Introduction to Qiskit

When using Qiskit a user workflow nominally consists of following four high-level steps:

  • Build: Design a quantum circuit(s) that represents the problem you are considering.

  • Compile: Compile circuits for a specific quantum service, e.g. a quantum system or classical simulator.

  • Run: Run the compiled circuits on the specified quantum service(s). These services can be cloud-based or local.

  • Analyze: Compute summary statistics and visualize the results of the experiments.

Here is an example of the entire workflow, with each step explained in detail in subsequent sections:

from qiskit import QuantumCircuit, transpile
from qiskit_aer import AerSimulator
from qiskit.visualization import plot_histogram

# Use Aer's AerSimulator
simulator = AerSimulator()

# Create a Quantum Circuit acting on the q register
circuit = QuantumCircuit(2, 2)

# Add a H gate on qubit 0

# Add a CX (CNOT) gate on control qubit 0 and target qubit 1
circuit.cx(0, 1)

# Map the quantum measurement to the classical bits
circuit.measure([0, 1], [0, 1])

# Compile the circuit for the support instruction set (basis_gates)
# and topology (coupling_map) of the backend
compiled_circuit = transpile(circuit, simulator)

# Execute the circuit on the aer simulator
job = simulator.run(compiled_circuit, shots=1000)

# Grab results from the job
result = job.result()

# Returns counts
counts = result.get_counts(compiled_circuit)
print("\nTotal count for 00 and 11 are:", counts)

# Draw the circuit

(Source code)

# Plot a histogram

(Source code)


Workflow Step–by–Step

The program above can be broken down into six steps:

  1. Import packages

  2. Initialize variables

  3. Add gates

  4. Visualize the circuit

  5. Simulate the experiment

  6. Visualize the results

Step 1 : Import Packages

The basic elements needed for your program are imported as follows:

from qiskit import QuantumCircuit
from qiskit_aer import AerSimulator
from qiskit.visualization import plot_histogram

In more detail, the imports are

  • QuantumCircuit: can be thought as the instructions of the quantum system. It holds all your quantum operations.

  • AerSimulator: is the Aer high performance circuit simulator.

  • plot_histogram: creates histograms.

Step 2 : Initialize Variables

Consider the next line of code

circuit = QuantumCircuit(2, 2)

Here, you are initializing with 2 qubits in the zero state; with 2 classical bits set to zero; and circuit is the quantum circuit.


  • QuantumCircuit(int, int)

Step 3 : Add Gates

You can add gates (operations) to manipulate the registers of your circuit.

Consider the following three lines of code:

circuit.cx(0, 1)
circuit.measure([0, 1], [0, 1])

The gates are added to the circuit one-by-one to form the Bell state

\[|\psi\rangle = \left(|00\rangle+|11\rangle\right)/\sqrt{2}.\]

The code above applies the following gates:

  • QuantumCircuit.h(0): A Hadamard gate \(H\) on qubit 0, which puts it into a superposition state.

  • QuantumCircuit.cx(0, 1): A controlled-Not operation (\(CNOT\)) on control qubit 0 and target qubit 1, putting the qubits in an entangled state.

  • QuantumCircuit.measure([0,1], [0,1]): if you pass the entire quantum and classical registers to measure, the ith qubit’s measurement result will be stored in the ith classical bit.

Step 4 : Visualize the Circuit

You can use qiskit.circuit.QuantumCircuit.draw() to view the circuit that you have designed in the various forms used in many textbooks and research articles.


(Source code)


In this circuit, the qubits are ordered with qubit zero at the top and qubit one at the bottom. The circuit is read left-to-right, meaning that gates which are applied earlier in the circuit show up farther to the left.

The default backend for QuantumCircuit.draw() or qiskit.visualization.circuit_drawer() is the text backend. However, depending on your local environment you may want to change these defaults to something better suited for your use case. This is done with the user config file. By default the user config file should be located in ~/.qiskit/settings.conf and is a .ini file.

For example, a settings.conf file for setting a Matplotlib drawer is:

circuit_drawer = mpl

You can use any of the valid circuit drawer backends as the value for this config, this includes text, mpl, latex, and latex_source.

Step 5 : Simulate the Experiment

Qiskit Aer is a high performance simulator framework for quantum circuits. It provides several backends to achieve different simulation goals.

If you have issues installing Aer, you can alternatively use the Basic Aer provider by replacing Aer with BasicAer. Basic Aer is included in Qiskit Terra.

from qiskit import QuantumCircuit, transpile
from qiskit.providers.basicaer import QasmSimulatorPy

To simulate this circuit, you will use the AerSimulator. Each run of this circuit will yield either the bit string 00 or 11.

simulator = AerSimulator()
compiled_circuit = transpile(circuit, simulator)
job = simulator.run(compiled_circuit, shots=1000)
result = job.result()
counts = result.get_counts(circuit)
print("\nTotal count for 00 and 11 are:",counts)

(Source code)

As expected, the output bit string is 00 approximately 50 percent of the time. The number of times the circuit is run can be specified via the shots argument of the execute method. The number of shots of the simulation was set to be 1000 (the default is 1024).

Once you have a result object, you can access the counts via the method get_counts(circuit). This gives you the aggregate outcomes of the experiment you ran.

Step 6 : Visualize the Results

Qiskit provides many visualizations,

including the function plot_histogram, to view your results.


(Source code)


The observed probabilities \(Pr(00)\) and \(Pr(11)\) are computed by taking the respective counts and dividing by the total number of shots.


Try changing the shots keyword in the run() method to see how the estimated probabilities change.

Next Steps

Now that you have learnt the basics, consider these learning resources: