(H, list) and isinstance(H[0], Qobj) and issuper(H[0])) or (not isinstance(H, (Qobj, QobjEvo)) and callable(H) and not options.rhs_with_state and issuper(H(0., args))) or (not isinstance(H, (Qobj, QobjEvo)) and callable(H) and options.rhs_with_state)) if not use_mesolve: return sesolve(H, rho0, tlist, e_ops=e_ops, args=args, options=options, progress_bar=progress_bar, _safe_mode=_safe_mode) if isket(rho0): rho0 = ket2dm(rho0) if (not (rho0.isoper or rho0.issuper)) or (rho0.dims[0] != rho0.dims[1]): raise ValueError( "input state must be a pure state vector, square density matrix, " "or superoperator") if isinstance(H, SolverSystem): ss = H elif isinstance(H, (list, Qobj, QobjEvo)): ss = _mesolve_QobjEvo(H, c_ops, tlist, args, options) elif callable(H): ss = _mesolve_func_td(H, c_ops, rho0, tlist, args, options) else: raise Exception("Invalid H type") func, ode_args = ss.makefunc(ss, rho0, args, e_ops, options) if _safe_mode: # This is to test safety of the function before starting the loop. v = rho0.full().ravel('F') func(0., v, *ode_args) + v res = _generic_ode_solve(func, ode_args, rho0, tlist, e_ops, options, progress_bar, dims=rho0.dims) res.num_collapse = len(c_ops) if e_ops_dict: res.expect = { e: res.expect[n] for n, e in enumerate(e_ops_dict.keys()) } return res
def bloch_redfield_solve(R, ekets, rho0, tlist, e_ops=[], options=None, progress_bar=None): """ Evolve the ODEs defined by Bloch-Redfield master equation. The Bloch-Redfield tensor can be calculated by the function :func:`bloch_redfield_tensor`. Parameters ---------- R : :class:`qutip.qobj` Bloch-Redfield tensor. ekets : array of :class:`qutip.qobj` Array of kets that make up a basis tranformation for the eigenbasis. rho0 : :class:`qutip.qobj` Initial density matrix. tlist : *list* / *array* List of times for :math:`t`. e_ops : list of :class:`qutip.qobj` / callback function List of operators for which to evaluate expectation values. options : :class:`qutip.Qdeoptions` Options for the ODE solver. Returns ------- output: :class:`qutip.solver` An instance of the class :class:`qutip.solver`, which contains either an *array* of expectation values for the times specified by `tlist`. """ if options is None: options = Options() if options.tidy: R.tidyup() if progress_bar is None: progress_bar = BaseProgressBar() elif progress_bar is True: progress_bar = TextProgressBar() # # check initial state # if isket(rho0): # Got a wave function as initial state: convert to density matrix. rho0 = rho0 * rho0.dag() # # prepare output array # n_tsteps = len(tlist) dt = tlist[1] - tlist[0] result_list = [] # # transform the initial density matrix and the e_ops opterators to the # eigenbasis # rho_eb = rho0.transform(ekets) e_eb_ops = [e.transform(ekets) for e in e_ops] for e_eb in e_eb_ops: if e_eb.isherm: result_list.append(np.zeros(n_tsteps, dtype=float)) else: result_list.append(np.zeros(n_tsteps, dtype=complex)) # # setup integrator # initial_vector = mat2vec(rho_eb.full()) r = scipy.integrate.ode(cy_ode_rhs) r.set_f_params(R.data.data, R.data.indices, R.data.indptr) r.set_integrator('zvode', method=options.method, order=options.order, atol=options.atol, rtol=options.rtol, nsteps=options.nsteps, first_step=options.first_step, min_step=options.min_step, max_step=options.max_step) r.set_initial_value(initial_vector, tlist[0]) # # start evolution # dt = np.diff(tlist) progress_bar.start(n_tsteps) for t_idx, _ in enumerate(tlist): progress_bar.update(t_idx) if not r.successful(): break rho_eb.data = dense2D_to_fastcsr_fmode(vec2mat(r.y), rho0.shape[0], rho0.shape[1]) # calculate all the expectation values, or output rho_eb if no # expectation value operators are given if e_ops: rho_eb_tmp = Qobj(rho_eb) for m, e in enumerate(e_eb_ops): result_list[m][t_idx] = expect(e, rho_eb_tmp) else: result_list.append(rho_eb.transform(ekets, True)) if t_idx < n_tsteps - 1: r.integrate(r.t + dt[t_idx]) progress_bar.finished() return result_list
def propagator(H, t, c_op_list=[], args={}, options=None, unitary_mode='batch', parallel=False, progress_bar=None, **kwargs): """ Calculate the propagator U(t) for the density matrix or wave function such that :math:`\psi(t) = U(t)\psi(0)` or :math:`\\rho_{\mathrm vec}(t) = U(t) \\rho_{\mathrm vec}(0)` where :math:`\\rho_{\mathrm vec}` is the vector representation of the density matrix. Parameters ---------- H : qobj or list Hamiltonian as a Qobj instance of a nested list of Qobjs and coefficients in the list-string or list-function format for time-dependent Hamiltonians (see description in :func:`qutip.mesolve`). t : float or array-like Time or list of times for which to evaluate the propagator. c_op_list : list List of qobj collapse operators. args : list/array/dictionary Parameters to callback functions for time-dependent Hamiltonians and collapse operators. options : :class:`qutip.Options` with options for the ODE solver. unitary_mode = str ('batch', 'single') Solve all basis vectors simulaneously ('batch') or individually ('single'). parallel : bool {False, True} Run the propagator in parallel mode. This will override the unitary_mode settings if set to True. progress_bar: BaseProgressBar Optional instance of BaseProgressBar, or a subclass thereof, for showing the progress of the simulation. By default no progress bar is used, and if set to True a TextProgressBar will be used. Returns ------- a : qobj Instance representing the propagator :math:`U(t)`. """ kw = _default_kwargs() if 'num_cpus' in kwargs: num_cpus = kwargs['num_cpus'] else: num_cpus = kw['num_cpus'] if progress_bar is None: progress_bar = BaseProgressBar() elif progress_bar is True: progress_bar = TextProgressBar() if options is None: options = Options() options.rhs_reuse = True rhs_clear() if isinstance(t, (int, float, np.integer, np.floating)): tlist = [0, t] else: tlist = t td_type = _td_format_check(H, c_op_list, solver='me') if isinstance(H, (types.FunctionType, types.BuiltinFunctionType, functools.partial)): H0 = H(0.0, args) elif isinstance(H, list): H0 = H[0][0] if isinstance(H[0], list) else H[0] else: H0 = H if len(c_op_list) == 0 and H0.isoper: # calculate propagator for the wave function N = H0.shape[0] dims = H0.dims if parallel: unitary_mode = 'single' u = np.zeros([N, N, len(tlist)], dtype=complex) output = parallel_map(_parallel_sesolve,range(N), task_args=(N,H, tlist,args,options), progress_bar=progress_bar, num_cpus=num_cpus) for n in range(N): for k, t in enumerate(tlist): u[:, n, k] = output[n].states[k].full().T else: if unitary_mode == 'single': u = np.zeros([N, N, len(tlist)], dtype=complex) progress_bar.start(N) for n in range(0, N): progress_bar.update(n) psi0 = basis(N, n) output = sesolve(H, psi0, tlist, [], args, options, _safe_mode=False) for k, t in enumerate(tlist): u[:, n, k] = output.states[k].full().T progress_bar.finished() elif unitary_mode =='batch': u = np.zeros(len(tlist), dtype=object) _rows = np.array([(N+1)*m for m in range(N)]) _cols = np.zeros_like(_rows) _data = np.ones_like(_rows,dtype=complex) psi0 = Qobj(sp.coo_matrix((_data,(_rows,_cols))).tocsr()) if td_type[1] > 0 or td_type[2] > 0: H2 = [] for k in range(len(H)): if isinstance(H[k], list): H2.append([tensor(qeye(N), H[k][0]), H[k][1]]) else: H2.append(tensor(qeye(N), H[k])) else: H2 = tensor(qeye(N), H) output = sesolve(H2, psi0, tlist, [] , args = args, _safe_mode=False, options=Options(normalize_output=False)) for k, t in enumerate(tlist): u[k] = sp_reshape(output.states[k].data, (N, N)) unit_row_norm(u[k].data, u[k].indptr, u[k].shape[0]) u[k] = u[k].T.tocsr() else: raise Exception('Invalid unitary mode.') elif len(c_op_list) == 0 and H0.issuper: # calculate the propagator for the vector representation of the # density matrix (a superoperator propagator) unitary_mode = 'single' N = H0.shape[0] sqrt_N = int(np.sqrt(N)) dims = H0.dims u = np.zeros([N, N, len(tlist)], dtype=complex) if parallel: output = parallel_map(_parallel_mesolve,range(N * N), task_args=(sqrt_N,H,tlist,c_op_list,args,options), progress_bar=progress_bar, num_cpus=num_cpus) for n in range(N * N): for k, t in enumerate(tlist): u[:, n, k] = mat2vec(output[n].states[k].full()).T else: progress_bar.start(N) for n in range(0, N): progress_bar.update(n) col_idx, row_idx = np.unravel_index(n,(sqrt_N,sqrt_N)) rho0 = Qobj(sp.csr_matrix(([1],([row_idx],[col_idx])), shape=(sqrt_N,sqrt_N), dtype=complex)) output = mesolve(H, rho0, tlist, [], [], args, options, _safe_mode=False) for k, t in enumerate(tlist): u[:, n, k] = mat2vec(output.states[k].full()).T progress_bar.finished() else: # calculate the propagator for the vector representation of the # density matrix (a superoperator propagator) unitary_mode = 'single' N = H0.shape[0] dims = [H0.dims, H0.dims] u = np.zeros([N * N, N * N, len(tlist)], dtype=complex) if parallel: output = parallel_map(_parallel_mesolve,range(N * N), task_args=(N,H,tlist,c_op_list,args,options), progress_bar=progress_bar, num_cpus=num_cpus) for n in range(N * N): for k, t in enumerate(tlist): u[:, n, k] = mat2vec(output[n].states[k].full()).T else: progress_bar.start(N * N) for n in range(N * N): progress_bar.update(n) col_idx, row_idx = np.unravel_index(n,(N,N)) rho0 = Qobj(sp.csr_matrix(([1],([row_idx],[col_idx])), shape=(N,N), dtype=complex)) output = mesolve(H, rho0, tlist, c_op_list, [], args, options, _safe_mode=False) for k, t in enumerate(tlist): u[:, n, k] = mat2vec(output.states[k].full()).T progress_bar.finished() if len(tlist) == 2: if unitary_mode == 'batch': return Qobj(u[-1], dims=dims) else: return Qobj(u[:, :, 1], dims=dims) else: if unitary_mode == 'batch': return np.array([Qobj(u[k], dims=dims) for k in range(len(tlist))], dtype=object) else: return np.array([Qobj(u[:, :, k], dims=dims) for k in range(len(tlist))], dtype=object)
def parallel_map( task, values, task_args=None, task_kwargs=None, num_cpus=None, progress_bar=None, ): """Map function `task` onto `values`, in parallel. This function's interface is identical to :func:`qutip.parallel.parallel_map` as of QuTiP 4.5.0, but has the option of using :mod:`loky` as a backend (see :func:`set_parallelization`). It also eliminates internal threads, according to :obj:`USE_THREADPOOL_LIMITS`. """ # TODO: if QuTiP's parallel_map catches up, we can remove this function, # and put QuTiP's parallel_map into __all__ to maintain krotov's interface. if task_args is None: task_args = () if task_kwargs is None: task_kwargs = {} if num_cpus is None: num_cpus = multiprocessing.cpu_count() if progress_bar is None: progress_bar = BaseProgressBar() if progress_bar is True: progress_bar = TextProgressBar() progress_bar.start(len(values)) nfinished = [0] def _update_progress_bar(x): nfinished[0] += 1 progress_bar.update(nfinished[0]) if USE_LOKY: Executor = LokyReusableExecutor if USE_THREADPOOL_LIMITS: Executor = partial( LokyReusableExecutor, initializer=_process_threadpool_limits_initializier, ) else: Executor = ProcessPoolExecutor _threadpool_limits = _no_threadpool_limits if USE_THREADPOOL_LIMITS: _threadpool_limits = threadpool_limits with _threadpool_limits(limits=1): with Executor(max_workers=num_cpus) as executor: jobs = [] try: for value in values: args = (value, ) + tuple(task_args) job = executor.submit(task, *args, **task_kwargs) job.add_done_callback(_update_progress_bar) jobs.append(job) res = [job.result() for job in jobs] except KeyboardInterrupt as e: raise e progress_bar.finished() return res
