Python Result.entropy方法代碼示例

# 需要導入模塊: from qutip.solver import Result [as 別名]
# 或者: from qutip.solver.Result import entropy [as 別名]
def _gather(sols):
    # gather list of Result objects, sols, into one.
    sol = Result()
    # sol = sols[0]
    ntraj = sum([a.ntraj for a in sols])
    sol.col_times = np.zeros((ntraj), dtype=np.ndarray)
    sol.col_which = np.zeros((ntraj), dtype=np.ndarray)
    sol.col_times[0:sols[0].ntraj] = sols[0].col_times
    sol.col_which[0:sols[0].ntraj] = sols[0].col_which
    sol.states = np.array(sols[0].states)
    sol.expect = np.array(sols[0].expect)
    if (hasattr(sols[0], 'entropy')):
        sol.entropy = np.array(sols[0].entropy)
    sofar = 0
    for j in range(1, len(sols)):
        sofar = sofar + sols[j - 1].ntraj
        sol.col_times[sofar:sofar + sols[j].ntraj] = (
            sols[j].col_times)
        sol.col_which[sofar:sofar + sols[j].ntraj] = (
            sols[j].col_which)
        if (config.e_num == 0):
            if (config.options.average_states):
                # collect states, averaged over trajectories
                sol.states += np.array(sols[j].states)
            else:
                # collect states, all trajectories
                sol.states = np.vstack((sol.states,
                                        np.array(sols[j].states)))
        else:
            if (config.options.average_expect):
                # collect expectation values, averaged
                for i in range(config.e_num):
                    sol.expect[i] += np.array(sols[j].expect[i])
            else:
                # collect expectation values, all trajectories
                sol.expect = np.vstack((sol.expect,
                                        np.array(sols[j].expect)))
        if (hasattr(sols[j], 'entropy')):
            if (config.options.average_states or
                    config.options.average_expect):
                # collect entropy values, averaged
                sol.entropy += np.array(sols[j].entropy)
            else:
                # collect entropy values, all trajectories
                sol.entropy = np.vstack((sol.entropy,
                                         np.array(sols[j].entropy)))
    if (config.options.average_states or config.options.average_expect):
        if (config.e_num == 0):
            sol.states = sol.states / len(sols)
        else:
            sol.expect = list(sol.expect / len(sols))
            inds = np.where(config.e_ops_isherm)[0]
            for jj in inds:
                sol.expect[jj] = np.real(sol.expect[jj])
        if (hasattr(sols[0], 'entropy')):
            sol.entropy = sol.entropy / len(sols)

    # convert sol.expect array to list and fix dtypes of arrays
    if (not config.options.average_expect) and config.e_num != 0:
        temp = [list(sol.expect[ii]) for ii in range(ntraj)]
        for ii in range(ntraj):
            for jj in np.where(config.e_ops_isherm)[0]:
                temp[ii][jj] = np.real(temp[ii][jj])
        sol.expect = temp
    # convert to list/array to be consistent with qutip mcsolve
    sol.states = list(sol.states)
    return sol

# 需要導入模塊: from qutip.solver import Result [as 別名]
# 或者: from qutip.solver.Result import entropy [as 別名]
def mcsolve_f90(H, psi0, tlist, c_ops, e_ops, ntraj=None,
                options=Options(), sparse_dms=True, serial=False,
                ptrace_sel=[], calc_entropy=False):
    """
    Monte-Carlo wave function solver with fortran 90 backend.
    Usage is identical to qutip.mcsolve, for problems without explicit
    time-dependence, and with some optional input:

    Parameters
    ----------
    H : qobj
        System Hamiltonian.
    psi0 : qobj
        Initial state vector
    tlist : array_like
        Times at which results are recorded.
    ntraj : int
        Number of trajectories to run.
    c_ops : array_like
        ``list`` or ``array`` of collapse operators.
    e_ops : array_like
        ``list`` or ``array`` of operators for calculating expectation values.
    options : Options
        Instance of solver options.
    sparse_dms : boolean
        If averaged density matrices are returned, they will be stored as
        sparse (Compressed Row Format) matrices during computation if
        sparse_dms = True (default), and dense matrices otherwise. Dense
        matrices might be preferable for smaller systems.
    serial : boolean
        If True (default is False) the solver will not make use of the
        multiprocessing module, and simply run in serial.
    ptrace_sel: list
        This optional argument specifies a list of components to keep when
        returning a partially traced density matrix. This can be convenient for
        large systems where memory becomes a problem, but you are only
        interested in parts of the density matrix.
    calc_entropy : boolean
        If ptrace_sel is specified, calc_entropy=True will have the solver
        return the averaged entropy over trajectories in results.entropy. This
        can be interpreted as a measure of entanglement. See Phys. Rev. Lett.
        93, 120408 (2004), Phys. Rev. A 86, 022310 (2012).

