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Visualize circuits

A visualization is useful while working with quantum circuits. Below find options in Qiskit® for drawing circuits, plotting data from executed jobs, seeing the state of a quantum computer, and more.

[1] :
from qiskit import QuantumCircuit, ClassicalRegister, QuantumRegister

Draw a quantum circuit

Drawing a circuit is supported natively by a QuantumCircuit object. You can either call print() on the circuit, or call the draw() method on the object. This will render an ASCII art version of the circuit diagram.

[2] :
# Build a quantum circuit
circuit = QuantumCircuit(3, 3)
circuit.h(range(3)), 1)
circuit.measure(range(3), range(3));
[3] :


     ┌───┐          ┌─┐   
q_0: ┤ H ├───────■──┤M├───
q_1: ┤ X ├┤ H ├┤ X ├─╫─┤M├
     ├───┤└┬─┬┘└───┘ ║ └╥┘
q_2: ┤ H ├─┤M├───────╫──╫─
     └───┘ └╥┘       ║  ║ 
c: 3/═══════╩════════╩══╩═
            2        0  1 
[4] :


     ┌───┐          ┌─┐   
q_0: ┤ H ├───────■──┤M├───
q_1: ┤ X ├┤ H ├┤ X ├─╫─┤M├
     ├───┤└┬─┬┘└───┘ ║ └╥┘
q_2: ┤ H ├─┤M├───────╫──╫─
     └───┘ └╥┘       ║  ║ 
c: 3/═══════╩════════╩══╩═
            2        0  1 

Alternative renderers

A text output is useful for quickly seeing the output while developing a circuit, but it doesn't provide the most flexibility. There are two alternative output renderers for the quantum circuit. One uses matplotlib(opens in a new tab), and the other uses LaTeX(opens in a new tab), which leverages the qcircuit package(opens in a new tab). These can be specified with mpl and latex values for the output kwarg on the draw() method.


OSX users can get the required LaTeX packages through the mactex package(opens in a new tab)

[5] :
# Matplotlib drawing


<Figure size 538.33x367.889 with 1 Axes>
[6] :
# Latex drawing


<PIL.Image.Image image mode=RGB size=343x132>

Control output from circuit.draw()

By default, the draw() method returns the rendered image as an object and does not output anything. The exact class returned depends on the output specified: 'text' (the default) returns a TextDrawer object, 'mpl' returns a matplotlib.Figure object, and latex returns a PIL.Image object. These return types enable modifying or directly interacting with the rendered output from the drawers.

Jupyter notebooks understand these return types and render them properly, but when running outside of Jupyter, this feature is not automatic. However, the draw() method has optional arguments to display or save the output. When specified, the filename kwarg takes a path to which it saves the rendered output. Alternatively, if you're using the mpl or latex outputs, you can leverage the interactive kwarg to open the image in a new window (this will not always work from within a notebook).

Customize the output

Depending on the output, there are also options to customize the circuit diagram.

Disable plot barriers and reverse bit order

The first two options are shared among all three backends. They allow you to configure both the bit orders and whether or not you draw barriers. These can be set by the reverse_bits kwarg and plot_barriers kwarg, respectively. The examples below will work with any output renderer; mpl is used here for brevity.

[7] :
# Draw a new circuit with barriers and more registers
q_a = QuantumRegister(3, name='qa')
q_b = QuantumRegister(5, name='qb')
c_a = ClassicalRegister(3)
c_b = ClassicalRegister(5)
circuit = QuantumCircuit(q_a, q_b, c_a, c_b)
circuit.cswap(q_b[0], q_b[1], q_b[2])
circuit.cswap(q_b[2], q_b[3], q_b[4])
circuit.cswap(q_b[3], q_b[4], q_b[0])
circuit.measure(q_a, c_a)
circuit.measure(q_b, c_b);
[8] :
# Draw the circuit


<Figure size 1392.87x869.556 with 1 Axes>
[9] :
# Draw the circuit with reversed bit order
circuit.draw(output='mpl', reverse_bits=True)


<Figure size 1142.04x869.556 with 1 Axes>
[10] :
# Draw the circuit without barriers
circuit.draw(output='mpl', plot_barriers=False)


<Figure size 1225.65x869.556 with 1 Axes>
[11] :
# Draw the circuit without barriers and reverse bit order
circuit.draw(output='mpl', plot_barriers=False, reverse_bits=True)


<Figure size 1058.43x869.556 with 1 Axes>

Renderer-specific customizations

Some available customizing options are specific to a renderer.

The fold kwarg can be used to set a maximum width for the output. In the text renderer, this sets the length of the lines of the diagram before it is wrapped to the next line. When using the 'mpl' renderer, this is used as the number of (visual) layers before folding to the next line.

The mpl renderer has the style kwarg, which is used to customize the output.

The scale option is used by the mpl and latex renderers to scale the size of the output image with a multiplicative adjustment factor.

The style kwarg takes in a dict with multiple options, including specifying colors, changing rendered text for different types of gates, choosing different line styles, and more.

