bosonic_qiskit.wigner

module documentation

(source)

Undocumented

Function	`plot`	Contour plot the given data array
Function	`plot_wigner`	Produce a Matplotlib figure for the Wigner function on the given state vector.
Function	`plot_wigner_projection`	Plot the projection onto 0, 1, +, - for the given circuit.
Function	`plot_wigner_snapshot`	Undocumented
Function	`simulate_wigner`	Simulate the circuit, optionally partial trace the results, and calculate the Wigner function
Function	`simulate_wigner_multiple_statevectors`	Simulate the circuit, optionally partial trace the results, and calculate the Wigner function on each statevector starting with the given label.
Function	`wigner`	Calculate the Wigner function on the given state vector.
Function	`wigner_mle`	Find the maximum likelihood estimation for the given state vectors and calculate the Wigner function on the result.
Type Alias	`WignerMethods`	Undocumented
Type Alias	`WignerResult`	Undocumented
Function	`_add_contourf`	Add a matplotlib contourf plot with color levels based on min/max values in z.
Function	`_wigner`	Undocumented

def plot(data, axes_min: int = -6, axes_max: int = 6, axes_steps: int = 200, file: str | Path | None = None, num_colors: int = 100, draw_grid: bool = False, dpi: int = 100): (source) ¶

Contour plot the given data array

def plot_wigner(circuit: CVCircuit, state_vector: Statevector, trace: bool = True, file: str | Path | None = None, axes_min: int = -6, axes_max: int = 6, axes_steps: int = 200, num_colors: int = 100, draw_grid: bool = False, dpi: int = 100, **wigner_kwargs): (source) ¶

Produce a Matplotlib figure for the Wigner function on the given state vector.

Optionally perform partial trace.

Parameters
circuit:`CVCircuit`	circuit with results to trace (to find Qubit index)
state_vector:`Statevector`	simulation results to trace over and plot
trace:`bool, optional`	True if qubits should be traced. Defaults to True.
file:`str, optional`	File path to save plot. If none, return plot. Defaults to None.
axes_min:`int, optional`	Minimum axes plot value. Defaults to -6.
axes_max:`int, optional`	Maximum axes plot value. Defaults to 6.
axes_steps:`int, optional`	Steps between axes ticks. Defaults to 200.
num_colors:`int, optional`	Number of color gradients in legend. Defaults to 100.
draw_grid:`bool, optional`	True if grid lines should be drawn on plot. Defaults to False.
dpi:`int`	Undocumented
**wigner_kwargs	Undocumented

def plot_wigner_projection(circuit: CVCircuit, qubit: Qubit, file: str | Path | None = None, draw_grid: bool = False, **wigner_kwargs): (source) ¶

Plot the projection onto 0, 1, +, - for the given circuit.

This is limited to CVCircuit with only one qubit, also provided as a parameter.

Parameters
circuit:`CVCircuit`	circuit to simulate and plot
qubit:`Qubit`	qubit to measure
file:`str, optional`	File path to save file, if None return plot. Defaults to None.
draw_grid:`bool, optional`	True if gridlines should be drawn on plots. Defaults to False.
**wigner_kwargs	Undocumented

def plot_wigner_snapshot(circuit: CVCircuit, result: Result, folder: str | Path | None = None, trace: bool = True, axes_min: int = -6, axes_max: int = 6, axes_steps: int = 200, num_colors: int = 100, **wigner_kwargs): (source) ¶

Undocumented

def simulate_wigner(circuit: CVCircuit, xvec: np.ndarray, shots: int, noise_passes: NoisePassLike | None = None, conditional_state: str | None = None, trace: bool = False, method: WignerMethods = 'clenshaw', g: float = np.sqrt(2)) -> tuple[WignerResult, Statevector]: (source) ¶

Simulate the circuit, optionally partial trace the results, and calculate the Wigner function

Parameters
circuit:`CVCircuit`	The circuit to simulate
xvec:`np.ndarray`	The grid points to evaluate on. This will also be used to define the grid in the y direction
shots:`int`	The number of shots to execute
noise_passes:`NoisePassLike \| None`	Optional noise passes to apply to the simulation
conditional_state:`str \| None`	An optional state to select
trace:`bool`	Whether or not to trace out all qubits, leaving the qumodes
method:`WignerMethods`	The method used to calculate the wigner function.
g:`float`	Undocumented

Returns
`tuple[WignerResult, Statevector]`	A tuple containing the wigner function and the statevector

def simulate_wigner_multiple_statevectors(circuit: CVCircuit, xvec: ArrayLike, shots: int, statevector_label: str, num_statevectors: int, noise_passes: NoisePassLike | None = None, trace: bool = False, g: float = np.sqrt(2), method: WignerMethods = 'clenshaw') -> list[WignerResult]: (source) ¶

Simulate the circuit, optionally partial trace the results, and calculate the Wigner function on each statevector starting with the given label.

def wigner(state: ArrayLike | Statevector | DensityMatrix, axes_min: int = -6, axes_max: int = 6, axes_steps: int = 200, g: float = np.sqrt(2), method: WignerMethods = 'clenshaw'): (source) ¶

Calculate the Wigner function on the given state vector.

Parameters
state:`array-like`	state vector to calculate Wigner function
axes_min:`int, optional`	Minimum axes plot value. Defaults to -6.
axes_max:`int, optional`	Maximum axes plot value. Defaults to 6.
axes_steps:`int, optional`	Steps between axes ticks. Defaults to 200.
g:`float`	Undocumented
method:`WignerMethods`	Undocumented
cutoff:`int`	cutoff used during simulation
hbar:`int, optional`	hbar value to use in Wigner function calculation. Defaults to 2.

Returns
`array-like`	Results of Wigner function calculation

def wigner_mle(states: Sequence[Statevector], axes_min: int = -6, axes_max: int = 6, axes_steps: int = 200, g: float = np.sqrt(2), method: WignerMethods = 'clenshaw'): (source) ¶

Find the maximum likelihood estimation for the given state vectors and calculate the Wigner function on the result.

Parameters
states:`array-like of array-like`	state vectors to calculate MLE and Wigner function
axes_min:`int, optional`	Minimum axes plot value. Defaults to -6.
axes_max:`int, optional`	Maximum axes plot value. Defaults to 6.
axes_steps:`int, optional`	Steps between axes ticks. Defaults to 200.
g:`float`	Undocumented
method:`WignerMethods`	Undocumented
cutoff:`int`	cutoff used during simulation
hbar:`int, optional`	hbar value to use in Wigner function calculation. Defaults to 2.

Returns
`array-like`	Results of Wigner function calculation

WignerMethods = (source) ¶

Undocumented

Value

Literal['clenshaw', 'iterative', 'laguerre', 'fft']

WignerResult = (source) ¶

Undocumented

Value

tuple[np.ndarray, ...]

def _add_contourf(ax: Axes, fig: Figure, title: str, x: Sequence[int | float], y: Sequence[int | float], z: Sequence[int | float], draw_grid: bool = False): (source) ¶

Add a matplotlib contourf plot with color levels based on min/max values in z.

def _wigner(state: DensityMatrix, xvec: ArrayLike, yvec: ArrayLike | None = None, g: float = np.sqrt(2), method: WignerMethods = 'clenshaw') -> WignerResult: (source) ¶

Undocumented