def grape_unitary_adaptive(U, H0, H_ops, R, times, eps=None, u_start=None, u_limits=None, interp_kind='linear', use_interp=False, alpha=None, beta=None, phase_sensitive=False, overlap_terminate=1.0, progress_bar=BaseProgressBar()): """ Calculate control pulses for the Hamiltonian operators in H_ops so that the unitary U is realized. Experimental: Work in progress. Parameters ---------- U : Qobj Target unitary evolution operator. H0 : Qobj Static Hamiltonian (that cannot be tuned by the control fields). H_ops: list of Qobj A list of operators that can be tuned in the Hamiltonian via the control fields. R : int Number of GRAPE iterations. time : array / list Array of time coordinates for control pulse evalutation. u_start : array Optional array with initial control pulse values. Returns ------- Instance of GRAPEResult, which contains the control pulses calculated with GRAPE, a time-dependent Hamiltonian that is defined by the control pulses, as well as the resulting propagator. """ if eps is None: eps = 0.1 * (2 * np.pi) / (times[-1]) eps_vec = np.array([eps / 2, eps, 2 * eps]) eps_log = np.zeros(R) overlap_log = np.zeros(R) best_k = 0 _k_overlap = np.array([0.0, 0.0, 0.0]) M = len(times) J = len(H_ops) K = len(eps_vec) Uf = [None for _ in range(K)] u = np.zeros((R, J, M, K)) if u_limits and len(u_limits) != 2: raise ValueError("u_limits must be a list with two values") if u_limits: warnings.warn("Causion: Using experimental feature u_limits") if u_limits and u_start: # make sure that no values in u0 violates the u_limits conditions u_start = np.array(u_start) u_start[u_start < u_limits[0]] = u_limits[0] u_start[u_start > u_limits[1]] = u_limits[1] if u_start is not None: for idx, u0 in enumerate(u_start): for k in range(K): u[0, idx, :, k] = u0 if beta: warnings.warn("Causion: Using experimental feature time-penalty") if phase_sensitive: _fidelity_function = lambda x: x else: _fidelity_function = lambda x: abs(x)**2 best_k = 1 _r = 0 _prev_overlap = 0 progress_bar.start(R) for r in range(R - 1): progress_bar.update(r) _r = r eps_log[r] = eps_vec[best_k] logger.debug("eps_vec: {}".format(eps_vec)) _t0 = time.time() dt = times[1] - times[0] if use_interp: ip_funcs = [ interp1d(times, u[r, j, :, best_k], kind=interp_kind, bounds_error=False, fill_value=u[r, j, -1, best_k]) for j in range(J) ] def _H_t(t, args=None): return H0 + sum( [float(ip_funcs[j](t)) * H_ops[j] for j in range(J)]) U_list = [(-1j * _H_t(times[idx]) * dt).expm() for idx in range(M - 1)] else: def _H_idx(idx): return H0 + sum( [u[r, j, idx, best_k] * H_ops[j] for j in range(J)]) U_list = [(-1j * _H_idx(idx) * dt).expm() for idx in range(M - 1)] logger.debug("Time 1: %fs" % (time.time() - _t0)) _t0 = time.time() U_f_list = [] U_b_list = [] U_f = 1 U_b = 1 for m in range(M - 1): U_f = U_list[m] * U_f U_f_list.append(U_f) U_b_list.insert(0, U_b) U_b = U_list[M - 2 - m].dag() * U_b logger.debug("Time 2: %fs" % (time.time() - _t0)) _t0 = time.time() for j in range(J): for m in range(M - 1): P = U_b_list[m] * U Q = 1j * dt * H_ops[j] * U_f_list[m] if phase_sensitive: du = -cy_overlap(P.data, Q.data) else: du = (-2 * cy_overlap(P.data, Q.data) * cy_overlap(U_f_list[m].data, P.data)) if alpha: # penalty term for high power control signals u du += -2 * alpha * u[r, j, m, best_k] * dt if beta: # penalty term for late control signals u du += -2 * beta * k**2 * u[r, j, k] * dt for k, eps_val in enumerate(eps_vec): u[r + 1, j, m, k] = u[r, j, m, k] + eps_val * du.real if u_limits: if u[r + 1, j, m, k] < u_limits[0]: u[r + 1, j, m, k] = u_limits[0] elif u[r + 1, j, m, k] > u_limits[1]: u[r + 1, j, m, k] = u_limits[1] u[r + 1, j, -1, :] = u[r + 1, j, -2, :] logger.debug("Time 3: %fs" % (time.time() - _t0)) _t0 = time.time() for k, eps_val in enumerate(eps_vec): def _H_idx(idx): return H0 + sum( [u[r + 1, j, idx, k] * H_ops[j] for j in range(J)]) U_list = [(-1j * _H_idx(idx) * dt).expm() for idx in range(M - 1)] Uf[k] = gate_sequence_product(U_list) _k_overlap[k] = _fidelity_function(cy_overlap(Uf[k].data, U.data)).real best_k = np.argmax(_k_overlap) logger.debug("k_overlap: ", _k_overlap, best_k) if _prev_overlap > _k_overlap[best_k]: logger.debug("Regression, stepping back with smaller eps.") u[r + 1, :, :, :] = u[r, :, :, :] eps_vec /= 2 else: if best_k == 0: eps_vec /= 2 elif best_k == 2: eps_vec *= 2 _prev_overlap = _k_overlap[best_k] overlap_log[r] = _k_overlap[best_k] if overlap_terminate < 1.0: if _k_overlap[best_k] > overlap_terminate: logger.info("Reached target fidelity, terminating.") break logger.debug("Time 4: %fs" % (time.time() - _t0)) _t0 = time.time() if use_interp: ip_funcs = [ interp1d(times, u[_r, j, :, best_k], kind=interp_kind, bounds_error=False, fill_value=u[R - 1, j, -1]) for j in range(J) ] H_td_func = [H0] + [[H_ops[j], lambda t, args, j=j: ip_funcs[j](t)] for j in range(J)] else: H_td_func = [H0] + [[H_ops[j], u[_r, j, :, best_k]] for j in range(J)] progress_bar.finished() result = GRAPEResult(u=u[:_r, :, :, best_k], U_f=Uf[best_k], H_t=H_td_func) result.eps = eps_log result.overlap = overlap_log return result
def cy_grape_unitary(U, H0, H_ops, R, times, eps=None, u_start=None, u_limits=None, interp_kind='linear', use_interp=False, alpha=None, beta=None, phase_sensitive=True, progress_bar=BaseProgressBar()): """ Calculate control pulses for the Hamitonian operators in H_ops so that the unitary U is realized. Experimental: Work in progress. Parameters ---------- U : Qobj Target unitary evolution operator. H0 : Qobj Static Hamiltonian (that cannot be tuned by the control fields). H_ops: list of Qobj A list of operators that can be tuned in the Hamiltonian via the control fields. R : int Number of GRAPE iterations. time : array / list Array of time coordinates for control pulse evalutation. u_start : array Optional array with initial control pulse values. Returns ------- Instance of GRAPEResult, which contains the control pulses calculated with GRAPE, a time-dependent Hamiltonian that is defined by the control pulses, as well as the resulting propagator. """ if eps is None: eps = 0.1 * (2 * np.pi) / (times[-1]) M = len(times) J = len(H_ops) u = np.zeros((R, J, M)) H_ops_data = [H_op.data for H_op in H_ops] if u_limits and len(u_limits) != 2: raise ValueError("u_limits must be a list with two values") if u_limits: warnings.warn("Causion: Using experimental feature u_limits") if u_limits and u_start: # make sure that no values in u0 violates the u_limits conditions u_start = np.array(u_start) u_start[u_start < u_limits[0]] = u_limits[0] u_start[u_start > u_limits[1]] = u_limits[1] if u_limits: use_u_limits = 1 u_min = u_limits[0] u_max = u_limits[1] else: use_u_limits = 0 u_min = 0.0 u_max = 0.0 if u_start is not None: for idx, u0 in enumerate(u_start): u[0, idx, :] = u0 if beta: warnings.warn("Causion: Using experimental feature time-penalty") alpha_val = alpha if alpha else 0.0 beta_val = beta if beta else 0.0 progress_bar.start(R) for r in range(R - 1): progress_bar.update(r) dt = times[1] - times[0] if use_interp: ip_funcs = [ interp1d(times, u[r, j, :], kind=interp_kind, bounds_error=False, fill_value=u[r, j, -1]) for j in range(J) ] def _H_t(t, args=None): return H0 + sum( [float(ip_funcs[j](t)) * H_ops[j] for j in range(J)]) U_list = [(-1j * _H_t(times[idx]) * dt).expm().data for idx in range(M - 1)] else: def _H_idx(idx): return H0 + sum([u[r, j, idx] * H_ops[j] for j in range(J)]) U_list = [(-1j * _H_idx(idx) * dt).expm().data for idx in range(M - 1)] U_f_list = [] U_b_list = [] U_f = 1 U_b = sp.eye(*(U.shape)) for n in range(M - 1): U_f = U_list[n] * U_f U_f_list.append(U_f) U_b_list.insert(0, U_b) U_b = U_list[M - 2 - n].T.conj().tocsr() * U_b cy_grape_inner(U.data, u, r, J, M, U_b_list, U_f_list, H_ops_data, dt, eps, alpha_val, beta_val, phase_sensitive, use_u_limits, u_min, u_max) if use_interp: ip_funcs = [ interp1d(times, u[R - 1, j, :], kind=interp_kind, bounds_error=False, fill_value=u[R - 1, j, -1]) for j in range(J) ] H_td_func = [H0] + [[H_ops[j], lambda t, args, j=j: ip_funcs[j](t)] for j in range(J)] else: H_td_func = [H0] + [[H_ops[j], u[-1, j, :]] for j in range(J)] progress_bar.finished() return GRAPEResult(u=u, U_f=Qobj(U_f_list[-1], dims=U.dims), H_t=H_td_func)
def grape_unitary(U, H0, H_ops, R, times, eps=None, u_start=None, u_limits=None, interp_kind='linear', use_interp=False, alpha=None, beta=None, phase_sensitive=True, progress_bar=BaseProgressBar()): """ Calculate control pulses for the Hamiltonian operators in H_ops so that the unitary U is realized. Experimental: Work in progress. Parameters ---------- U : Qobj Target unitary evolution operator. H0 : Qobj Static Hamiltonian (that cannot be tuned by the control fields). H_ops: list of Qobj A list of operators that can be tuned in the Hamiltonian via the control fields. R : int Number of GRAPE iterations. time : array / list Array of time coordinates for control pulse evalutation. u_start : array Optional array with initial control pulse values. Returns ------- Instance of GRAPEResult, which contains the control pulses calculated with GRAPE, a time-dependent Hamiltonian that is defined by the control pulses, as well as the resulting propagator. """ if eps is None: eps = 0.1 * (2 * np.pi) / (times[-1]) M = len(times) J = len(H_ops) u = np.zeros((R, J, M)) if u_limits and len(u_limits) != 2: raise ValueError("u_limits must be a list with two values") if u_limits: warnings.warn("Caution: Using experimental feature u_limits") if u_limits and u_start: # make sure that no values in u0 violates the u_limits conditions u_start = np.array(u_start) u_start[u_start < u_limits[0]] = u_limits[0] u_start[u_start > u_limits[1]] = u_limits[1] if u_start is not None: for idx, u0 in enumerate(u_start): u[0, idx, :] = u0 if beta: warnings.warn("Causion: Using experimental feature time-penalty") progress_bar.start(R) for r in range(R - 1): progress_bar.update(r) dt = times[1] - times[0] if use_interp: ip_funcs = [ interp1d(times, u[r, j, :], kind=interp_kind, bounds_error=False, fill_value=u[r, j, -1]) for j in range(J) ] def _H_t(t, args=None): return H0 + sum( [float(ip_funcs[j](t)) * H_ops[j] for j in range(J)]) U_list = [(-1j * _H_t(times[idx]) * dt).expm() for idx in range(M - 1)] else: def _H_idx(idx): return H0 + sum([u[r, j, idx] * H_ops[j] for j in range(J)]) U_list = [(-1j * _H_idx(idx) * dt).expm() for idx in range(M - 1)] U_f_list = [] U_b_list = [] U_f = 1 U_b = 1 for n in range(M - 1): U_f = U_list[n] * U_f U_f_list.append(U_f) U_b_list.insert(0, U_b) U_b = U_list[M - 2 - n].dag() * U_b for j in range(J): for m in range(M - 1): P = U_b_list[m] * U Q = 1j * dt * H_ops[j] * U_f_list[m] if phase_sensitive: du = -_overlap(P, Q) else: du = -2 * _overlap(P, Q) * _overlap(U_f_list[m], P) if alpha: # penalty term for high power control signals u du += -2 * alpha * u[r, j, m] * dt if beta: # penalty term for late control signals u du += -2 * beta * m * u[r, j, m] * dt u[r + 1, j, m] = u[r, j, m] + eps * du.real if u_limits: if u[r + 1, j, m] < u_limits[0]: u[r + 1, j, m] = u_limits[0] elif u[r + 1, j, m] > u_limits[1]: u[r + 1, j, m] = u_limits[1] u[r + 1, j, -1] = u[r + 1, j, -2] if use_interp: ip_funcs = [ interp1d(times, u[R - 1, j, :], kind=interp_kind, bounds_error=False, fill_value=u[R - 1, j, -1]) for j in range(J) ] H_td_func = [H0] + [[H_ops[j], lambda t, args, j=j: ip_funcs[j](t)] for j in range(J)] else: H_td_func = [H0] + [[H_ops[j], u[-1, j, :]] for j in range(J)] progress_bar.finished() # return U_f_list[-1], H_td_func, u return GRAPEResult(u=u, U_f=U_f_list[-1], H_t=H_td_func)
