def plot_bloch_vector(bloch, title="", ax=None, figsize=None, coord_type="cartesian"):
"""Plot the Bloch sphere.
Plot a sphere, axes, the Bloch vector, and its projections onto each axis.
Args:
bloch (list[double]): array of three elements where [<x>, <y>, <z>] (Cartesian)
or [<r>, <theta>, <phi>] (spherical in radians)
<theta> is inclination angle from +z direction
<phi> is azimuth from +x direction
title (str): a string that represents the plot title
ax (matplotlib.axes.Axes): An Axes to use for rendering the bloch
sphere
figsize (tuple): Figure size in inches. Has no effect is passing ``ax``.
coord_type (str): a string that specifies coordinate type for bloch
(Cartesian or spherical), default is Cartesian
Returns:
Figure: A matplotlib figure instance if ``ax = None``.
Raises:
MissingOptionalLibraryError: Requires matplotlib.
Example:
.. jupyter-execute::
from qiskit.visualization import plot_bloch_vector
%matplotlib inline
plot_bloch_vector([0,1,0], title="New Bloch Sphere")
"""
if not HAS_MATPLOTLIB:
raise MissingOptionalLibraryError(
libname="Matplotlib",
name="plot_bloch_vector",
pip_install="pip install matplotlib",
)
from qiskit.visualization.bloch import Bloch
if figsize is None:
figsize = (5, 5)
B = Bloch(axes=ax)
if coord_type == "spherical":
r, theta, phi = bloch[0], bloch[1], bloch[2]
bloch[0] = r * np.sin(theta) * np.cos(phi)
bloch[1] = r * np.sin(theta) * np.sin(phi)
bloch[2] = r * np.cos(theta)
B.add_vectors(bloch)
B.render(title=title)
if ax is None:
fig = B.fig
fig.set_size_inches(figsize[0], figsize[1])
matplotlib_close_if_inline(fig)
return fig
return None
def get_image(self, interactive: bool = False) -> matplotlib.pyplot.Figure:
"""Get image data to return.
Args:
interactive: When set `True` show the circuit in a new window.
This depends on the matplotlib backend being used supporting this.
Returns:
Matplotlib figure data.
"""
matplotlib_close_if_inline(self.figure)
if self.figure and interactive:
self.figure.show()
return self.figure
def plot_state_qsphere(
state,
figsize=None,
ax=None,
show_state_labels=True,
show_state_phases=False,
use_degrees=False,
*,
rho=None,
filename=None,
):
"""Plot the qsphere representation of a quantum state.
Here, the size of the points is proportional to the probability
of the corresponding term in the state and the color represents
the phase.
Args:
state (Statevector or DensityMatrix or ndarray): an N-qubit quantum state.
figsize (tuple): Figure size in inches.
ax (matplotlib.axes.Axes): An optional Axes object to be used for
the visualization output. If none is specified a new matplotlib
Figure will be created and used. Additionally, if specified there
will be no returned Figure since it is redundant.
show_state_labels (bool): An optional boolean indicating whether to
show labels for each basis state.
show_state_phases (bool): An optional boolean indicating whether to
show the phase for each basis state.
use_degrees (bool): An optional boolean indicating whether to use
radians or degrees for the phase values in the plot.
Returns:
Figure: A matplotlib figure instance if the ``ax`` kwarg is not set
Raises:
MissingOptionalLibraryError: Requires matplotlib.
VisualizationError: if input is not a valid N-qubit state.
QiskitError: Input statevector does not have valid dimensions.
Example:
.. jupyter-execute::
from qiskit import QuantumCircuit
from qiskit.quantum_info import Statevector
from qiskit.visualization import plot_state_qsphere
%matplotlib inline
qc = QuantumCircuit(2)
qc.h(0)
qc.cx(0, 1)
state = Statevector.from_instruction(qc)
plot_state_qsphere(state)
"""
if not HAS_MATPLOTLIB:
raise MissingOptionalLibraryError(
libname="Matplotlib",
name="plot_state_qsphere",
pip_install="pip install matplotlib",
)
import matplotlib.gridspec as gridspec
from matplotlib import pyplot as plt
from matplotlib.patches import Circle
from qiskit.visualization.bloch import Arrow3D
try:
import seaborn as sns
except ImportError as ex:
raise MissingOptionalLibraryError(
libname="seaborn",
name="plot_state_qsphere",
pip_install="pip install seaborn",
) from ex
rho = DensityMatrix(state)
num = rho.num_qubits
if num is None:
raise VisualizationError("Input is not a multi-qubit quantum state.")
