Code your first quantum circuit

Learn to code your first quantum circuit in Quantum Lab, without downloading anything to your computer. While you can benefit from having some familiarity with Python and linear algebra, you can get a sense of the big picture without those prerequisites.


To code any quantum circuit in Quantum Lab, you follow three high-level steps:

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

  • Execute: Run circuits on different backends, either systems or simulators.

  • Analyze: Calculate summary statistics and visualize the results of your circuit jobs.

The following instructions guide you through building a circuit with example code in a Jupyter Notebook environment, executing your program, and analyzing the results. You will then learn in greater detail how each component of the program functions.

Run code in Quantum Lab

1. Sign in

Go to IBM Quantum Experience. Click the Sign in button in the upper right corner, then sign in or create an IBMid account.

page.

2. Open Quantum Lab

Open Quantum Lab by clicking on the Quantum Lab icon in the left navigation bar.

Left-hand navigation button for Quantum Lab.

Click on New Notebook +. The first cell in your new notebook imports Qiskit into your Jupyter Notebook environment.

New notebook button.

3. Enter code into a notebook

Copy Code Snippet 1 (below) and paste it in an empty code block cell in your new notebook, then click the Run button. (You can also run the cell by holding the Shift key and then pressing the Enter/Return key on your keyboard.)

Code Snippet 1

# Build

# 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, 1)

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

# Execute

# Use Aer's qasm_simulator
simulator = Aer.get_backend('qasm_simulator')

# Execute the circuit on the qasm simulator
job = execute(circuit, simulator, shots=1000)

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

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

# Analyze

# Draw the circuit


Total count for 00 and 11 are: {'00': 508, '11': 492}
     ┌───┐     ┌─┐   
q_0: ┤ H ├──■──┤M├───
q_1: ─────┤ X ├─╫─┤M├
          └───┘ ║ └╥┘
c: 2/═══════════╩══╩═
                0  1 

4. Plot a histogram

To see a visualization of the results, copy and paste Code Snippet 2 (below) into the next empty cell in your notebook, and click the Run button. This visualization is from a single-shot statevector simulator.

Code Snippet 2

# Analyze

# Plot a histogram

Explanation: components of the program

The program contains six actions. The comments above each line of code indicate which of the six actions it is performing. Explore the details of each in the following sections.

  • Build

  1. Import packages

  2. Initialize variables

  3. Add gates

  • Execute

  1. Simulate the experiment

  • Analyze

  1. Visualize the circuit

  2. Visualize the results

Import packages

The import lines import the basic elements (packages and functions) needed for your program. The following imports are the default imports for any new notebook in Quantum Lab, so they do not appear in the code snippet.

%matplotlib inline
# Importing standard Qiskit libraries and configuring account
from qiskit import QuantumCircuit, execute, Aer, IBMQ
from qiskit.compiler import transpile, assemble
from import *
from qiskit.visualization import *
# Loading your IBM Q account(s)
provider = IBMQ.load_account()

The imports used in the rest of the code example are

  • QuantumCircuit: Holds all your quantum operations; the instructions for the quantum system

  • execute: Runs your circuit

  • Aer: Handles simulator backends

  • qiskit.visualization: Enables data visualization, such as plot_histogram

Initialize variables

In the next line of code, you initialize two qubits in the zero state, and two classical bits in the zero state, in the quantum circuit called circuit.

circuit = QuantumCircuit(2, 2)

Add gates

The next three lines of code, beginning with circuit., add gates that manipulate the qubits in your circuit.

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

The code above applies the following gates:

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

  •, 1): A controlled-NOT operation ( C_{X} ) on control qubit 0 and target qubit 1, putting the qubits in an entangled state

  • QuantumCircuit.measure([0,1], [0,1]): The first argument indexes the qubits, the second argument indexes the classical bits. The nth qubit’s measurement result will be stored in the nth classical bit.

This particular trio of gates added one-by-one to the circuit forms the Bell state,

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

In this state, there is a 50% chance of finding both qubits to have the value 0 and a 50% chance of finding both qubits to have the value 1.

Simulate the experiment

The next line of code calls a specific simulator framework – in this case, it calls Qiskit Aer, which is a high-performance simulator that provides several backends to achieve different simulation goals.

In this program, you use the qasm_simulator. Each run of this circuit will yield either the bit string '00' or '11'.

simulator = Aer.get_backend('qasm_simulator')
job = execute(circuit, simulator, shots=1000)
result = job.result()
counts = result.get_counts(circuit)
print("\nTotal count for 00 and 11 are:",counts)

Total count for 00 and 11 are: {'00': 485, '11': 515}

The program specifies the number of times to run the circuit in the shots argument of the execute method (job = execute(circuit, simulator, shots=1000)). The number of shots of the simulation is set to 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 your experiment.

As expected, the output bit string is '00' approximately 50 percent of the time. This simulator does not model noise. Any deviation from 50 percent is due to the small sample size.

Visualize the circuit

QuantumCircuit.draw() (called by circuit.draw() in the code) displays your circuit in one of the various styles used in textbooks and research articles.

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

In this circuit, the qubits are ordered with qubit zero at the top and qubit one below it. The circuit is read from left to right, representing the passage of time.

Visualize the results

Qiskit provides many visualizations, including the function plot_histogram, to view your results.


The probabilities (relative frequencies) of observing the |00\rangle and |11\rangle states are computed by taking the respective counts and dividing by the total number of shots.

Next steps

Now that you have learned the basics, return to the Quantum Lab landing page and create your own notebooks with cloud-based access to Qiskit, or continue your quantum journey with the Qiskit textbook, Learn Quantum Computation with Qiskit.