def propagator(H, t, c_op_list=[], args={}, options=None, parallel=False, progress_bar=None, **kwargs): """ Calculate the propagator U(t) for the density matrix or wave function such that :math:`\psi(t) = U(t)\psi(0)` or :math:`\\rho_{\mathrm vec}(t) = U(t) \\rho_{\mathrm vec}(0)` where :math:`\\rho_{\mathrm vec}` is the vector representation of the density matrix. Parameters ---------- H : qobj or list Hamiltonian as a Qobj instance of a nested list of Qobjs and coefficients in the list-string or list-function format for time-dependent Hamiltonians (see description in :func:`qutip.mesolve`). t : float or array-like Time or list of times for which to evaluate the propagator. c_op_list : list List of qobj collapse operators. args : list/array/dictionary Parameters to callback functions for time-dependent Hamiltonians and collapse operators. options : :class:`qutip.Options` with options for the ODE solver. parallel : bool {False, True} Run the propagator in parallel mode. progress_bar: BaseProgressBar Optional instance of BaseProgressBar, or a subclass thereof, for showing the progress of the simulation. By default no progress bar is used, and if set to True a TextProgressBar will be used. Returns ------- a : qobj Instance representing the propagator :math:`U(t)`. """ kw = _default_kwargs() if 'num_cpus' in kwargs: num_cpus = kwargs['num_cpus'] else: num_cpus = kw['num_cpus'] if progress_bar is None: progress_bar = BaseProgressBar() elif progress_bar is True: progress_bar = TextProgressBar() if options is None: options = Options() options.rhs_reuse = True rhs_clear() if isinstance(t, (int, float, np.integer, np.floating)): tlist = [0, t] else: tlist = t td_type = _td_format_check(H, c_op_list, solver='me')[2] if td_type > 0: rhs_generate(H, c_op_list, args=args, options=options) if isinstance( H, (types.FunctionType, types.BuiltinFunctionType, functools.partial)): H0 = H(0.0, args) elif isinstance(H, list): H0 = H[0][0] if isinstance(H[0], list) else H[0] else: H0 = H if len(c_op_list) == 0 and H0.isoper: # calculate propagator for the wave function N = H0.shape[0] dims = H0.dims u = np.zeros([N, N, len(tlist)], dtype=complex) if parallel: output = parallel_map(_parallel_sesolve, range(N), task_args=(N, H, tlist, args, options), progress_bar=progress_bar, num_cpus=num_cpus) for n in range(N): for k, t in enumerate(tlist): u[:, n, k] = output[n].states[k].full().T else: progress_bar.start(N) for n in range(0, N): progress_bar.update(n) psi0 = basis(N, n) output = sesolve(H, psi0, tlist, [], args, options, _safe_mode=False) for k, t in enumerate(tlist): u[:, n, k] = output.states[k].full().T progress_bar.finished() # todo: evolving a batch of wave functions: # psi_0_list = [basis(N, n) for n in range(N)] # psi_t_list = mesolve(H, psi_0_list, [0, t], [], [], args, options) # for n in range(0, N): # u[:,n] = psi_t_list[n][1].full().T elif len(c_op_list) == 0 and H0.issuper: # calculate the propagator for the vector representation of the # density matrix (a superoperator propagator) N = H0.shape[0] sqrt_N = int(np.sqrt(N)) dims = H0.dims u = np.zeros([N, N, len(tlist)], dtype=complex) if parallel: output = parallel_map(_parallel_mesolve, range(N * N), task_args=(sqrt_N, H, tlist, c_op_list, args, options), progress_bar=progress_bar, num_cpus=num_cpus) for n in range(N * N): for k, t in enumerate(tlist): u[:, n, k] = mat2vec(output[n].states[k].full()).T else: progress_bar.start(N) for n in range(0, N): progress_bar.update(n) col_idx, row_idx = np.unravel_index(n, (sqrt_N, sqrt_N)) rho0 = Qobj( sp.csr_matrix(([1], ([row_idx], [col_idx])), shape=(sqrt_N, sqrt_N), dtype=complex)) output = mesolve(H, rho0, tlist, [], [], args, options, _safe_mode=False) for k, t in enumerate(tlist): u[:, n, k] = mat2vec(output.states[k].full()).T progress_bar.finished() else: # calculate the propagator for the vector representation of the # density matrix (a superoperator propagator) N = H0.shape[0] dims = [H0.dims, H0.dims] u = np.zeros([N * N, N * N, len(tlist)], dtype=complex) if parallel: output = parallel_map(_parallel_mesolve, range(N * N), task_args=(N, H, tlist, c_op_list, args, options), progress_bar=progress_bar, num_cpus=num_cpus) for n in range(N * N): for k, t in enumerate(tlist): u[:, n, k] = mat2vec(output[n].states[k].full()).T else: progress_bar.start(N * N) for n in range(N * N): progress_bar.update(n) col_idx, row_idx = np.unravel_index(n, (N, N)) rho0 = Qobj( sp.csr_matrix(([1], ([row_idx], [col_idx])), shape=(N, N), dtype=complex)) output = mesolve(H, rho0, tlist, c_op_list, [], args, options, _safe_mode=False) for k, t in enumerate(tlist): u[:, n, k] = mat2vec(output.states[k].full()).T progress_bar.finished() if len(tlist) == 2: return Qobj(u[:, :, 1], dims=dims) else: return np.array( [Qobj(u[:, :, k], dims=dims) for k in range(len(tlist))], dtype=object)
def mcsolve(H, psi0, tlist, c_ops, e_ops, ntraj=None, args={}, options=None, progress_bar=True, map_func=None, map_kwargs=None): """Monte Carlo evolution of a state vector :math:`|\psi \\rangle` for a given Hamiltonian and sets of collapse operators, and possibly, operators for calculating expectation values. Options for the underlying ODE solver are given by the Options class. mcsolve supports time-dependent Hamiltonians and collapse operators using either Python functions of strings to represent time-dependent coefficients. Note that, the system Hamiltonian MUST have at least one constant term. As an example of a time-dependent problem, consider a Hamiltonian with two terms ``H0`` and ``H1``, where ``H1`` is time-dependent with coefficient ``sin(w*t)``, and collapse operators ``C0`` and ``C1``, where ``C1`` is time-dependent with coeffcient ``exp(-a*t)``. Here, w and a are constant arguments with values ``W`` and ``A``. Using the Python function time-dependent format requires two Python functions, one for each collapse coefficient. Therefore, this problem could be expressed as:: def H1_coeff(t,args): return sin(args['w']*t) def C1_coeff(t,args): return exp(-args['a']*t) H = [H0, [H1, H1_coeff]] c_ops = [C0, [C1, C1_coeff]] args={'a': A, 'w': W} or in String (Cython) format we could write:: H = [H0, [H1, 'sin(w*t)']] c_ops = [C0, [C1, 'exp(-a*t)']] args={'a': A, 'w': W} Constant terms are preferably placed first in the Hamiltonian and collapse operator lists. Parameters ---------- H : :class:`qutip.Qobj` System Hamiltonian. psi0 : :class:`qutip.Qobj` Initial state vector tlist : array_like Times at which results are recorded. ntraj : int Number of trajectories to run. c_ops : array_like single collapse operator or ``list`` or ``array`` of collapse operators. e_ops : array_like single operator or ``list`` or ``array`` of operators for calculating expectation values. args : dict Arguments for time-dependent Hamiltonian and collapse operator terms. options : Options Instance of ODE solver options. progress_bar: BaseProgressBar Optional instance of BaseProgressBar, or a subclass thereof, for showing the progress of the simulation. Set to None to disable the progress bar. map_func: function A map function for managing the calls to the single-trajactory solver. map_kwargs: dictionary Optional keyword arguments to the map_func function. Returns ------- results : :class:`qutip.solver.Result` Object storing all results from the simulation. .. note:: It is possible to reuse the random number seeds from a previous run of the mcsolver by passing the output Result object seeds via the Options class, i.e. Options(seeds=prev_result.seeds). """ if debug: print(inspect.stack()[0][3]) if options is None: options = Options() if ntraj is None: ntraj = options.ntraj config.map_func = map_func if map_func is not None else parallel_map config.map_kwargs = map_kwargs if map_kwargs is not None else {} if not psi0.isket: raise Exception("Initial state must be a state vector.") if isinstance(c_ops, Qobj): c_ops = [c_ops] if isinstance(e_ops, Qobj): e_ops = [e_ops] if isinstance(e_ops, dict): e_ops_dict = e_ops e_ops = [e for e in e_ops.values()] else: e_ops_dict = None config.options = options if progress_bar: if progress_bar is True: config.progress_bar = TextProgressBar() else: config.progress_bar = progress_bar else: config.progress_bar = BaseProgressBar() # set num_cpus to the value given in qutip.settings if none in Options if not config.options.num_cpus: config.options.num_cpus = qutip.settings.num_cpus if config.options.num_cpus == 1: # fallback on serial_map if num_cpu == 1, since there is no # benefit of starting multiprocessing in this case config.map_func = serial_map # set initial value data if options.tidy: config.psi0 = psi0.tidyup(options.atol).full().ravel() else: config.psi0 = psi0.full().ravel() config.psi0_dims = psi0.dims config.psi0_shape = psi0.shape # set options on ouput states if config.options.steady_state_average: config.options.average_states = True # set general items config.tlist = tlist if isinstance(ntraj, (list, np.ndarray)): config.ntraj = np.sort(ntraj)[-1] else: config.ntraj = ntraj # set norm finding constants config.norm_tol = options.norm_tol config.norm_steps = options.norm_steps # convert array based time-dependence to string format H, c_ops, args = _td_wrap_array_str(H, c_ops, args, tlist) # SETUP ODE DATA IF NONE EXISTS OR NOT REUSING # -------------------------------------------- if not options.rhs_reuse or not config.tdfunc: # reset config collapse and time-dependence flags to default values config.soft_reset() # check for type of time-dependence (if any) time_type, h_stuff, c_stuff = _td_format_check(H, c_ops, 'mc') c_terms = len(c_stuff[0]) + len(c_stuff[1]) + len(c_stuff[2]) # set time_type for use in multiprocessing config.tflag = time_type # check for collapse operators if c_terms > 0: config.cflag = 1 else: config.cflag = 0 # Configure data _mc_data_config(H, psi0, h_stuff, c_ops, c_stuff, args, e_ops, options, config) # compile and load cython functions if necessary _mc_func_load(config) else: # setup args for new parameters when rhs_reuse=True and tdfunc is given # string based if config.tflag in [1, 10, 11]: if any(args): config.c_args = [] arg_items = list(args.items()) for k in range(len(arg_items)): config.c_args.append(arg_items[k][1]) # function based elif config.tflag in [2, 3, 20, 22]: config.h_func_args = args # load monte carlo class mc = _MC(config) # Run the simulation mc.run() # Remove RHS cython file if necessary if not options.rhs_reuse and config.tdname: _cython_build_cleanup(config.tdname) # AFTER MCSOLVER IS DONE # ---------------------- # Store results in the Result object output = Result() output.solver = 'mcsolve' output.seeds = config.options.seeds # state vectors if (mc.psi_out is not None and config.options.average_states and config.cflag and ntraj != 1): output.states = parfor(_mc_dm_avg, mc.psi_out.T) elif mc.psi_out is not None: output.states = mc.psi_out # expectation values if (mc.expect_out is not None and config.cflag and config.options.average_expect): # averaging if multiple trajectories if isinstance(ntraj, int): output.expect = [np.mean(np.array([mc.expect_out[nt][op] for nt in range(ntraj)], dtype=object), axis=0) for op in range(config.e_num)] elif isinstance(ntraj, (list, np.ndarray)): output.expect = [] for num in ntraj: expt_data = np.mean(mc.expect_out[:num], axis=0) data_list = [] if any([not op.isherm for op in e_ops]): for k in range(len(e_ops)): if e_ops[k].isherm: data_list.append(np.real(expt_data[k])) else: data_list.append(expt_data[k]) else: data_list = [data for data in expt_data] output.expect.append(data_list) else: # no averaging for single trajectory or if average_expect flag # (Options) is off if mc.expect_out is not None: output.expect = mc.expect_out # simulation parameters output.times = config.tlist output.num_expect = config.e_num output.num_collapse = config.c_num output.ntraj = config.ntraj output.col_times = mc.collapse_times_out output.col_which = mc.which_op_out if e_ops_dict: output.expect = {e: output.expect[n] for n, e in enumerate(e_ops_dict.keys())} return output
def odesolve(H, rho0, tlist, c_op_list, e_ops, args=None, options=None): """ Master equation evolution of a density matrix for a given Hamiltonian. Evolution of a state vector or density matrix (`rho0`) for a given Hamiltonian (`H`) and set of collapse operators (`c_op_list`), by integrating the set of ordinary differential equations that define the system. The output is either the state vector at arbitrary points in time (`tlist`), or the expectation values of the supplied operators (`e_ops`). For problems with time-dependent Hamiltonians, `H` can be a callback function that takes two arguments, time and `args`, and returns the Hamiltonian at that point in time. `args` is a list of parameters that is passed to the callback function `H` (only used for time-dependent Hamiltonians). Parameters ---------- H : :class:`qutip.qobj` system Hamiltonian, or a callback function for time-dependent Hamiltonians. rho0 : :class:`qutip.qobj` initial density matrix or state vector (ket). tlist : *list* / *array* list of times for :math:`t`. c_op_list : list of :class:`qutip.qobj` list of collapse operators. e_ops : list of :class:`qutip.qobj` / callback function list of operators for which to evaluate expectation values. args : *dictionary* dictionary of parameters for time-dependent Hamiltonians and collapse operators. options : :class:`qutip.Options` with options for the ODE solver. Returns ------- output :array Expectation values of wavefunctions/density matrices for the times specified by `tlist`. Notes ----- On using callback function: odesolve transforms all :class:`qutip.qobj` objects to sparse matrices before handing the problem to the integrator function. In order for your callback function to work correctly, pass all :class:`qutip.qobj` objects that are used in constructing the Hamiltonian via args. odesolve will check for :class:`qutip.qobj` in `args` and handle the conversion to sparse matrices. All other :class:`qutip.qobj` objects that are not passed via `args` will be passed on to the integrator to scipy who will raise an NotImplemented exception. Deprecated in QuTiP 2.0.0. Use :func:`mesolve` instead. """ warnings.warn("odesolve is deprecated since 2.0.0. Use mesolve instead.", DeprecationWarning) if debug: print(inspect.stack()[0][3]) if options is None: options = Options() if (c_op_list and len(c_op_list) > 0) or not isket(rho0): if isinstance(H, list): output = _mesolve_list_td(H, rho0, tlist, c_op_list, e_ops, args, options, BaseProgressBar()) if isinstance( H, (types.FunctionType, types.BuiltinFunctionType, partial)): output = _mesolve_func_td(H, rho0, tlist, c_op_list, e_ops, args, options, BaseProgressBar()) else: output = _mesolve_const(H, rho0, tlist, c_op_list, e_ops, args, options, BaseProgressBar()) else: if isinstance(H, list): output = _sesolve_list_td(H, rho0, tlist, e_ops, args, options, BaseProgressBar()) if isinstance( H, (types.FunctionType, types.BuiltinFunctionType, partial)): output = _sesolve_func_td(H, rho0, tlist, e_ops, args, options, BaseProgressBar()) else: output = _sesolve_const(H, rho0, tlist, e_ops, args, options, BaseProgressBar()) if len(e_ops) > 0: return output.expect else: return output.states
def mesolve(H, rho0, tlist, c_ops=[], e_ops=[], args={}, options=None, progress_bar=None, _safe_mode=True): """ Master equation evolution of a density matrix for a given Hamiltonian and set of collapse operators, or a Liouvillian. Evolve the state vector or density matrix (`rho0`) using a given Hamiltonian (`H`) and an [optional] set of collapse operators (`c_ops`), by integrating the set of ordinary differential equations that define the system. In the absence of collapse operators the system is evolved according to the unitary evolution of the Hamiltonian. The output is either the state vector at arbitrary points in time (`tlist`), or the expectation values of the supplied operators (`e_ops`). If e_ops is a callback function, it is invoked for each time in `tlist` with time and the state as arguments, and the function does not use any return values. If either `H` or the Qobj elements in `c_ops` are superoperators, they will be treated as direct contributions to the total system Liouvillian. This allows to solve master equations that are not on standard Lindblad form by passing a custom Liouvillian in place of either the `H` or `c_ops` elements. **Time-dependent operators** For time-dependent problems, `H` and `c_ops` can be callback functions that takes two arguments, time and `args`, and returns the Hamiltonian or Liouvillian for the system at that point in time (*callback format*). Alternatively, `H` and `c_ops` can be a specified in a nested-list format where each element in the list is a list of length 2, containing an operator (:class:`qutip.qobj`) at the first element and where the second element is either a string (*list string format*), a callback function (*list callback format*) that evaluates to the time-dependent coefficient for the corresponding operator, or a NumPy array (*list array format*) which specifies the value of the coefficient to the corresponding operator for each value of t in tlist. *Examples* H = [[H0, 'sin(w*t)'], [H1, 'sin(2*w*t)']] H = [[H0, f0_t], [H1, f1_t]] where f0_t and f1_t are python functions with signature f_t(t, args). H = [[H0, np.sin(w*tlist)], [H1, np.sin(2*w*tlist)]] In the *list string format* and *list callback format*, the string expression and the callback function must evaluate to a real or complex number (coefficient for the corresponding operator). In all cases of time-dependent operators, `args` is a dictionary of parameters that is used when evaluating operators. It is passed to the callback functions as second argument. **Additional options** Additional options to mesolve can be set via the `options` argument, which should be an instance of :class:`qutip.solver.Options`. Many ODE integration options can be set this way, and the `store_states` and `store_final_state` options can be used to store states even though expectation values are requested via the `e_ops` argument. .. note:: If an element in the list-specification of the Hamiltonian or the list of collapse operators are in superoperator form it will be added to the total Liouvillian of the problem with out further transformation. This allows for using mesolve for solving master equations that are not on standard Lindblad form. .. note:: On using callback function: mesolve transforms all :class:`qutip.qobj` objects to sparse matrices before handing the problem to the integrator function. In order for your callback function to work correctly, pass all :class:`qutip.qobj` objects that are used in constructing the Hamiltonian via args. mesolve will check for :class:`qutip.qobj` in `args` and handle the conversion to sparse matrices. All other :class:`qutip.qobj` objects that are not passed via `args` will be passed on to the integrator in scipy which will raise an NotImplemented exception. Parameters ---------- H : :class:`qutip.Qobj` System Hamiltonian, or a callback function for time-dependent Hamiltonians, or alternatively a system Liouvillian. rho0 : :class:`qutip.Qobj` initial density matrix or state vector (ket). tlist : *list* / *array* list of times for :math:`t`. c_ops : list of :class:`qutip.Qobj` single collapse operator, or list of collapse operators, or a list of Liouvillian superoperators. e_ops : list of :class:`qutip.Qobj` / callback function single single operator or list of operators for which to evaluate expectation values. args : *dictionary* dictionary of parameters for time-dependent Hamiltonians and collapse operators. options : :class:`qutip.Options` with options for the solver. progress_bar : BaseProgressBar Optional instance of BaseProgressBar, or a subclass thereof, for showing the progress of the simulation. Returns ------- result: :class:`qutip.Result` An instance of the class :class:`qutip.Result`, which contains either an *array* `result.expect` of expectation values for the times specified by `tlist`, or an *array* `result.states` of state vectors or density matrices corresponding to the times in `tlist` [if `e_ops` is an empty list], or nothing if a callback function was given in place of operators for which to calculate the expectation values. """ # check whether c_ops or e_ops is is a single operator # if so convert it to a list containing only that operator if isinstance(c_ops, Qobj): c_ops = [c_ops] if isinstance(e_ops, Qobj): e_ops = [e_ops] if isinstance(e_ops, dict): e_ops_dict = e_ops e_ops = [e for e in e_ops.values()] else: e_ops_dict = None if _safe_mode: _solver_safety_check(H, rho0, c_ops, e_ops, args) if progress_bar is None: progress_bar = BaseProgressBar() elif progress_bar is True: progress_bar = TextProgressBar() # check if rho0 is a superoperator, in which case e_ops argument should # be empty, i.e., e_ops = [] if issuper(rho0) and not e_ops == []: raise TypeError("Must have e_ops = [] when initial condition rho0 is" + " a superoperator.") # convert array based time-dependence to string format H, c_ops, args = _td_wrap_array_str(H, c_ops, args, tlist) # check for type (if any) of time-dependent inputs _, n_func, n_str = _td_format_check(H, c_ops) if options is None: options = Options() if (not options.rhs_reuse) or (not config.tdfunc): # reset config collapse and time-dependence flags to default values config.reset() #check if should use OPENMP check_use_openmp(options) res = None # # dispatch the appropriate solver # if ((c_ops and len(c_ops) > 0) or (not isket(rho0)) or (isinstance(H, Qobj) and issuper(H)) or (isinstance(H, list) and isinstance(H[0], Qobj) and issuper(H[0]))): # # we have collapse operators, or rho0 is not a ket, # or H is a Liouvillian # # # find out if we are dealing with all-constant hamiltonian and # collapse operators or if we have at least one time-dependent # operator. Then delegate to appropriate solver... # if isinstance(H, Qobj): # constant hamiltonian if n_func == 0 and n_str == 0: # constant collapse operators res = _mesolve_const(H, rho0, tlist, c_ops, e_ops, args, options, progress_bar) elif n_str > 0: # constant hamiltonian but time-dependent collapse # operators in list string format res = _mesolve_list_str_td([H], rho0, tlist, c_ops, e_ops, args, options, progress_bar) elif n_func > 0: # constant hamiltonian but time-dependent collapse # operators in list function format res = _mesolve_list_func_td([H], rho0, tlist, c_ops, e_ops, args, options, progress_bar) elif isinstance( H, (types.FunctionType, types.BuiltinFunctionType, partial)): # function-callback style time-dependence: must have constant # collapse operators if n_str > 0: # or n_func > 0: raise TypeError("Incorrect format: function-format " + "Hamiltonian cannot be mixed with " + "time-dependent collapse operators.") else: res = _mesolve_func_td(H, rho0, tlist, c_ops, e_ops, args, options, progress_bar) elif isinstance(H, list): # determine if we are dealing with list of [Qobj, string] or # [Qobj, function] style time-dependencies (for pure python and # cython, respectively) if n_func > 0: res = _mesolve_list_func_td(H, rho0, tlist, c_ops, e_ops, args, options, progress_bar) else: res = _mesolve_list_str_td(H, rho0, tlist, c_ops, e_ops, args, options, progress_bar) else: raise TypeError("Incorrect specification of Hamiltonian " + "or collapse operators.") else: # # no collapse operators: unitary dynamics # if n_func > 0: res = _sesolve_list_func_td(H, rho0, tlist, e_ops, args, options, progress_bar) elif n_str > 0: res = _sesolve_list_str_td(H, rho0, tlist, e_ops, args, options, progress_bar) elif isinstance( H, (types.FunctionType, types.BuiltinFunctionType, partial)): res = _sesolve_func_td(H, rho0, tlist, e_ops, args, options, progress_bar) else: res = _sesolve_const(H, rho0, tlist, e_ops, args, options, progress_bar) if e_ops_dict: res.expect = { e: res.expect[n] for n, e in enumerate(e_ops_dict.keys()) } return res
def sesolve(H, psi0, tlist, e_ops=[], args={}, options=Options(), progress_bar=BaseProgressBar(), _safe_mode=True): """ Schrodinger equation evolution of a state vector or unitary matrix for a given Hamiltonian. Evolve the state vector (`psi0`) using a given Hamiltonian (`H`), by integrating the set of ordinary differential equations that define the system. Alternatively evolve a unitary matrix in solving the Schrodinger operator equation. The output is either the state vector or unitary matrix at arbitrary points in time (`tlist`), or the expectation values of the supplied operators (`e_ops`). If e_ops is a callback function, it is invoked for each time in `tlist` with time and the state as arguments, and the function does not use any return values. e_ops cannot be used in conjunction with solving the Schrodinger operator equation Parameters ---------- H : :class:`qutip.qobj`, :class:`qutip.qobjevo`, *list*, *callable* system Hamiltonian as a Qobj, list of Qobj and coefficient, QobjEvo, or a callback function for time-dependent Hamiltonians. list format and options can be found in QobjEvo's description. psi0 : :class:`qutip.qobj` initial state vector (ket) or initial unitary operator `psi0 = U` tlist : *list* / *array* list of times for :math:`t`. e_ops : list of :class:`qutip.qobj` / callback function single operator or list of operators for which to evaluate expectation values. For list operator evolution, the overlapse is computed: tr(e_ops[i].dag()*op(t)) args : *dictionary* dictionary of parameters for time-dependent Hamiltonians options : :class:`qutip.Qdeoptions` with options for the ODE solver. progress_bar : BaseProgressBar Optional instance of BaseProgressBar, or a subclass thereof, for showing the progress of the simulation. Returns ------- output: :class:`qutip.solver` An instance of the class :class:`qutip.solver`, which contains either an *array* of expectation values for the times specified by `tlist`, or an *array* or state vectors corresponding to the times in `tlist` [if `e_ops` is an empty list], or nothing if a callback function was given inplace of operators for which to calculate the expectation values. """ if isinstance(e_ops, Qobj): e_ops = [e_ops] elif isinstance(e_ops, dict): e_ops_dict = e_ops e_ops = [e for e in e_ops.values()] else: e_ops_dict = None if progress_bar is True: progress_bar = TextProgressBar() if not (psi0.isket or psi0.isunitary): raise TypeError("The unitary solver requires psi0 to be" " a ket as initial state" " or a unitary as initial operator.") if options.rhs_reuse and not isinstance(H, SolverSystem): # TODO: deprecate when going to class based solver. if "sesolve" in solver_safe: # print(" ") H = solver_safe["sesolve"] else: pass # raise Exception("Could not find the Hamiltonian to reuse.") #check if should use OPENMP check_use_openmp(options) if isinstance(H, SolverSystem): ss = H elif isinstance(H, (list, Qobj, QobjEvo)): ss = _sesolve_QobjEvo(H, tlist, args, options) elif callable(H): ss = _sesolve_func_td(H, args, options) else: raise Exception("Invalid H type") func, ode_args = ss.makefunc(ss, psi0, args, options) if _safe_mode: v = psi0.full().ravel('F') func(0., v, *ode_args) + v res = _generic_ode_solve(func, ode_args, psi0, tlist, e_ops, options, progress_bar, dims=psi0.dims) if e_ops_dict: res.expect = { e: res.expect[n] for n, e in enumerate(e_ops_dict.keys()) } res.SolverSystem = ss return res
def propagator(H, t, c_op_list=[], args={}, options=None, unitary_mode='batch', parallel=False, progress_bar=None, _safe_mode=True, **kwargs): r""" Calculate the propagator U(t) for the density matrix or wave function such that :math:`\psi(t) = U(t)\psi(0)` or :math:`\rho_{\mathrm vec}(t) = U(t) \rho_{\mathrm vec}(0)` where :math:`\rho_{\mathrm vec}` is the vector representation of the density matrix. Parameters ---------- H : qobj or list Hamiltonian as a Qobj instance of a nested list of Qobjs and coefficients in the list-string or list-function format for time-dependent Hamiltonians (see description in :func:`qutip.mesolve`). t : float or array-like Time or list of times for which to evaluate the propagator. c_op_list : list List of qobj collapse operators. args : list/array/dictionary Parameters to callback functions for time-dependent Hamiltonians and collapse operators. options : :class:`qutip.Options` with options for the ODE solver. unitary_mode = str ('batch', 'single') Solve all basis vectors simulaneously ('batch') or individually ('single'). parallel : bool {False, True} Run the propagator in parallel mode. This will override the unitary_mode settings if set to True. progress_bar: BaseProgressBar Optional instance of BaseProgressBar, or a subclass thereof, for showing the progress of the simulation. By default no progress bar is used, and if set to True a TextProgressBar will be used. Returns ------- a : qobj Instance representing the propagator :math:`U(t)`. """ kw = _default_kwargs() if 'num_cpus' in kwargs: num_cpus = kwargs['num_cpus'] else: num_cpus = kw['num_cpus'] if progress_bar is None: progress_bar = BaseProgressBar() elif progress_bar is True: progress_bar = TextProgressBar() if options is None: options = Options() options.rhs_reuse = True rhs_clear() if isinstance(t, (int, float, np.integer, np.floating)): tlist = [0, t] else: tlist = t if _safe_mode: _solver_safety_check(H, None, c_ops=c_op_list, e_ops=[], args=args) td_type = _td_format_check(H, c_op_list, solver='me') if isinstance( H, (types.FunctionType, types.BuiltinFunctionType, functools.partial)): H0 = H(0.0, args) if unitary_mode == 'batch': # batch don't work with function Hamiltonian unitary_mode = 'single' elif isinstance(H, list): H0 = H[0][0] if isinstance(H[0], list) else H[0] else: H0 = H if len(c_op_list) == 0 and H0.isoper: # calculate propagator for the wave function N = H0.shape[0] dims = H0.dims if parallel: unitary_mode = 'single' u = np.zeros([N, N, len(tlist)], dtype=complex) output = parallel_map(_parallel_sesolve, range(N), task_args=(N, H, tlist, args, options), progress_bar=progress_bar, num_cpus=num_cpus) for n in range(N): for k, t in enumerate(tlist): u[:, n, k] = output[n].states[k].full().T else: if unitary_mode == 'single': output = sesolve(H, qeye(dims[0]), tlist, [], args, options, _safe_mode=False) if len(tlist) == 2: return output.states[-1] else: return output.states elif unitary_mode == 'batch': u = np.zeros(len(tlist), dtype=object) _rows = np.array([(N + 1) * m for m in range(N)]) _cols = np.zeros_like(_rows) _data = np.ones_like(_rows, dtype=complex) psi0 = Qobj(sp.coo_matrix((_data, (_rows, _cols))).tocsr()) if td_type[1] > 0 or td_type[2] > 0: H2 = [] for k in range(len(H)): if isinstance(H[k], list): H2.append([tensor(qeye(N), H[k][0]), H[k][1]]) else: H2.append(tensor(qeye(N), H[k])) else: H2 = tensor(qeye(N), H) options.normalize_output = False output = sesolve(H2, psi0, tlist, [], args=args, options=options, _safe_mode=False) for k, t in enumerate(tlist): u[k] = sp_reshape(output.states[k].data, (N, N)) unit_row_norm(u[k].data, u[k].indptr, u[k].shape[0]) u[k] = u[k].T.tocsr() else: raise Exception('Invalid unitary mode.') elif len(c_op_list) == 0 and H0.issuper: # calculate the propagator for the vector representation of the # density matrix (a superoperator propagator) unitary_mode = 'single' N = H0.shape[0] sqrt_N = int(np.sqrt(N)) dims = H0.dims u = np.zeros([N, N, len(tlist)], dtype=complex) if parallel: output = parallel_map(_parallel_mesolve, range(N * N), task_args=(sqrt_N, H, tlist, c_op_list, args, options), progress_bar=progress_bar, num_cpus=num_cpus) for n in range(N * N): for k, t in enumerate(tlist): u[:, n, k] = mat2vec(output[n].states[k].full()).T else: rho0 = qeye(N, N) rho0.dims = [[sqrt_N, sqrt_N], [sqrt_N, sqrt_N]] output = mesolve(H, psi0, tlist, [], args, options, _safe_mode=False) if len(tlist) == 2: return output.states[-1] else: return output.states else: # calculate the propagator for the vector representation of the # density matrix (a superoperator propagator) unitary_mode = 'single' N = H0.shape[0] dims = [H0.dims, H0.dims] u = np.zeros([N * N, N * N, len(tlist)], dtype=complex) if parallel: output = parallel_map(_parallel_mesolve, range(N * N), task_args=(N, H, tlist, c_op_list, args, options), progress_bar=progress_bar, num_cpus=num_cpus) for n in range(N * N): for k, t in enumerate(tlist): u[:, n, k] = mat2vec(output[n].states[k].full()).T else: progress_bar.start(N * N) for n in range(N * N): progress_bar.update(n) col_idx, row_idx = np.unravel_index(n, (N, N)) rho0 = Qobj( sp.csr_matrix(([1], ([row_idx], [col_idx])), shape=(N, N), dtype=complex)) output = mesolve(H, rho0, tlist, c_op_list, [], args, options, _safe_mode=False) for k, t in enumerate(tlist): u[:, n, k] = mat2vec(output.states[k].full()).T progress_bar.finished() if len(tlist) == 2: if unitary_mode == 'batch': return Qobj(u[-1], dims=dims) else: return Qobj(u[:, :, 1], dims=dims) else: if unitary_mode == 'batch': return np.array([Qobj(u[k], dims=dims) for k in range(len(tlist))], dtype=object) else: return np.array( [Qobj(u[:, :, k], dims=dims) for k in range(len(tlist))], dtype=object)