# get the eigenvectors and eigenvalues
eigvals, eigvecs = linalg.eigh(rho.data)
if figsize is None:
figsize = (7, 7)
if ax is None:
return_fig = True
fig = plt.figure(figsize=figsize)
else:
return_fig = False
fig = ax.get_figure()
gs = gridspec.GridSpec(nrows=3, ncols=3)
ax = fig.add_subplot(gs[0:3, 0:3], projection="3d")
ax.axes.set_xlim3d(-1.0, 1.0)
ax.axes.set_ylim3d(-1.0, 1.0)
ax.axes.set_zlim3d(-1.0, 1.0)
ax.axes.grid(False)
ax.view_init(elev=5, azim=275)
# Force aspect ratio
# MPL 3.2 or previous do not have set_box_aspect
if hasattr(ax.axes, "set_box_aspect"):
ax.axes.set_box_aspect((1, 1, 1))
# start the plotting
# Plot semi-transparent sphere
u = np.linspace(0, 2 * np.pi, 25)
v = np.linspace(0, np.pi, 25)
x = np.outer(np.cos(u), np.sin(v))
y = np.outer(np.sin(u), np.sin(v))
z = np.outer(np.ones(np.size(u)), np.cos(v))
ax.plot_surface(
x, y, z, rstride=1, cstride=1, color=plt.rcParams["grid.color"], alpha=0.2, linewidth=0
)
# Get rid of the panes
ax.w_xaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))
ax.w_yaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))
ax.w_zaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))
# Get rid of the spines
ax.w_xaxis.line.set_color((1.0, 1.0, 1.0, 0.0))
ax.w_yaxis.line.set_color((1.0, 1.0, 1.0, 0.0))
ax.w_zaxis.line.set_color((1.0, 1.0, 1.0, 0.0))
# Get rid of the ticks
ax.set_xticks([])
ax.set_yticks([])
ax.set_zticks([])
# traversing the eigvals/vecs backward as sorted low->high
for idx in range(eigvals.shape[0] - 1, -1, -1):
if eigvals[idx] > 0.001:
# get the max eigenvalue
state = eigvecs[:, idx]
loc = np.absolute(state).argmax()
# remove the global phase from max element
angles = (np.angle(state[loc]) + 2 * np.pi) % (2 * np.pi)
angleset = np.exp(-1j * angles)
state = angleset * state
d = num
for i in range(2 ** num):
# get x,y,z points
element = bin(i)[2:].zfill(num)
weight = element.count("1")
zvalue = -2 * weight / d + 1
number_of_divisions = n_choose_k(d, weight)
weight_order = bit_string_index(element)
angle = (float(weight) / d) * (np.pi * 2) + (
weight_order * 2 * (np.pi / number_of_divisions)
)
if (weight > d / 2) or (
(weight == d / 2) and (weight_order >= number_of_divisions / 2)
):
angle = np.pi - angle - (2 * np.pi / number_of_divisions)
xvalue = np.sqrt(1 - zvalue ** 2) * np.cos(angle)
yvalue = np.sqrt(1 - zvalue ** 2) * np.sin(angle)
# get prob and angle - prob will be shade and angle color
prob = np.real(np.dot(state[i], state[i].conj()))
prob = min(prob, 1) # See https://github.com/Qiskit/qiskit-terra/issues/4666
colorstate = phase_to_rgb(state[i])
alfa = 1
if yvalue >= 0.1:
alfa = 1.0 - yvalue
if not np.isclose(prob, 0) and show_state_labels:
rprime = 1.3
angle_theta = np.arctan2(np.sqrt(1 - zvalue ** 2), zvalue)
xvalue_text = rprime * np.sin(angle_theta) * np.cos(angle)
yvalue_text = rprime * np.sin(angle_theta) * np.sin(angle)
zvalue_text = rprime * np.cos(angle_theta)
element_text = "$\\vert" + element + "\\rangle$"
if show_state_phases:
element_angle = (np.angle(state[i]) + (np.pi * 4)) % (np.pi * 2)
if use_degrees:
element_text += "\n$%.1f^\\circ$" % (element_angle * 180 / np.pi)
else:
element_angle = pi_check(element_angle, ndigits=3).replace("pi", "\\pi")
element_text += "\n$%s$" % (element_angle)
ax.text(
xvalue_text,
yvalue_text,
zvalue_text,
element_text,
ha="center",
va="center",
size=12,
)
ax.plot(
[xvalue],
[yvalue],
[zvalue],
markerfacecolor=colorstate,
markeredgecolor=colorstate,
marker="o",
markersize=np.sqrt(prob) * 30,
alpha=alfa,
)
a = Arrow3D(
[0, xvalue],
[0, yvalue],
[0, zvalue],
mutation_scale=20,
alpha=prob,
arrowstyle="-",
color=colorstate,
lw=2,
)
ax.add_artist(a)
# add weight lines
for weight in range(d + 1):
theta = np.linspace(-2 * np.pi, 2 * np.pi, 100)
z = -2 * weight / d + 1
r = np.sqrt(1 - z ** 2)
x = r * np.cos(theta)
y = r * np.sin(theta)
ax.plot(x, y, z, color=(0.5, 0.5, 0.5), lw=1, ls=":", alpha=0.5)
# add center point
ax.plot(
[0],
[0],
[0],
markerfacecolor=(0.5, 0.5, 0.5),
markeredgecolor=(0.5, 0.5, 0.5),
marker="o",
markersize=3,
alpha=1,
)
else:
break
n = 64
theta = np.ones(n)
colors = sns.hls_palette(n)
ax2 = fig.add_subplot(gs[2:, 2:])
ax2.pie(theta, colors=colors[5 * n // 8 :] + colors[: 5 * n // 8], radius=0.75)
ax2.add_artist(Circle((0, 0), 0.5, color="white", zorder=1))
offset = 0.95 # since radius of sphere is one.
if use_degrees:
labels = ["Phase\n(Deg)", "0", "90", "180 ", "270"]
else:
labels = ["Phase", "$0$", "$\\pi/2$", "$\\pi$", "$3\\pi/2$"]
ax2.text(0, 0, labels[0], horizontalalignment="center", verticalalignment="center", fontsize=14)
ax2.text(
offset, 0, labels[1], horizontalalignment="center", verticalalignment="center", fontsize=14
)
ax2.text(
0, offset, labels[2], horizontalalignment="center", verticalalignment="center", fontsize=14
)
ax2.text(
-offset, 0, labels[3], horizontalalignment="center", verticalalignment="center", fontsize=14
)
ax2.text(
0, -offset, labels[4], horizontalalignment="center", verticalalignment="center", fontsize=14
)
if return_fig:
matplotlib_close_if_inline(fig)
if filename is None:
return fig
else:
return fig.savefig(filename)
def plot_state_paulivec(
state, title="", figsize=None, color=None, ax=None, *, rho=None, filename=None
):
"""Plot the paulivec representation of a quantum state.
Plot a bargraph of the mixed state rho over the pauli matrices
Args:
state (Statevector or DensityMatrix or ndarray): an N-qubit quantum state.
title (str): a string that represents the plot title
figsize (tuple): Figure size in inches.
color (list or str): Color of the expectation value bars.
ax (matplotlib.axes.Axes): An optional Axes object to be used for
the visualization output. If none is specified a new matplotlib
Figure will be created and used. Additionally, if specified there
will be no returned Figure since it is redundant.
Returns:
matplotlib.Figure:
The matplotlib.Figure of the visualization if the
``ax`` kwarg is not set
Raises:
MissingOptionalLibraryError: Requires matplotlib.
VisualizationError: if input is not a valid N-qubit state.