    Returns
    -------
    results : Result
        Object storing all results from simulation.

    """
    if ntraj is None:
        ntraj = options.ntraj

    if psi0.type != 'ket':
        raise Exception("Initial state must be a state vector.")
    config.options = options
    # 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
    # set initial value data
    if options.tidy:
        config.psi0 = psi0.tidyup(options.atol).full()
    else:
        config.psi0 = psi0.full()
    config.psi0_dims = psi0.dims
    config.psi0_shape = psi0.shape
    # set general items
    config.tlist = tlist
    if isinstance(ntraj, (list, np.ndarray)):
        raise Exception("ntraj as list argument is not supported.")
    else:
        config.ntraj = ntraj
        # ntraj_list = [ntraj]
    # set norm finding constants
    config.norm_tol = options.norm_tol
    config.norm_steps = options.norm_steps

    if not options.rhs_reuse:
        config.soft_reset()
        # no time dependence
        config.tflag = 0
        # check for collapse operators
        if len(c_ops) > 0:
            config.cflag = 1
        else:
            config.cflag = 0
        # Configure data
        _mc_data_config(H, psi0, [], c_ops, [], [], e_ops, options, config)

    # Load Monte Carlo class
    mc = _MC_class()
    # Set solver type
    if (options.method == 'adams'):
        mc.mf = 10
    elif (options.method == 'bdf'):
        mc.mf = 22
    else:
        if debug:
            print('Unrecognized method for ode solver, using "adams".')
        mc.mf = 10
#.........這裏部分代碼省略.........

# 需要導入模塊: from qutip.solver import Result [as 別名]
# 或者: from qutip.solver.Result import entropy [as 別名]
    def evolve_serial(self, args):

        if debug:
            print(inspect.stack()[0][3] + ":" + str(os.getpid()))

        # run ntraj trajectories for one process via fortran
        # get args
        queue, ntraj, instanceno, rngseed = args
        # initialize the problem in fortran
        _init_tlist()
        _init_psi0()
        if (self.ptrace_sel != []):
            _init_ptrace_stuff(self.ptrace_sel)
        _init_hamilt()
        if (config.c_num != 0):
            _init_c_ops()
        if (config.e_num != 0):
            _init_e_ops()
        # set options
        qtf90.qutraj_run.n_c_ops = config.c_num
        qtf90.qutraj_run.n_e_ops = config.e_num
        qtf90.qutraj_run.ntraj = ntraj
        qtf90.qutraj_run.unravel_type = self.unravel_type
        qtf90.qutraj_run.average_states = config.options.average_states
        qtf90.qutraj_run.average_expect = config.options.average_expect
        qtf90.qutraj_run.init_result(config.psi0_shape[0],
                                     config.options.atol,
                                     config.options.rtol, mf=self.mf,
                                     norm_steps=config.norm_steps,
                                     norm_tol=config.norm_tol)
        # set optional arguments
        qtf90.qutraj_run.order = config.options.order
        qtf90.qutraj_run.nsteps = config.options.nsteps
        qtf90.qutraj_run.first_step = config.options.first_step
        qtf90.qutraj_run.min_step = config.options.min_step
        qtf90.qutraj_run.max_step = config.options.max_step
        qtf90.qutraj_run.norm_steps = config.options.norm_steps
        qtf90.qutraj_run.norm_tol = config.options.norm_tol
        # use sparse density matrices during computation?
        qtf90.qutraj_run.rho_return_sparse = self.sparse_dms
        # calculate entropy of reduced density matrice?
        qtf90.qutraj_run.calc_entropy = self.calc_entropy
        # run
        show_progress = 1 if debug else 0
        qtf90.qutraj_run.evolve(instanceno, rngseed, show_progress)

        # construct Result instance
        sol = Result()
        sol.ntraj = ntraj
        # sol.col_times = qtf90.qutraj_run.col_times
        # sol.col_which = qtf90.qutraj_run.col_which-1
        sol.col_times, sol.col_which = self.get_collapses(ntraj)
        if (config.e_num == 0):
            sol.states = self.get_states(len(config.tlist), ntraj)
        else:
            sol.expect = self.get_expect(len(config.tlist), ntraj)
        if (self.calc_entropy):
            sol.entropy = self.get_entropy(len(config.tlist))

        if (not self.serial_run):
            # put to queue
            queue.put(sol)
            queue.join()

        # deallocate stuff
        # finalize()
        return sol