def sesolve(H, psi0, tlist, e_ops=None, args=None, options=None, progress_bar=None, _safe_mode=True): """ Schrödinger equation evolution of a state vector or unitary matrix for a given Hamiltonian. Evolve the state vector (``psi0``) using a given Hamiltonian (``H``), by integrating the set of ordinary differential equations that define the system. Alternatively evolve a unitary matrix in solving the Schrodinger operator equation. The output is either the state vector or unitary matrix at arbitrary points in time (``tlist``), or the expectation values of the supplied operators (``e_ops``). If ``e_ops`` is a callback function, it is invoked for each time in ``tlist`` with time and the state as arguments, and the function does not use any return values. ``e_ops`` cannot be used in conjunction with solving the Schrodinger operator equation Parameters ---------- H : :class:`~Qobj`, :class:`~QobjEvo`, list, or callable System Hamiltonian as a :obj:`~Qobj , list of :obj:`Qobj` and coefficient, :obj:`~QObjEvo`, or a callback function for time-dependent Hamiltonians. List format and options can be found in QobjEvo's description. psi0 : :class:`~Qobj` Initial state vector (ket) or initial unitary operator ``psi0 = U``. tlist : array_like of float List of times for :math:`t`. e_ops : None / list / callback function, optional A list of operators as `Qobj` and/or callable functions (can be mixed) or a single callable function. For callable functions, they are called as ``f(t, state)`` and return the expectation value. A single callback's expectation value can be any type, but a callback part of a list must return a number as the expectation value. For operators, the result's expect will be computed by :func:`qutip.expect` when the state is a ``ket``. For operator evolution, the overlap is computed by: :: (e_ops[i].dag() * op(t)).tr() args : dict, optional Dictionary of scope parameters for time-dependent Hamiltonians. options : :obj:`~solver.Options`, optional Options for the ODE solver. progress_bar : :obj:`~BaseProgressBar`, optional Optional instance of :obj:`~BaseProgressBar`, or a subclass thereof, for showing the progress of the simulation. Returns ------- output: :class:`~solver.Result` An instance of the class :class:`~solver.Result`, which contains either an array of expectation values for the times specified by ``tlist``, or an array or state vectors corresponding to the times in ``tlist`` (if ``e_ops`` is an empty list), or nothing if a callback function was given inplace of operators for which to calculate the expectation values. """ if e_ops is None: e_ops = [] if isinstance(e_ops, Qobj): e_ops = [e_ops] elif isinstance(e_ops, dict): e_ops_dict = e_ops e_ops = [e for e in e_ops.values()] else: e_ops_dict = None if progress_bar is None: progress_bar = BaseProgressBar() if progress_bar is True: progress_bar = TextProgressBar() if not (psi0.isket or psi0.isunitary): raise TypeError("The unitary solver requires psi0 to be" " a ket as initial state" " or a unitary as initial operator.") if options is None: options = Options() if options.rhs_reuse and not isinstance(H, SolverSystem): # TODO: deprecate when going to class based solver. if "sesolve" in solver_safe: H = solver_safe["sesolve"] if args is None: args = {} check_use_openmp(options) if isinstance(H, SolverSystem): ss = H elif isinstance(H, (list, Qobj, QobjEvo)): ss = _sesolve_QobjEvo(H, tlist, args, options) elif callable(H): ss = _sesolve_func_td(H, args, options) else: raise Exception("Invalid H type") func, ode_args = ss.makefunc(ss, psi0, args, e_ops, options) if _safe_mode: v = psi0.full().ravel('F') func(0., v, *ode_args) + v res = _generic_ode_solve(func, ode_args, psi0, tlist, e_ops, options, progress_bar, dims=psi0.dims) if e_ops_dict: res.expect = {e: res.expect[n] for n, e in enumerate(e_ops_dict.keys())} return res
def parallel_map(task, values, task_args=tuple(), task_kwargs={}, **kwargs): """ Parallel execution of a mapping of `values` to the function `task`. This is functionally equivalent to:: result = [task(value, *task_args, **task_kwargs) for value in values] Parameters ---------- task : a Python function The function that is to be called for each value in ``task_vec``. values : array / list The list or array of values for which the ``task`` function is to be evaluated. task_args : list / dictionary The optional additional argument to the ``task`` function. task_kwargs : list / dictionary The optional additional keyword argument to the ``task`` function. progress_bar : ProgressBar Progress bar class instance for showing progress. Returns -------- result : list The result list contains the value of ``task(value, *task_args, **task_kwargs)`` for each value in ``values``. """ os.environ['QUTIP_IN_PARALLEL'] = 'TRUE' kw = _default_kwargs() if 'num_cpus' in kwargs: kw['num_cpus'] = kwargs['num_cpus'] try: progress_bar = kwargs['progress_bar'] if progress_bar is True: progress_bar = TextProgressBar() except: progress_bar = BaseProgressBar() progress_bar.start(len(values)) nfinished = [0] def _update_progress_bar(x): nfinished[0] += 1 progress_bar.update(nfinished[0]) try: pool = Pool(processes=kw['num_cpus']) async_res = [pool.apply_async(task, (value,) + task_args, task_kwargs, _update_progress_bar) for value in values] while not all([ar.ready() for ar in async_res]): for ar in async_res: ar.wait(timeout=0.1) pool.terminate() pool.join() except KeyboardInterrupt as e: os.environ['QUTIP_IN_PARALLEL'] = 'FALSE' pool.terminate() pool.join() raise e progress_bar.finished() os.environ['QUTIP_IN_PARALLEL'] = 'FALSE' return [ar.get() for ar in async_res]
def sesolve(H, psi0, tlist, e_ops=[], args={}, options=None, progress_bar=None, _safe_mode=True): """ Schrodinger equation evolution of a state vector or unitary matrix for a given Hamiltonian. Evolve the state vector (`psi0`) using a given Hamiltonian (`H`), by integrating the set of ordinary differential equations that define the system. Alternatively evolve a unitary matrix in solving the Schrodinger operator equation. The output is either the state vector or unitary matrix at arbitrary points in time (`tlist`), or the expectation values of the supplied operators (`e_ops`). If e_ops is a callback function, it is invoked for each time in `tlist` with time and the state as arguments, and the function does not use any return values. e_ops cannot be used in conjunction with solving the Schrodinger operator equation Parameters ---------- H : :class:`qutip.qobj` system Hamiltonian, or a callback function for time-dependent Hamiltonians. psi0 : :class:`qutip.qobj` initial state vector (ket) or initial unitary operator `psi0 = U` tlist : *list* / *array* list of times for :math:`t`. e_ops : list of :class:`qutip.qobj` / callback function single single operator or list of operators for which to evaluate expectation values. Must be empty list operator evolution args : *dictionary* dictionary of parameters for time-dependent Hamiltonians options : :class:`qutip.Qdeoptions` with options for the ODE solver. progress_bar : BaseProgressBar Optional instance of BaseProgressBar, or a subclass thereof, for showing the progress of the simulation. Returns ------- output: :class:`qutip.solver` An instance of the class :class:`qutip.solver`, which contains either an *array* of expectation values for the times specified by `tlist`, or an *array* or state vectors corresponding to the times in `tlist` [if `e_ops` is an empty list], or nothing if a callback function was given inplace of operators for which to calculate the expectation values. """ # check initial state: must be a state vector if _safe_mode: if not isinstance(psi0, Qobj): raise TypeError("psi0 must be Qobj") if psi0.isket: pass elif psi0.isunitary: if not e_ops == []: raise TypeError("Must have e_ops = [] when initial condition" " psi0 is a unitary operator.") else: raise TypeError("The unitary solver requires psi0 to be" " a ket as initial state" " or a unitary as initial operator.") _solver_safety_check(H, psi0, c_ops=[], e_ops=e_ops, args=args) if isinstance(e_ops, Qobj): e_ops = [e_ops] if isinstance(e_ops, dict): e_ops_dict = e_ops e_ops = [e for e in e_ops.values()] else: e_ops_dict = None if progress_bar is None: progress_bar = BaseProgressBar() elif progress_bar is True: progress_bar = TextProgressBar() # convert array based time-dependence to string format H, _, args = _td_wrap_array_str(H, [], args, tlist) # check for type (if any) of time-dependent inputs n_const, n_func, n_str = _td_format_check(H, []) if options is None: options = Options() if (not options.rhs_reuse) or (not config.tdfunc): # reset config time-dependence flags to default values config.reset() #check if should use OPENMP check_use_openmp(options) if n_func > 0: res = _sesolve_list_func_td(H, psi0, tlist, e_ops, args, options, progress_bar) elif n_str > 0: res = _sesolve_list_str_td(H, psi0, tlist, e_ops, args, options, progress_bar) elif isinstance(H, (types.FunctionType, types.BuiltinFunctionType, partial)): res = _sesolve_func_td(H, psi0, tlist, e_ops, args, options, progress_bar) elif isinstance(H, Qobj): res = _sesolve_const(H, psi0, tlist, e_ops, args, options, progress_bar) else: raise TypeError("Invalid Hamiltonian specification") if e_ops_dict: res.expect = {e: res.expect[n] for n, e in enumerate(e_ops_dict.keys())} return res
