filter_functions.plotting module

This module provides various plotting functions.

Functions

plot_bloch_vector_evolution(): Plot the evolution of the Bloch vector on a QuTiP-generated Bloch sphere
plot_filter_function(): Plot the filter function of a given PulseSequence
plot_infidelity_convergence(): Helper function called by infidelity() to plot the convergence of the infidelity
plot_pulse_correlation_filter_function(): Plot the pulse correlation filter function of a given PulseSequence
plot_pulse_train(): Plot the pulse train of a given PulseSequence
plot_cumulant_function(): Plot the cumulant function of a PulseSequence for a given spectrum as an image.

Plot the evolution of the Bloch vector under the given pulse sequence.

Parameters:

pulse: PulseSequence: The PulseSequence instance whose control Hamiltonian determines the time evolution of the Bloch vector.
psi0: Qobj or array_like, optional: The initial state before the pulse is applied. Defaults to \(|0\rangle\).
b: qutip.Bloch, optional: If given, the QuTiP Bloch instance on which to plot the time evolution.
n_samples: int, optional: The number of time points to be sampled.
cmap: matplotlib colormap, optional: The colormap for the trajectory. Requires matplotlib >= 3.3.0.
add_cbar: bool, optional: Add a colorbar encoding the time evolution to the figure. Default is false.
show: bool, optional: Whether to show the sphere (by calling b.make_sphere()).
return_Bloch: bool, optional: Whether to return the qutip.bloch.Bloch instance
cbar_kwargs: dict, optional: A dictionary with keyword arguments to be fed into the colorbar constructor (if add_cbar == True).
**bloch_kwargs: optional: Keyword arguments to be fed into the qutip.Bloch constructor (if b not given).

Returns:

b: qutip.Bloch: The qutip.Bloch instance

Raises:

ValueError: If the pulse is for more than one qubit

See also

qutip.bloch.Bloch: Qutip’s Bloch sphere implementation.

Plot the cumulant function for a given noise spectrum as an image.

The cumulant function generates the error transfer matrix \(\tilde{\mathcal{U}}\) exactly for Gaussian noise and to second order for non-Gaussian noise.

The function may be called with either a PulseSequence, a spectrum, and a list of frequencies in which case the cumulant function is calculated for those parameters, or with a precomputed cumulant function.

As of now, only auto-correlated spectra are implemented.

Parameters:

pulse: PulseSequence: The pulse sequence.
spectrum: ndarray: The two-sided noise spectrum.
omega: array_like: The frequencies for which to evaluate the error transfer matrix.
cumulant_function: ndarray, shape (n_nops, d**2, d**2): A precomputed cumulant function. If given, pulse, spectrum, omega are not required.
n_oper_identifiers: array_like, optional: The identifiers of the noise operators for which the cumulant function should be plotted. All identifiers can be accessed via pulse.n_oper_identifiers. Defaults to all.
second_order: bool, optional: Also take into account the frequency shifts \(\Delta\) that correspond to second order Magnus expansion and constitute unitary terms. Default False.
colorscale: str, optional: The scale of the color code (‘linear’ or ‘log’ (default))
linthresh: float, optional: The threshold below which the colorscale will be linear (only for ‘log’) colorscale
basis_labels: array_like (str), optional: Custom labels for the elements of the cumulant function (the basis elements). Grabbed from the basis by default.
basis_labelsize: int, optional: The size in points for the basis labels.
cbar_label: str, optional: The label for the colorbar. Default: ‘Cumulant Function’.
cbar_labelsize: int, optional: The size in points for the colorbar label.
fig: matplotlib figure, optional: A matplotlib figure instance to plot in
grid: matplotlib ImageGrid, optional: An ImageGrid instance to use for plotting.
cmap: matplotlib colormap, optional: The colormap for the matrix plot.
grid_kw: dict, optional: Dictionary with keyword arguments passed to the ImageGrid constructor.
cbar_kw: dict, optional: Dictionary with keyword arguments passed to the colorbar constructor.
imshow_kw: dict, optional: Dictionary with keyword arguments passed to imshow.
figure_kw: optional: Keyword argument dictionaries that are fed into the matplotlib.pyplot.figure() function if no fig instance is specified.

Returns:

fig: matplotlib figure: The matplotlib figure instance used for plotting.
grid: matplotlib ImageGrid: The ImageGrid instance used for plotting.

plot_filter_function(pulse: PulseSequence, omega: Sequence[float] | None = None, n_oper_identifiers: Sequence[int] | None = None, fig: Figure | None = None, axes: Axes | None = None, xscale: str = 'log', yscale: str = 'linear', omega_in_units_of_tau: bool = False, cycler: Cycler | None = None, plot_kw: dict = {}, subplot_kw: dict | None = None, gridspec_kw: dict | None = None, **figure_kw) → Tuple[Figure, Axes, Legend][source]

Plot the fidelity filter function(s) of the given PulseSequence for positive frequencies. As of now only the diagonal elements of \(F_{\alpha\beta}\) are implemented, i.e. the filter functions corresponding to uncorrelated noise sources.