Example:
.. jupyter-execute::
from qiskit import QuantumCircuit
from qiskit.quantum_info import Statevector
from qiskit.visualization import plot_state_paulivec
%matplotlib inline
qc = QuantumCircuit(2)
qc.h(0)
qc.cx(0, 1)
state = Statevector.from_instruction(qc)
plot_state_paulivec(state, color='midnightblue',
title="New PauliVec plot")
"""
if not HAS_MATPLOTLIB:
raise MissingOptionalLibraryError(
libname="Matplotlib",
name="plot_state_paulivec",
pip_install="pip install matplotlib",
)
from matplotlib import pyplot as plt
labels, values = _paulivec_data(state)
numelem = len(values)
if figsize is None:
figsize = (7, 5)
if color is None:
color = "#648fff"
ind = np.arange(numelem) # the x locations for the groups
width = 0.5 # the width of the bars
if ax is None:
return_fig = True
fig, ax = plt.subplots(figsize=figsize)
else:
return_fig = False
fig = ax.get_figure()
ax.grid(zorder=0, linewidth=1, linestyle="--")
ax.bar(ind, values, width, color=color, zorder=2)
ax.axhline(linewidth=1, color="k")
# add some text for labels, title, and axes ticks
ax.set_ylabel("Expectation value", fontsize=14)
ax.set_xticks(ind)
ax.set_yticks([-1, -0.5, 0, 0.5, 1])
ax.set_xticklabels(labels, fontsize=14, rotation=70)
ax.set_xlabel("Pauli", fontsize=14)
ax.set_ylim([-1, 1])
ax.set_facecolor("#eeeeee")
for tick in ax.xaxis.get_major_ticks() + ax.yaxis.get_major_ticks():
tick.label.set_fontsize(14)
ax.set_title(title, fontsize=16)
if return_fig:
matplotlib_close_if_inline(fig)
if filename is None:
return fig
else:
return fig.savefig(filename)
def plot_state_hinton(
state, title="", figsize=None, ax_real=None, ax_imag=None, *, rho=None, filename=None
):
"""Plot a hinton diagram for the density matrix of a quantum state.
Args:
state (Statevector or DensityMatrix or ndarray): An N-qubit quantum state.
title (str): a string that represents the plot title
figsize (tuple): Figure size in inches.
filename (str): file path to save image to.
ax_real (matplotlib.axes.Axes): An optional Axes object to be used for
the visualization output. If none is specified a new matplotlib
Figure will be created and used. If this is specified without an
ax_imag only the real component plot will be generated.
Additionally, if specified there will be no returned Figure since
it is redundant.
ax_imag (matplotlib.axes.Axes): An optional Axes object to be used for
the visualization output. If none is specified a new matplotlib
Figure will be created and used. If this is specified without an
ax_imag only the real component plot will be generated.
Additionally, if specified there will be no returned Figure since
it is redundant.
Returns:
matplotlib.Figure:
The matplotlib.Figure of the visualization if
neither ax_real or ax_imag is set.
Raises:
MissingOptionalLibraryError: Requires matplotlib.
VisualizationError: if input is not a valid N-qubit state.
Example:
.. jupyter-execute::
from qiskit import QuantumCircuit
from qiskit.quantum_info import DensityMatrix
from qiskit.visualization import plot_state_hinton
%matplotlib inline
qc = QuantumCircuit(2)
qc.h(0)
qc.cx(0, 1)
state = DensityMatrix.from_instruction(qc)
plot_state_hinton(state, title="New Hinton Plot")
"""
if not HAS_MATPLOTLIB:
raise MissingOptionalLibraryError(
libname="Matplotlib",
name="plot_state_hinton",
pip_install="pip install matplotlib",
)
from matplotlib import pyplot as plt
# Figure data
rho = DensityMatrix(state)
num = rho.num_qubits
if num is None:
raise VisualizationError("Input is not a multi-qubit quantum state.")