def run(self, num_traj=0, psi0=None, tlist=None, args={}, e_ops=None, options=None, progress_bar=True, map_func=parallel_map, map_kwargs={}): # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% # 4 situation for run: # - first run # - change parameters # - add trajectories # (self.add_traj) Not Implemented # - continue from the last time and states # (self.continue_runs) Not Implemented # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% options = options if options is not None else self.options if self.ran and tlist[0] == self.t: # psi0 is ignored since we restart from a # different states for each trajectories self.continue_runs(num_traj, tlist, args, e_ops, options, progress_bar, map_func, map_kwargs) return if args and args != self.ss.args: self.ss.set_args(self.ss, args) self.reset() if e_ops and e_ops != self.e_ops: self.set_e_ops(e_ops) self.reset() if psi0 is not None and psi0 != self.psi0: self.psi0 = psi0 self.reset() tlist = np.array(tlist) if tlist is not None and np.all(tlist != self.tlist): self.tlist = tlist self.reset() if self.ran: if options.store_states and self._psi_out[0].shape[0] == 1: self.reset() else: # if not reset here, add trajectories self.add_traj(num_traj, progress_bar, map_func, map_kwargs) return if not num_traj: num_traj = options.ntraj if options.num_cpus == 1 or num_traj == 1: map_func = serial_map if len(self.seeds) != num_traj: self.seed(num_traj, self.seeds) if not progress_bar: progress_bar = BaseProgressBar() elif progress_bar is True: progress_bar = TextProgressBar() # set arguments for input to monte carlo map_kwargs_ = {'progress_bar': progress_bar, 'num_cpus': options.num_cpus} map_kwargs_.update(map_kwargs) map_kwargs = map_kwargs_ if self.e_ops is None: self.set_e_ops() if self.ss.type == "Diagonal": results = map_func(self._single_traj_diag, list(range(num_traj)), **map_kwargs) else: results = map_func(self._single_traj, list(range(num_traj)), **map_kwargs) self.t = self.tlist[-1] self.num_traj = num_traj self.ran = True for result in results: state_out, ss_out, expect, collapse = result self._psi_out.append(state_out) self._ss_out.append(ss_out) self._expect_out.append(expect) self._collapse.append(collapse) self._psi_out = np.stack(self._psi_out) self._ss_out = np.stack(self._ss_out)
def propagator(H, t, c_op_list=[], args={}, options=None, parallel=False, progress_bar=None, **kwargs): """ Calculate the propagator U(t) for the density matrix or wave function such that :math:`\psi(t) = U(t)\psi(0)` or :math:`\\rho_{\mathrm vec}(t) = U(t) \\rho_{\mathrm vec}(0)` where :math:`\\rho_{\mathrm vec}` is the vector representation of the density matrix. Parameters ---------- H : qobj or list Hamiltonian as a Qobj instance of a nested list of Qobjs and coefficients in the list-string or list-function format for time-dependent Hamiltonians (see description in :func:`qutip.mesolve`). t : float or array-like Time or list of times for which to evaluate the propagator. c_op_list : list List of qobj collapse operators. args : list/array/dictionary Parameters to callback functions for time-dependent Hamiltonians and collapse operators. options : :class:`qutip.Options` with options for the ODE solver. parallel : bool {False, True} Run the propagator in parallel mode. progress_bar: BaseProgressBar Optional instance of BaseProgressBar, or a subclass thereof, for showing the progress of the simulation. By default no progress bar is used, and if set to True a TextProgressBar will be used. Returns ------- a : qobj Instance representing the propagator :math:`U(t)`. """ kw = _default_kwargs() if 'num_cpus' in kwargs: num_cpus = kwargs['num_cpus'] else: num_cpus = kw['num_cpus'] if progress_bar is None: progress_bar = BaseProgressBar() elif progress_bar is True: progress_bar = TextProgressBar() if options is None: options = Options() options.rhs_reuse = True rhs_clear() if isinstance(t, (int, float, np.integer, np.floating)): tlist = [0, t] else: tlist = t td_type = _td_format_check(H, c_op_list, solver='me')[2] if td_type > 0: rhs_generate(H, c_op_list, args=args, options=options) if isinstance(H, (types.FunctionType, types.BuiltinFunctionType, functools.partial)): H0 = H(0.0, args) elif isinstance(H, list): H0 = H[0][0] if isinstance(H[0], list) else H[0] else: H0 = H if len(c_op_list) == 0 and H0.isoper: # calculate propagator for the wave function N = H0.shape[0] dims = H0.dims u = np.zeros([N, N, len(tlist)], dtype=complex) if parallel: output = parallel_map(_parallel_sesolve,range(N), task_args=(N,H,tlist,args,options), progress_bar=progress_bar, num_cpus=num_cpus) for n in range(N): for k, t in enumerate(tlist): u[:, n, k] = output[n].states[k].full().T else: progress_bar.start(N) for n in range(0, N): progress_bar.update(n) psi0 = basis(N, n) output = sesolve(H, psi0, tlist, [], args, options) for k, t in enumerate(tlist): u[:, n, k] = output.states[k].full().T progress_bar.finished() # todo: evolving a batch of wave functions: # psi_0_list = [basis(N, n) for n in range(N)] # psi_t_list = mesolve(H, psi_0_list, [0, t], [], [], args, options) # for n in range(0, N): # u[:,n] = psi_t_list[n][1].full().T elif len(c_op_list) == 0 and H0.issuper: # calculate the propagator for the vector representation of the # density matrix (a superoperator propagator) N = H0.shape[0] sqrt_N = int(np.sqrt(N)) dims = H0.dims u = np.zeros([N, N, len(tlist)], dtype=complex) if parallel: output = parallel_map(_parallel_mesolve,range(N * N), task_args=(sqrt_N,H,tlist,c_op_list,args,options), progress_bar=progress_bar, num_cpus=num_cpus) for n in range(N * N): for k, t in enumerate(tlist): u[:, n, k] = mat2vec(output[n].states[k].full()).T else: progress_bar.start(N) for n in range(0, N): progress_bar.update(n) col_idx, row_idx = np.unravel_index(n,(sqrt_N,sqrt_N)) rho0 = Qobj(sp.csr_matrix(([1],([row_idx],[col_idx])), shape=(sqrt_N,sqrt_N), dtype=complex)) output = mesolve(H, rho0, tlist, [], [], args, options) for k, t in enumerate(tlist): u[:, n, k] = mat2vec(output.states[k].full()).T progress_bar.finished() else: # calculate the propagator for the vector representation of the # density matrix (a superoperator propagator) N = H0.shape[0] dims = [H0.dims, H0.dims] u = np.zeros([N * N, N * N, len(tlist)], dtype=complex) if parallel: output = parallel_map(_parallel_mesolve,range(N * N), task_args=(N,H,tlist,c_op_list,args,options), progress_bar=progress_bar, num_cpus=num_cpus) for n in range(N * N): for k, t in enumerate(tlist): u[:, n, k] = mat2vec(output[n].states[k].full()).T else: progress_bar.start(N * N) for n in range(N * N): progress_bar.update(n) col_idx, row_idx = np.unravel_index(n,(N,N)) rho0 = Qobj(sp.csr_matrix(([1],([row_idx],[col_idx])), shape=(N,N), dtype=complex)) output = mesolve(H, rho0, tlist, c_op_list, [], args, options) for k, t in enumerate(tlist): u[:, n, k] = mat2vec(output.states[k].full()).T progress_bar.finished() if len(tlist) == 2: return Qobj(u[:, :, 1], dims=dims) else: return np.array([Qobj(u[:, :, k], dims=dims) for k in range(len(tlist))], dtype=object)
def brmesolve(H, psi0, tlist, a_ops=[], e_ops=[], c_ops=[], args={}, use_secular=True, tol=qset.atol, spectra_cb=None, options=None, progress_bar=None, _safe_mode=True): """ Solves for the dynamics of a system using the Bloch-Redfield master equation, given an input Hamiltonian, Hermitian bath-coupling terms and their associated spectrum functions, as well as possible Lindblad collapse operators. For time-independent systems, the Hamiltonian must be given as a Qobj, whereas the bath-coupling terms (a_ops), must be written as a nested list of operator - spectrum function pairs, where the frequency is specified by the `w` variable. *Example* a_ops = [[a+a.dag(),lambda w: 0.2*(w>=0)]] For time-dependent systems, the Hamiltonian, a_ops, and Lindblad collapse operators (c_ops), can be specified in the QuTiP string-based time-dependent format. For the a_op spectra, the frequency variable must be `w`, and the string cannot contain any other variables other than the possibility of having a time-dependence through the time variable `t`: *Example* a_ops = [[a+a.dag(), '0.2*exp(-t)*(w>=0)']] Parameters ---------- H : Qobj / list System Hamiltonian given as a Qobj or nested list in string-based format. psi0: Qobj Initial density matrix or state vector (ket). tlist : array_like List of times for evaluating evolution a_ops : list Nested list of Hermitian system operators that couple to the bath degrees of freedom, along with their associated spectra. e_ops : list List of operators for which to evaluate expectation values. c_ops : list List of system collapse operators, or nested list in string-based format. args : dict (not implimented) Placeholder for future implementation, kept for API consistency. use_secular : bool {True} Use secular approximation when evaluating bath-coupling terms. tol : float {qutip.setttings.atol} Tolerance used for removing small values after basis transformation. spectra_cb : list DEPRECIATED. Do not use. options : :class:`qutip.solver.Options` Options for the solver. progress_bar : BaseProgressBar Optional instance of BaseProgressBar, or a subclass thereof, for showing the progress of the simulation. Returns ------- result: :class:`qutip.solver.Result` An instance of the class :class:`qutip.solver.Result`, which contains either an array of expectation values, for operators given in e_ops, or a list of states for the times specified by `tlist`. """ if isinstance(c_ops, Qobj): c_ops = [c_ops] if isinstance(e_ops, Qobj): e_ops = [e_ops] if isinstance(e_ops, dict): e_ops_dict = e_ops e_ops = [e for e in e_ops.values()] else: e_ops_dict = None 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 if _safe_mode: _solver_safety_check(H, psi0, a_ops + c_ops, e_ops, args) # check for type (if any) of time-dependent inputs _, n_func, n_str = _td_format_check(H, a_ops + c_ops) if progress_bar is None: progress_bar = BaseProgressBar() elif progress_bar is True: progress_bar = TextProgressBar() if options is None: options = Options() if (not options.rhs_reuse) or (not config.tdfunc): # reset config collapse and time-dependence flags to default values config.reset() #check if should use OPENMP check_use_openmp(options) if n_str == 0: R, ekets = bloch_redfield_tensor(H, a_ops, spectra_cb=None, c_ops=c_ops) output = Result() output.solver = "brmesolve" output.times = tlist results = bloch_redfield_solve(R, ekets, psi0, tlist, e_ops, options, progress_bar=progress_bar) if e_ops: output.expect = results else: output.states = results return output elif n_str != 0 and n_func == 0: output = _td_brmesolve(H, psi0, tlist, a_ops=a_ops, e_ops=e_ops, c_ops=c_ops, use_secular=use_secular, tol=tol, options=options, progress_bar=progress_bar, _safe_mode=_safe_mode) return output else: raise Exception('Cannot mix func and str formats.')