Available options are:

  • textcolor (str): The color code for text. Defaults to '#000000'

  • subtextcolor (str): The color code for subtext. Defaults to '#000000'

  • linecolor (str): The color code for lines. Defaults to '#000000'

  • creglinecolor (str): The color code for classical register lines '#778899'

  • gatetextcolor (str): The color code for gate text '#000000'

  • gatefacecolor (str): The color code for gates. Defaults to '#ffffff'

  • barrierfacecolor (str): The color code for barriers. Defaults to '#bdbdbd'

  • backgroundcolor (str): The color code for the background. Defaults to '#ffffff'

  • fontsize (int): The font size for text. Defaults to 13

  • subfontsize (int): The font size for subtext. Defaults to 8

  • displaytext (dict): A dictionary of the text for each element type in the output visualization. The default values are:

    'id': 'id', 'u0': 'U_0', 'u1': 'U_1', 'u2': 'U_2', 'u3': 'U_3', 'x': 'X', 'y': 'Y', 'z': 'Z', 'h': 'H', 's': 'S', 'sdg': 'S^\dagger', 't': 'T', 'tdg': 'T^\dagger', 'rx': 'R_x', 'ry': 'R_y', 'rz': 'R_z', 'reset': '\left|0\right\rangle'

    You must specify all the necessary values if using this. There is no provision for an incomplete dict passed in.

  • displaycolor (dict): The color codes to use for each circuit element. By default, all values default to the value of gatefacecolor and the keys are the same as displaytext. Also, just like displaytext, there is no provision for an incomplete dict passed in.

  • latexdrawerstyle (bool): When set to True, enable LaTeX mode, which will draw gates like the latex output modes.

  • usepiformat (bool): When set to True, use radians for output.

  • fold (int): The number of circuit elements at which to fold the circuit. Defaults to 20

  • cregbundle (bool): If set True, bundle classical registers.

  • showindex (bool): If set True, draw an index.

  • compress (bool): If set True, draw a compressed circuit.

  • figwidth (int): The maximum width (in inches) for the output figure.

  • dpi (int): The DPI to use for the output image. Defaults to 150.

  • creglinestyle (str): The style of line to use for classical registers. Choices are 'solid', 'doublet', or any valid matplotlib linestyle kwarg value. Defaults to doublet.

[12] :
# Set line length to 80 for above circuit


            ░ ┌───┐ ░    ┌─┐                           
qa_0: ──────░─┤ H ├─░────┤M├───────────────────────────
      ┌───┐ ░ ├───┤ ░    └╥┘┌─┐                        
qa_1: ┤ X ├─░─┤ H ├─░─────╫─┤M├────────────────────────
      └───┘ ░ ├───┤ ░     ║ └╥┘┌─┐                     
qa_2: ──────░─┤ H ├─░─────╫──╫─┤M├─────────────────────
            ░ ├───┤ ░     ║  ║ └╥┘    ░ ┌─┐            
qb_0: ──────░─┤ H ├─■─────╫──╫──╫──X──░─┤M├────────────
      ┌───┐ ░ ├───┤ │     ║  ║  ║  │  ░ └╥┘┌─┐         
qb_1: ┤ X ├─░─┤ H ├─X─────╫──╫──╫──┼──░──╫─┤M├─────────
      ├───┤ ░ ├───┤ │     ║  ║  ║  │  ░  ║ └╥┘┌─┐      
qb_2: ┤ X ├─░─┤ H ├─X──■──╫──╫──╫──┼──░──╫──╫─┤M├──────
      └───┘ ░ ├───┤    │  ║  ║  ║  │  ░  ║  ║ └╥┘┌─┐   
qb_3: ──────░─┤ H ├────X──╫──╫──╫──■──░──╫──╫──╫─┤M├───
      ┌───┐ ░ ├───┤    │  ║  ║  ║  │  ░  ║  ║  ║ └╥┘┌─┐
qb_4: ┤ X ├─░─┤ H ├────X──╫──╫──╫──X──░──╫──╫──╫──╫─┤M├
      └───┘ ░ └───┘       ║  ║  ║     ░  ║  ║  ║  ║ └╥┘
c0: 3/════════════════════╩══╩══╩════════╬══╬══╬══╬══╬═
                          0  1  2        ║  ║  ║  ║  ║ 
c1: 5/═══════════════════════════════════╩══╩══╩══╩══╩═
                                         0  1  2  3  4 
[13] :
# Change the background color in mpl
style = {'backgroundcolor': 'lightgreen'}
circuit.draw(output='mpl', style=style)


<Figure size 1392.87x869.556 with 1 Axes>
[14] :
# Scale the mpl output to 1/2 the normal size
circuit.draw(output='mpl', scale=0.5)


<Figure size 696.437x434.778 with 1 Axes>

circuit_drawer() as function

If you have an application where you prefer to draw a circuit with a self-contained function instead of as a method of a circuit object, you can directly use the circuit_drawer() function, which is part of the public stable interface from qiskit.visualization. The function behaves identically to the circuit.draw() method, except that it takes in a circuit object as required argument.