max_weight = 2 ** np.ceil(np.log(np.abs(rho.data).max()) / np.log(2))
datareal = np.real(rho.data)
dataimag = np.imag(rho.data)
if figsize is None:
figsize = (8, 5)
if not ax_real and not ax_imag:
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=figsize)
else:
if ax_real:
fig = ax_real.get_figure()
else:
fig = ax_imag.get_figure()
ax1 = ax_real
ax2 = ax_imag
column_names = [bin(i)[2:].zfill(num) for i in range(2 ** num)]
row_names = [bin(i)[2:].zfill(num) for i in range(2 ** num)]
ly, lx = datareal.shape
# Real
if ax1:
ax1.patch.set_facecolor("gray")
ax1.set_aspect("equal", "box")
ax1.xaxis.set_major_locator(plt.NullLocator())
ax1.yaxis.set_major_locator(plt.NullLocator())
for (x, y), w in np.ndenumerate(datareal):
color = "white" if w > 0 else "black"
size = np.sqrt(np.abs(w) / max_weight)
rect = plt.Rectangle(
[0.5 + x - size / 2, 0.5 + y - size / 2],
size,
size,
facecolor=color,
edgecolor=color,
)
ax1.add_patch(rect)
ax1.set_xticks(0.5 + np.arange(lx))
ax1.set_yticks(0.5 + np.arange(ly))
ax1.set_xlim([0, lx])
ax1.set_ylim([ly, 0])
ax1.set_yticklabels(row_names, fontsize=14)
ax1.set_xticklabels(column_names, fontsize=14, rotation=90)
ax1.invert_yaxis()
ax1.set_title("Re[$\\rho$]", fontsize=14)
# Imaginary
if ax2:
ax2.patch.set_facecolor("gray")
ax2.set_aspect("equal", "box")
ax2.xaxis.set_major_locator(plt.NullLocator())
ax2.yaxis.set_major_locator(plt.NullLocator())
for (x, y), w in np.ndenumerate(dataimag):
color = "white" if w > 0 else "black"
size = np.sqrt(np.abs(w) / max_weight)
rect = plt.Rectangle(
[0.5 + x - size / 2, 0.5 + y - size / 2],
size,
size,
facecolor=color,
edgecolor=color,
)
ax2.add_patch(rect)
ax2.set_xticks(0.5 + np.arange(lx))
ax2.set_yticks(0.5 + np.arange(ly))
ax1.set_xlim([0, lx])
ax1.set_ylim([ly, 0])
ax2.set_yticklabels(row_names, fontsize=14)
ax2.set_xticklabels(column_names, fontsize=14, rotation=90)
ax2.invert_yaxis()
ax2.set_title("Im[$\\rho$]", fontsize=14)
if title:
fig.suptitle(title, fontsize=16)
if ax_real is None and ax_imag is None:
matplotlib_close_if_inline(fig)
if filename is None:
return fig
else:
return fig.savefig(filename)
def plot_state_city(
state,
title="",
figsize=None,
color=None,
alpha=1,
ax_real=None,
ax_imag=None,
*,
rho=None,
filename=None,
):
"""Plot the cityscape of quantum state.
Plot two 3d bar graphs (two dimensional) of the real and imaginary
part of the density matrix rho.
Args:
state (Statevector or DensityMatrix or ndarray): an N-qubit quantum state.
title (str): a string that represents the plot title
figsize (tuple): Figure size in inches.
color (list): A list of len=2 giving colors for real and
imaginary components of matrix elements.
alpha (float): Transparency value for bars
ax_real (matplotlib.axes.Axes): An optional Axes object to be used for
the visualization output. If none is specified a new matplotlib
Figure will be created and used. If this is specified without an
ax_imag only the real component plot will be generated.
Additionally, if specified there will be no returned Figure since
it is redundant.
ax_imag (matplotlib.axes.Axes): An optional Axes object to be used for
the visualization output. If none is specified a new matplotlib
Figure will be created and used. If this is specified without an
ax_real only the imaginary component plot will be generated.
Additionally, if specified there will be no returned Figure since
it is redundant.
Returns:
matplotlib.Figure:
The matplotlib.Figure of the visualization if the
``ax_real`` and ``ax_imag`` kwargs are not set
Raises:
MissingOptionalLibraryError: Requires matplotlib.
ValueError: When 'color' is not a list of len=2.
VisualizationError: if input is not a valid N-qubit state.
Example:
.. jupyter-execute::
from qiskit import QuantumCircuit
from qiskit.quantum_info import DensityMatrix
from qiskit.visualization import plot_state_city
%matplotlib inline
qc = QuantumCircuit(2)
qc.h(0)
qc.cx(0, 1)
state = DensityMatrix.from_instruction(qc)
plot_state_city(state, color=['midnightblue', 'midnightblue'],
title="New State City")
"""
if not HAS_MATPLOTLIB:
raise MissingOptionalLibraryError(
libname="Matplotlib",
name="plot_state_city",
pip_install="pip install matplotlib",
)
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
rho = DensityMatrix(state)
num = rho.num_qubits
if num is None:
raise VisualizationError("Input is not a multi-qubit quantum state.")