def sesolve(H, rho0, tlist, e_ops, args={}, options=None, progress_bar=BaseProgressBar()): """ Schrodinger equation evolution of a state vector for a given Hamiltonian. Evolve the state vector or density matrix (`rho0`) using a given Hamiltonian (`H`), by integrating the set of ordinary differential equations that define the system. The output is either the state vector at arbitrary points in time (`tlist`), or the expectation values of the supplied operators (`e_ops`). If e_ops is a callback function, it is invoked for each time in `tlist` with time and the state as arguments, and the function does not use any return values. Parameters ---------- H : :class:`qutip.qobj` system Hamiltonian, or a callback function for time-dependent Hamiltonians. rho0 : :class:`qutip.qobj` initial density matrix or state vector (ket). tlist : *list* / *array* list of times for :math:`t`. e_ops : list of :class:`qutip.qobj` / callback function single single operator or list of operators for which to evaluate expectation values. args : *dictionary* dictionary of parameters for time-dependent Hamiltonians and collapse operators. options : :class:`qutip.Qdeoptions` with options for the ODE solver. Returns ------- output: :class:`qutip.odedata` An instance of the class :class:`qutip.odedata`, which contains either an *array* of expectation values for the times specified by `tlist`, or an *array* or state vectors or density matrices corresponding to the times in `tlist` [if `e_ops` is an empty list], or nothing if a callback function was given inplace of operators for which to calculate the expectation values. """ if isinstance(e_ops, Qobj): e_ops = [e_ops] if isinstance(e_ops, dict): e_ops_dict = e_ops e_ops = [e for e in e_ops.values()] else: e_ops_dict = None # check for type (if any) of time-dependent inputs n_const, n_func, n_str = _ode_checks(H, []) if options is None: options = Odeoptions() if (not options.rhs_reuse) or (not odeconfig.tdfunc): # reset odeconfig time-dependence flags to default values odeconfig.reset() if n_func > 0: res = _sesolve_list_func_td(H, rho0, tlist, e_ops, args, options, progress_bar) elif n_str > 0: res = _sesolve_list_str_td(H, rho0, tlist, e_ops, args, options, progress_bar) elif isinstance(H, (types.FunctionType, types.BuiltinFunctionType, partial)): res = _sesolve_func_td(H, rho0, tlist, e_ops, args, options, progress_bar) else: res = _sesolve_const(H, rho0, tlist, e_ops, args, options, progress_bar) if e_ops_dict: res.expect = {e: res.expect[n] for n, e in enumerate(e_ops_dict.keys())} return res
def propagator(H, t, c_op_list, args=None, options=None, sparse=False, progress_bar=None): """ Calculate the propagator U(t) for the density matrix or wave function such that :math:`\psi(t) = U(t)\psi(0)` or :math:`\\rho_{\mathrm vec}(t) = U(t) \\rho_{\mathrm vec}(0)` where :math:`\\rho_{\mathrm vec}` is the vector representation of the density matrix. Parameters ---------- H : qobj or list Hamiltonian as a Qobj instance of a nested list of Qobjs and coefficients in the list-string or list-function format for time-dependent Hamiltonians (see description in :func:`qutip.mesolve`). t : float or array-like Time or list of times for which to evaluate the propagator. c_op_list : list List of qobj collapse operators. args : list/array/dictionary Parameters to callback functions for time-dependent Hamiltonians and collapse operators. options : :class:`qutip.Options` with options for the ODE solver. progress_bar: BaseProgressBar Optional instance of BaseProgressBar, or a subclass thereof, for showing the progress of the simulation. By default no progress bar is used, and if set to True a TextProgressBar will be used. Returns ------- a : qobj Instance representing the propagator :math:`U(t)`. """ if progress_bar is None: progress_bar = BaseProgressBar() elif progress_bar is True: progress_bar = TextProgressBar() if options is None: options = Options() options.rhs_reuse = True rhs_clear() if isinstance(t, (int, float, np.integer, np.floating)): tlist = [0, t] else: tlist = t if isinstance( H, (types.FunctionType, types.BuiltinFunctionType, functools.partial)): H0 = H(0.0, args) elif isinstance(H, list): H0 = H[0][0] if isinstance(H[0], list) else H[0] else: H0 = H if len(c_op_list) == 0 and H0.isoper: # calculate propagator for the wave function N = H0.shape[0] dims = H0.dims u = np.zeros([N, N, len(tlist)], dtype=complex) progress_bar.start(N) for n in range(0, N): progress_bar.update(n) psi0 = basis(N, n) output = sesolve(H, psi0, tlist, [], args, options) for k, t in enumerate(tlist): u[:, n, k] = output.states[k].full().T progress_bar.finished() # todo: evolving a batch of wave functions: # psi_0_list = [basis(N, n) for n in range(N)] # psi_t_list = mesolve(H, psi_0_list, [0, t], [], [], args, options) # for n in range(0, N): # u[:,n] = psi_t_list[n][1].full().T elif len(c_op_list) == 0 and H0.issuper: # calculate the propagator for the vector representation of the # density matrix (a superoperator propagator) N = H0.shape[0] dims = H0.dims u = np.zeros([N, N, len(tlist)], dtype=complex) progress_bar.start(N) for n in range(0, N): progress_bar.update(n) psi0 = basis(N, n) rho0 = Qobj(vec2mat(psi0.full())) output = mesolve(H, rho0, tlist, [], [], args, options) for k, t in enumerate(tlist): u[:, n, k] = mat2vec(output.states[k].full()).T progress_bar.finished() else: # calculate the propagator for the vector representation of the # density matrix (a superoperator propagator) N = H0.shape[0] dims = [H0.dims, H0.dims] u = np.zeros([N * N, N * N, len(tlist)], dtype=complex) if sparse: progress_bar.start(N * N) for n in range(N * N): progress_bar.update(n) psi0 = basis(N * N, n) psi0.dims = [dims[0], 1] rho0 = vector_to_operator(psi0) output = mesolve(H, rho0, tlist, c_op_list, [], args, options) for k, t in enumerate(tlist): u[:, n, k] = operator_to_vector( output.states[k]).full(squeeze=True) progress_bar.finished() else: progress_bar.start(N * N) for n in range(N * N): progress_bar.update(n) psi0 = basis(N * N, n) rho0 = Qobj(vec2mat(psi0.full())) output = mesolve(H, rho0, tlist, c_op_list, [], args, options) for k, t in enumerate(tlist): u[:, n, k] = mat2vec(output.states[k].full()).T progress_bar.finished() if len(tlist) == 2: return Qobj(u[:, :, 1], dims=dims) else: return [Qobj(u[:, :, k], dims=dims) for k in range(len(tlist))]
def mesolve(H, rho0, tlist, c_ops, e_ops, args={}, options=None, progress_bar=BaseProgressBar()): """ Master equation evolution of a density matrix for a given Hamiltonian. Evolve the state vector or density matrix (`rho0`) using a given Hamiltonian (`H`) and an [optional] set of collapse operators (`c_op_list`), by integrating the set of ordinary differential equations that define the system. In the absense of collase operators the system is evolved according to the unitary evolution of the Hamiltonian. The output is either the state vector at arbitrary points in time (`tlist`), or the expectation values of the supplied operators (`e_ops`). If e_ops is a callback function, it is invoked for each time in `tlist` with time and the state as arguments, and the function does not use any return values. **Time-dependent operators** For problems with time-dependent problems `H` and `c_ops` can be callback functions that takes two arguments, time and `args`, and returns the Hamiltonian or Liuovillian for the system at that point in time (*callback format*). Alternatively, `H` and `c_ops` can be a specified in a nested-list format where each element in the list is a list of length 2, containing an operator (:class:`qutip.qobj`) at the first element and where the second element is either a string (*list string format*) or a callback function (*list callback format*) that evaluates to the time-dependent coefficient for the corresponding operator. *Examples* H = [[H0, 'sin(w*t)'], [H1, 'sin(2*w*t)']] H = [[H0, f0_t], [H1, f1_t]] where f0_t and f1_t are python functions with signature f_t(t, args). In the *list string format* and *list callback format*, the string expression and the callback function must evaluate to a real or complex number (coefficient for the corresponding operator). In all cases of time-dependent operators, `args` is a dictionary of parameters that is used when evaluating operators. It is passed to the callback functions as second argument .. note:: If an element in the list-specification of the Hamiltonian or the list of collapse operators are in super-operator for it will be added to the total Liouvillian of the problem with out further transformation. This allows for using mesolve for solving master equations that are not on standard Lindblad form. .. note:: On using callback function: mesolve transforms all :class:`qutip.qobj` objects to sparse matrices before handing the problem to the integrator function. In order for your callback function to work correctly, pass all :class:`qutip.qobj` objects that are used in constructing the Hamiltonian via args. odesolve will check for :class:`qutip.qobj` in `args` and handle the conversion to sparse matrices. All other :class:`qutip.qobj` objects that are not passed via `args` will be passed on to the integrator to scipy who will raise an NotImplemented exception. Parameters ---------- H : :class:`qutip.qobj` system Hamiltonian, or a callback function for time-dependent Hamiltonians. rho0 : :class:`qutip.qobj` initial density matrix or state vector (ket). tlist : *list* / *array* list of times for :math:`t`. c_ops : list of :class:`qutip.qobj` single collapse operator, or list of collapse operators. e_ops : list of :class:`qutip.qobj` / callback function single single operator or list of operators for which to evaluate expectation values. args : *dictionary* dictionary of parameters for time-dependent Hamiltonians and collapse operators. options : :class:`qutip.Odeoptions` with options for the ODE solver. Returns ------- output: :class:`qutip.odedata` An instance of the class :class:`qutip.odedata`, which contains either an *array* of expectation values for the times specified by `tlist`, or an *array* or state vectors or density matrices corresponding to the times in `tlist` [if `e_ops` is an empty list], or nothing if a callback function was given inplace of operators for which to calculate the expectation values. """ # check whether c_ops or e_ops is is a single operator # if so convert it to a list containing only that operator if isinstance(c_ops, Qobj): c_ops = [c_ops] if isinstance(e_ops, Qobj): e_ops = [e_ops] if isinstance(e_ops, dict): e_ops_dict = e_ops e_ops = [e for e in e_ops.values()] else: e_ops_dict = None # check for type (if any) of time-dependent inputs n_const, n_func, n_str = _ode_checks(H, c_ops) if options is None: options = Odeoptions() if (not options.rhs_reuse) or (not odeconfig.tdfunc): # reset odeconfig collapse and time-dependence flags to default values odeconfig.reset() res = None # # dispatch the appropriate solver # if ((c_ops and len(c_ops) > 0) or (not isket(rho0)) or (isinstance(H, Qobj) and issuper(H)) or (isinstance(H, list) and isinstance(H[0], Qobj) and issuper(H[0]))): # # we have collapse operators # # # find out if we are dealing with all-constant hamiltonian and # collapse operators or if we have at least one time-dependent # operator. Then delegate to appropriate solver... # if isinstance(H, Qobj): # constant hamiltonian if n_func == 0 and n_str == 0: # constant collapse operators res = _mesolve_const(H, rho0, tlist, c_ops, e_ops, args, options, progress_bar) elif n_str > 0: # constant hamiltonian but time-dependent collapse # operators in list string format res = _mesolve_list_str_td([H], rho0, tlist, c_ops, e_ops, args, options, progress_bar) elif n_func > 0: # constant hamiltonian but time-dependent collapse # operators in list function format res = _mesolve_list_func_td([H], rho0, tlist, c_ops, e_ops, args, options, progress_bar) elif isinstance( H, (types.FunctionType, types.BuiltinFunctionType, partial)): # old style time-dependence: must have constant collapse operators if n_str > 0: # or n_func > 0: raise TypeError("Incorrect format: function-format " + "Hamiltonian cannot be mixed with " + "time-dependent collapse operators.") else: res = _mesolve_func_td(H, rho0, tlist, c_ops, e_ops, args, options, progress_bar) elif isinstance(H, list): # determine if we are dealing with list of [Qobj, string] or # [Qobj, function] style time-dependencies (for pure python and # cython, respectively) if n_func > 0: res = _mesolve_list_func_td(H, rho0, tlist, c_ops, e_ops, args, options, progress_bar) else: res = _mesolve_list_str_td(H, rho0, tlist, c_ops, e_ops, args, options, progress_bar) else: raise TypeError("Incorrect specification of Hamiltonian " + "or collapse operators.") else: # # no collapse operators: unitary dynamics # if n_func > 0: res = _sesolve_list_func_td(H, rho0, tlist, e_ops, args, options, progress_bar) elif n_str > 0: res = _sesolve_list_str_td(H, rho0, tlist, e_ops, args, options, progress_bar) elif isinstance( H, (types.FunctionType, types.BuiltinFunctionType, partial)): res = _sesolve_func_td(H, rho0, tlist, e_ops, args, options, progress_bar) else: res = _sesolve_const(H, rho0, tlist, e_ops, args, options, progress_bar) if e_ops_dict: res.expect = { e: res.expect[n] for n, e in enumerate(e_ops_dict.keys()) } return res