Note: In Qiskit Terra <=<= 0.7, the default behavior for the circuit_drawer() from import circuit_drawer function is to use the latex output backend, and in 0.6.x that includes a fallback to mpl if latex fails for any reason. Starting with release > 0.7, the default changes to the text output.

[15] :
from qiskit.visualization import circuit_drawer
[16] :
circuit_drawer(circuit, output='mpl', plot_barriers=False)


<Figure size 1225.65x869.556 with 1 Axes>

Plot histogram

The following function visualizes the data from a quantum circuit executed on a system or simulator.


For example, make a two-qubit Bell state:

[17] :
from qiskit_aer import Aer
from qiskit import *
from qiskit.visualization import plot_histogram
[18] :
# quantum circuit to make a Bell state
bell = QuantumCircuit(2, 2)
bell.h(0), 1)
meas = QuantumCircuit(2, 2)
meas.measure([0,1], [0,1])
# execute the quantum circuit
backend = Aer.get_backend('qasm_simulator') # the device to run on
circ = bell.compose(meas)
result =, shots=1000).result()
counts  = result.get_counts(circ)


{'00': 528, '11': 472}
[19] :


<Figure size 700x500 with 1 Axes>

Options when plotting a histogram

Use the following options for plot_histogram() to adjust the output graph.

  • legend: Provides a label for the executions. It takes a list of strings used to label each execution's results. This is mostly useful when plotting multiple execution results in the same histogram
  • sort: Adjusts the order the bars in the histogram are rendered. It can be set to either ascending order with asc or descending order with desc
  • number_to_keep: Takes an integer for the number of terms to show. The rest are grouped together in a single bar called "rest"
  • color: Adjusts the color of the bars; takes a string or a list of strings for the colors to use for the bars for each execution.
  • bar_labels: Adjusts whether labels are printed above the bars
  • figsize: Takes a tuple of the size in inches to make the output figure
[20] :
# Execute two-qubit Bell state again
second_result =, shots=1000).result()
second_counts  = second_result.get_counts(circ)
# Plot results with legend
legend = ['First execution', 'Second execution']
plot_histogram([counts, second_counts], legend=legend)


<Figure size 700x500 with 1 Axes>
[21] :
plot_histogram([counts, second_counts], legend=legend, sort='desc', figsize=(15,12),
               color=['orange', 'black'], bar_labels=False)


<Figure size 1500x1200 with 1 Axes>

Use the output from plot_histogram()

When using the plot_histogram() function, it returns a matplotlib.Figure for the rendered visualization. Jupyter notebooks understand this return type and render it properly, but when running outside of Jupyter,this feature is not automatic. However, the matplotlib.Figure class natively has methods to both display and save the visualization. You can call .show() on the returned object from plot_histogram() to open the image in a new window (assuming your configured matplotlib backend is interactive). Alternatively, call .savefig('out.png') to save the figure to out.png. The savefig() method takes a path so you can adjust the location and filename where you're saving the output.

Plot state

In many situations it is helpful to see the state of a quantum computer - perhaps for debugging purposes. Here we assume you have a particular state (either from simulation or state tomography), and the goal is to visualize the quantum state. This requires exponential resources, so we advise to only view the state of small quantum systems. There are several functions for generating different types of visualization of a quantum state:


A quantum state is either a density matrix ρ\rho (Hermitian matrix) or statevector ψ|\psi\rangle (complex vector). The density matrix is related to the statevector by

ρ=ψψ,\rho = |\psi\rangle\langle \psi|,

and is more general as it can represent mixed states (positive sum of statevectors)

ρ=kpkψkψk.\rho = \sum_k p_k |\psi_k\rangle\langle \psi_k |.

The visualizations generated by the functions are:

  • 'plot_state_city': The standard view for quantum states where the real and imaginary (imag) parts of the density matrix are plotted like a city.

  • 'plot_state_qsphere': The Qiskit-unique view of a quantum state where the amplitude and phase of the state vector are plotted in a spherical ball. The amplitude is the thickness of the arrow and the phase is the color. For mixed states it will show different 'qsphere' for each component.

  • 'plot_state_paulivec': The representation of the density matrix using Pauli operators as the basis ρ=q=0d21pjPj/d\rho=\sum_{q=0}^{d^2-1}p_jP_j/d.

  • 'plot_state_hinton': Same as 'city' but where the size of the element represents the value of the matrix element.

  • 'plot_bloch_multivector': The projection of the quantum state onto the single-qubit space and plotting on a Bloch sphere.

[22] :
from qiskit.visualization import plot_state_city, plot_bloch_multivector
from qiskit.visualization import plot_state_paulivec, plot_state_hinton
from qiskit.visualization import plot_state_qsphere
[23] :
# execute the quantum circuit
backend = Aer.get_backend('statevector_simulator') # the device to run on
result =
psi  = result.get_statevector(bell)
[24] :


<Figure size 1600x800 with 2 Axes>
[25] :


<Figure size 800x500 with 2 Axes>
[26] :


<Figure size 700x700 with 2 Axes>
[27] :


<Figure size 700x500 with 1 Axes>
[28] :