# get the real and imag parts of rho
datareal = np.real(rho.data)
dataimag = np.imag(rho.data)
# get the labels
column_names = [bin(i)[2:].zfill(num) for i in range(2 ** num)]
row_names = [bin(i)[2:].zfill(num) for i in range(2 ** num)]
lx = len(datareal[0]) # Work out matrix dimensions
ly = len(datareal[:, 0])
xpos = np.arange(0, lx, 1) # Set up a mesh of positions
ypos = np.arange(0, ly, 1)
xpos, ypos = np.meshgrid(xpos + 0.25, ypos + 0.25)
xpos = xpos.flatten()
ypos = ypos.flatten()
zpos = np.zeros(lx * ly)
dx = 0.5 * np.ones_like(zpos) # width of bars
dy = dx.copy()
dzr = datareal.flatten()
dzi = dataimag.flatten()
if color is None:
color = ["#648fff", "#648fff"]
else:
if len(color) != 2:
raise ValueError("'color' must be a list of len=2.")
if color[0] is None:
color[0] = "#648fff"
if color[1] is None:
color[1] = "#648fff"
if ax_real is None and ax_imag is None:
# set default figure size
if figsize is None:
figsize = (15, 5)
fig = plt.figure(figsize=figsize)
ax1 = fig.add_subplot(1, 2, 1, projection="3d")
ax2 = fig.add_subplot(1, 2, 2, projection="3d")
elif ax_real is not None:
fig = ax_real.get_figure()
ax1 = ax_real
ax2 = ax_imag
else:
fig = ax_imag.get_figure()
ax1 = None
ax2 = ax_imag
max_dzr = max(dzr)
min_dzr = min(dzr)
min_dzi = np.min(dzi)
max_dzi = np.max(dzi)
if ax1 is not None:
fc1 = generate_facecolors(xpos, ypos, zpos, dx, dy, dzr, color[0])
for idx, cur_zpos in enumerate(zpos):
if dzr[idx] > 0:
zorder = 2
else:
zorder = 0
b1 = ax1.bar3d(
xpos[idx],
ypos[idx],
cur_zpos,
dx[idx],
dy[idx],
dzr[idx],
alpha=alpha,
zorder=zorder,
)
b1.set_facecolors(fc1[6 * idx : 6 * idx + 6])
xlim, ylim = ax1.get_xlim(), ax1.get_ylim()
x = [xlim[0], xlim[1], xlim[1], xlim[0]]
y = [ylim[0], ylim[0], ylim[1], ylim[1]]
z = [0, 0, 0, 0]
verts = [list(zip(x, y, z))]
pc1 = Poly3DCollection(verts, alpha=0.15, facecolor="k", linewidths=1, zorder=1)
if min(dzr) < 0 < max(dzr):
ax1.add_collection3d(pc1)
ax1.set_xticks(np.arange(0.5, lx + 0.5, 1))
ax1.set_yticks(np.arange(0.5, ly + 0.5, 1))
if max_dzr != min_dzr:
ax1.axes.set_zlim3d(np.min(dzr), max(np.max(dzr) + 1e-9, max_dzi))
else:
if min_dzr == 0:
ax1.axes.set_zlim3d(np.min(dzr), max(np.max(dzr) + 1e-9, np.max(dzi)))
else:
ax1.axes.set_zlim3d(auto=True)
ax1.get_autoscalez_on()
ax1.w_xaxis.set_ticklabels(row_names, fontsize=14, rotation=45, ha="right", va="top")
ax1.w_yaxis.set_ticklabels(
column_names, fontsize=14, rotation=-22.5, ha="left", va="center"
)
ax1.set_zlabel("Re[$\\rho$]", fontsize=14)
for tick in ax1.zaxis.get_major_ticks():
tick.label.set_fontsize(14)
if ax2 is not None:
fc2 = generate_facecolors(xpos, ypos, zpos, dx, dy, dzi, color[1])
for idx, cur_zpos in enumerate(zpos):
if dzi[idx] > 0:
zorder = 2
else:
zorder = 0
b2 = ax2.bar3d(
xpos[idx],
ypos[idx],
cur_zpos,
dx[idx],
dy[idx],
dzi[idx],
alpha=alpha,
zorder=zorder,
)
b2.set_facecolors(fc2[6 * idx : 6 * idx + 6])
xlim, ylim = ax2.get_xlim(), ax2.get_ylim()
x = [xlim[0], xlim[1], xlim[1], xlim[0]]
y = [ylim[0], ylim[0], ylim[1], ylim[1]]
z = [0, 0, 0, 0]
verts = [list(zip(x, y, z))]
pc2 = Poly3DCollection(verts, alpha=0.2, facecolor="k", linewidths=1, zorder=1)
if min(dzi) < 0 < max(dzi):
ax2.add_collection3d(pc2)
ax2.set_xticks(np.arange(0.5, lx + 0.5, 1))
ax2.set_yticks(np.arange(0.5, ly + 0.5, 1))
if min_dzi != max_dzi:
eps = 0
ax2.axes.set_zlim3d(np.min(dzi), max(np.max(dzr) + 1e-9, np.max(dzi) + eps))
else:
if min_dzi == 0:
ax2.set_zticks([0])
eps = 1e-9
ax2.axes.set_zlim3d(np.min(dzi), max(np.max(dzr) + 1e-9, np.max(dzi) + eps))
else:
ax2.axes.set_zlim3d(auto=True)
ax2.w_xaxis.set_ticklabels(row_names, fontsize=14, rotation=45, ha="right", va="top")
ax2.w_yaxis.set_ticklabels(
column_names, fontsize=14, rotation=-22.5, ha="left", va="center"
)
ax2.set_zlabel("Im[$\\rho$]", fontsize=14)
for tick in ax2.zaxis.get_major_ticks():
tick.label.set_fontsize(14)
ax2.get_autoscalez_on()
fig.suptitle(title, fontsize=16)
if ax_real is None and ax_imag is None:
matplotlib_close_if_inline(fig)
if filename is None:
return fig
else:
return fig.savefig(filename)
def plot_bloch_multivector(
state, title="", figsize=None, *, rho=None, reverse_bits=False, filename=None
):
"""Plot the Bloch sphere.
Plot a sphere, axes, the Bloch vector, and its projections onto each axis.
Args:
state (Statevector or DensityMatrix or ndarray): an N-qubit quantum state.
title (str): a string that represents the plot title
figsize (tuple): Has no effect, here for compatibility only.
reverse_bits (bool): If True, plots qubits following Qiskit's convention [Default:False].
Returns:
matplotlib.Figure:
A matplotlib figure instance.
Raises:
MissingOptionalLibraryError: Requires matplotlib.
VisualizationError: if input is not a valid N-qubit state.
Example:
.. jupyter-execute::
from qiskit import QuantumCircuit
from qiskit.quantum_info import Statevector
from qiskit.visualization import plot_bloch_multivector
%matplotlib inline
qc = QuantumCircuit(2)
qc.h(0)
qc.cx(0, 1)
state = Statevector.from_instruction(qc)
plot_bloch_multivector(state, title="New Bloch Multivector", reverse_bits=False)
"""
if not HAS_MATPLOTLIB:
raise MissingOptionalLibraryError(
libname="Matplotlib",
name="plot_bloch_multivector",
pip_install="pip install matplotlib",
)
from matplotlib import pyplot as plt
# Data
bloch_data = (
_bloch_multivector_data(state)[::-1] if reverse_bits else _bloch_multivector_data(state)
)
num = len(bloch_data)
width, height = plt.figaspect(1 / num)
fig = plt.figure(figsize=(width, height))
for i in range(num):
pos = num - 1 - i if reverse_bits else i
ax = fig.add_subplot(1, num, i + 1, projection="3d")
plot_bloch_vector(bloch_data[i], "qubit " + str(pos), ax=ax, figsize=figsize)
fig.suptitle(title, fontsize=16, y=1.01)
matplotlib_close_if_inline(fig)
if filename is None:
return fig
else:
return fig.savefig(filename)
def pulse_drawer(
data: Union[Waveform, Union[Schedule, Instruction]],
dt: int = 1,
style: Union[PulseStyle, SchedStyle] = None,
filename: str = None,
interp_method: Callable = None,
scale: float = None,
channel_scales: Dict[Channel, float] = None,
plot_all: bool = False,
plot_range: Tuple[float, float] = None,
interactive: bool = False,
table: bool = False,
label: bool = False,
framechange: bool = True,
channels: List[Channel] = None,
show_framechange_channels: bool = True,
draw_title: bool = False,
):
"""Deprecated.
Plot the interpolated envelope of pulse and schedule.
Args:
data: Pulse or schedule object to plot.
dt: Time interval of samples. Pulses are visualized in the unit of
cycle time if not provided.
style: A style sheet to configure plot appearance.