Parameters:

pulse: PulseSequence: The pulse sequence whose filter function to plot.
omega: array_like, optional: The frequencies at which to evaluate the filter function. If not given, the pulse sequence’s omega attribute is used (if set) or sensible values are chosen automatically (if None)
n_oper_identifiers: array_like, optional: The identifiers of the noise operators for which the filter function should be plotted. All identifiers can be accessed via pulse.n_oper_identifiers. Defaults to all.
fig: matplotlib figure, optional: A matplotlib figure instance to plot in
axes: matplotlib axes, optional: A matplotlib axes instance to use for plotting.
xscale: str, optional: x-axis scaling. One of (‘linear’, ‘log’).
yscale: str, optional: y-axis scaling. One of (‘linear’, ‘log’).
omega_in_units_of_tau: bool, optional: Plot \(\omega\tau\) or just \(\omega\) on x-axis.
cycler: cycler.Cycler, optional: A Cycler instance used to set the style cycle if multiple lines are to be drawn
plot_kw: dict, optional: Dictionary with keyword arguments passed to the plot function
subplot_kw: dict, optional: Dictionary with keyword arguments passed to the subplots constructor
gridspec_kw: dict, optional: Dictionary with keyword arguments passed to the gridspec constructor
figure_kw: optional: Keyword argument dictionaries that are fed into the matplotlib.pyplot.subplots() function if no fig instance is specified.

Returns:

fig: matplotlib figure: The matplotlib figure instance used for plotting.
axes: matplotlib axes: The matplotlib axes instance used for plotting.
legend: matplotlib legend: The matplotlib legend instance in the plot.

Raises:

ValueError: If an invalid number of n_oper_labels were given

plot_infidelity_convergence(n_samples: Sequence[int], infids: Sequence[float], axes: Axes | None = None) → Tuple[Figure, Axes][source]

Plot the convergence of the infidelity integral. The function arguments are those returned by infidelity() with the test_convergence flag set to True.

Parameters:

n_samples: array_like: Array with the number of samples at which the integral was evaluated
infids: array_like, shape (n_samples, [n_oper_inds, optional]): Array with the calculated infidelities for each noise operator on the second axis or the second axis already traced out.
axes: sequence of two matplotlib axes, optional: Two axes that the result is plotted in.

Returns:

fig: matplotlib figure: The matplotlib figure instance used for plotting.
axes: matplotlib axes: The matplotlib axes instances used for plotting.

plot_pulse_correlation_filter_function(pulse: PulseSequence, n_oper_identifiers: Sequence[int] | None = None, fig: Figure | None = None, xscale: str = 'log', yscale: str = 'linear', omega_in_units_of_tau: bool = True, cycler: Cycler | None = None, plot_kw: dict = {}, subplot_kw: dict | None = None, gridspec_kw: dict | None = None, **figure_kw) → Tuple[Figure, Axes, Legend][source]

Plot the fidelity pulse correlation filter functions of the given PulseSequence if they were computed during concatenation for positive frequencies.

Returns a figure with n by n subplots where n is the number of pulses that were concatenated. As of now only the diagonal elements of \(F_{\alpha\beta}\) are implemented, i.e. the filter functions corresponding to uncorrelated noise sources.

Parameters:

pulse: PulseSequence: The pulse sequence whose filter function to plot.
n_oper_identifiers: array_like, optional: The identifiers of the noise operators for which the filter function should be plotted. All identifiers can be accessed via pulse.n_oper_identifiers. Defaults to all.
fig: matplotlib figure, optional: A matplotlib figure instance to plot in
xscale: str, optional: x-axis scaling. One of (‘linear’, ‘log’).
yscale: str, optional: y-axis scaling. One of (‘linear’, ‘log’).
omega_in_units_of_tau: bool, optional: Plot \(\omega\tau\) or just \(\omega\) on x-axis.
cycler: cycler.Cycler, optional: A Cycler instance used to set the style cycle if multiple lines are to be drawn in one subplot. Used for all subplots.
plot_kw: dict, optional: Dictionary with keyword arguments passed to the plot function
subplot_kw: dict, optional: Dictionary with keyword arguments passed to the subplots constructor
gridspec_kw: dict, optional: Dictionary with keyword arguments passed to the gridspec constructor
figure_kw: optional: Keyword argument dictionaries that are fed into the matplotlib.pyplot.subplots() function if no fig instance if specified.

Returns:

fig: matplotlib figure: The matplotlib figure instance used for plotting.
axes: matplotlib axes: The matplotlib axes instances used for plotting.
legend: matplotlib legend: The matplotlib legend instance in the plot.

Raises:

CalculationError: If the pulse correlation filter function was not computed during concatenation.

plot_pulse_train(pulse: PulseSequence, c_oper_identifiers: Sequence[int] | None = None, fig: Figure | None = None, axes: Axes | None = None, cycler: Cycler | None = None, plot_kw: dict | None = {}, subplot_kw: dict | None = None, gridspec_kw: dict | None = None, **figure_kw) → Tuple[Figure, Axes, Legend][source]

Plot the pulsetrain of the PulseSequence pulse.

Parameters:

pulse: PulseSequence: The pulse sequence whose pulse train to plot.
c_oper_identifiers: array_like, optional: The identifiers of the control operators for which the pulse train should be plotted. All identifiers can be accessed via pulse.c_oper_identifiers. Defaults to all.
fig: matplotlib figure, optional: A matplotlib figure instance to plot in
axes: matplotlib axes, optional: A matplotlib axes instance to use for plotting.
cycler: cycler.Cycler, optional: A Cycler instance used to set the style cycle if multiple lines are to be drawn
plot_kw: dict, optional: Dictionary with keyword arguments passed to the plot function
subplot_kw: dict, optional: Dictionary with keyword arguments passed to the subplots constructor
gridspec_kw: dict, optional: Dictionary with keyword arguments passed to the gridspec constructor
figure_kw: optional: Keyword argument dictionaries that are fed into the matplotlib.pyplot.subplots() function if no fig instance is specified.

Returns:

fig: matplotlib figure: The matplotlib figure instance used for plotting.
axes: matplotlib axes: The matplotlib axes instance used for plotting.
legend: matplotlib legend: The matplotlib legend instance in the plot.

Raises:

ValueError: If an invalid number of c_oper_labels were given