See :mod:`~qiskit.visualization.pulse.qcstyle` for more information.
filename: Name required to save pulse image. The drawer just returns
`matplot.Figure` object if not provided.
interp_method: Interpolation function. Interpolation is disabled in default.
See :mod:`~qiskit.visualization.pulse.interpolation` for more information.
scale: Scaling of waveform amplitude. Pulses are automatically
scaled channel by channel if not provided.
channel_scales: Dictionary of scale factor for specific channels.
Scale of channels not specified here is overwritten by `scale`.
plot_all: When set `True` plot empty channels.
plot_range: A tuple of time range to plot.
interactive: When set `True` show the circuit in a new window.
This depends on the matplotlib backend being used supporting this.
table: When set `True` draw event table for supported commands.
label: When set `True` draw label for individual instructions.
framechange: When set `True` draw framechange indicators.
channels: A list of channel names to plot.
All non-empty channels are shown if not provided.
show_framechange_channels: When set `True` plot channels
with only framechange instructions.
draw_title: Add a title to the plot when set to ``True``.
Returns:
matplotlib.figure.Figure: A matplotlib figure object for the pulse envelope.
Example:
This example shows how to visualize your pulse schedule.
Pulse names are added to the plot, unimportant channels are removed
and the time window is truncated to draw out U3 pulse sequence of interest.
.. jupyter-execute::
import numpy as np
import qiskit
from qiskit import pulse
from qiskit.providers.fake_provider import FakeAlmaden
inst_map = FakeAlmaden().defaults().instruction_schedule_map
sched = pulse.Schedule()
sched += inst_map.get('u3', 0, np.pi, 0, np.pi)
sched += inst_map.get('measure', list(range(20))) << sched.duration
channels = [pulse.DriveChannel(0), pulse.MeasureChannel(0)]
scales = {pulse.DriveChannel(0): 10}
qiskit.visualization.pulse_drawer(sched,
channels=channels,
plot_range=(0, 1000),
label=True,
channel_scales=scales)
You are also able to call visualization module from the instance method::
sched.draw(channels=channels, plot_range=(0, 1000), label=True, channel_scales=scales)
To customize the format of the schedule plot, you can setup your style sheet.
.. jupyter-execute::
import numpy as np
import qiskit
from qiskit import pulse
from qiskit.providers.fake_provider import FakeAlmaden
inst_map = FakeAlmaden().defaults().instruction_schedule_map
sched = pulse.Schedule()
sched += inst_map.get('u3', 0, np.pi, 0, np.pi)
sched += inst_map.get('measure', list(range(20))) << sched.duration
# setup style sheet
my_style = qiskit.visualization.SchedStyle(
figsize = (10, 5),
bg_color='w',
d_ch_color = ['#32cd32', '#556b2f'])
channels = [pulse.DriveChannel(0), pulse.MeasureChannel(0)]
scales = {pulse.DriveChannel(0): 10}
qiskit.visualization.pulse_drawer(sched, style=my_style,
channels=channels,
plot_range=(0, 1000),
label=True,
channel_scales=scales)
Raises:
VisualizationError: when invalid data is given
MissingOptionalLibraryError: when matplotlib is not installed
"""
warnings.warn(
"This legacy pulse drawer is deprecated and will be removed no earlier than "
"3 months after the release date. Use `qiskit.visualization.pulse_drawer_v2` "
"instead. After the legacy drawer is removed, the import path of this module "
"will be dedicated to the v2 drawer. "
"New drawer will provide much more flexibility with richer stylesheets "
"and cleaner visualization.",
DeprecationWarning,
)
if isinstance(data, Waveform):
drawer = _matplotlib.WaveformDrawer(style=style)
image = drawer.draw(data,
dt=dt,
interp_method=interp_method,
scale=scale)
elif isinstance(data, (Schedule, Instruction)):
drawer = _matplotlib.ScheduleDrawer(style=style)
image = drawer.draw(
data,
dt=dt,
interp_method=interp_method,
scale=scale,
channel_scales=channel_scales,
plot_range=plot_range,
plot_all=plot_all,
table=table,
label=label,
framechange=framechange,
channels=channels,
show_framechange_channels=show_framechange_channels,
draw_title=draw_title,
)
else:
raise VisualizationError("This data cannot be visualized.")
if filename:
image.savefig(filename, dpi=drawer.style.dpi, bbox_inches="tight")
matplotlib_close_if_inline(image)
if image and interactive:
image.show()
return image