Hello world

Create a simple quantum program and run it on a simulator or real quantum system in this Hello world example. Begin with following the Install and set up instructions if you haven't already, including the steps to Set up to use IBM Quantum Platform.

We recommend that you use the Jupyter (opens in a new tab) development environment to interact with Qiskit. Be sure to install the recommended extra visualization support (pip install qiskit[visualization]), and note that zsh users need to put 'qiskit[visualization]' in single quotes.

To learn about quantum computing in general, check out the Basics of quantum information course (opens in a new tab) in IBM Quantum Learning.

Write a quantum program

Qiskit uses quantum circuits to represent quantum programs. When creating a circuit, you'll usually create a new QuantumCircuit object, then add instructions to it in sequence.

The following code cell creates a circuit that produces a Bell state, which is a specific two-qubit entangled state. For a simple experiment that uses a Bell state to show the effects of entanglement, check out the CHSH game (opens in a new tab).

from qiskit import QuantumCircuit
 
# Create a new circuit with two qubits (first argument) and two classical
# bits (second argument)
qc = QuantumCircuit(2, 2)
 
# Add a Hadamard gate to qubit 0
qc.h(0)
 
# Perform a controlled-X gate on qubit 1, controlled by qubit 0
qc.cx(0, 1)
 
# Measure qubit 0 to cbit 0, and qubit 1 to cbit 1
qc.measure(0, 0)
qc.measure(1, 1)
 
# Return a drawing of the circuit using MatPlotLib ("mpl"). This is the
# last line of the cell, so the drawing appears in the cell output.
# Remove the "mpl" argument to get a text drawing.
qc.draw("mpl")

Output:

See QuantumCircuit in the documentation for all available operations.

Run your quantum program

Quantum circuits can produce random results, so you'll often want to collect a sample of the outputs by running the circuit many times. You can sample a circuit's output using the Sampler class. Sampler is one of our two primitives, which provide methods that make it easier to build modular algorithms and other higher-order programs.

This tutorial uses Qiskit's built-in Sampler, which efficiently evaluates the possibility of multiple relevant data points in the context of destructive interference, but can only handle small numbers of qubits. You can replace this with samplers of higher-performance simulators from Qiskit Aer (opens in a new tab), or with samplers that send your circuit over the internet to run on real quantum computers (such as IBM's Qiskit Runtime (opens in a new tab) sampler).

from qiskit_ibm_runtime import QiskitRuntimeService, Sampler
 
service = QiskitRuntimeService()
 
# Run on the least-busy backend you have access to
backend = service.least_busy(simulator=False,operational=True)
 
# Create a Sampler object
sampler = Sampler(backend)
 
# Submit the circuit to the sampler
job = sampler.run(qc)
 
# Once the job is complete, get the result
job.result()

# Run on a simulator
backend = service.get_backend("ibmq_qasm_simulator")

The quasi_dists property is a list of sampled distributions for each circuit we provided. Since we only provided one circuit, the list only has one element.

The distributions are dictionary-like objects. Each key is a measured output, and its value is the fraction of the time that output was measured. In this case, the circuit measurement output was 0 ~50% of the time, and 3 (which is 11 in binary) ~50% of the time.

Plotting results

You can use the plot_histogram function to plot your Sampler results. This function returns a MatPlotLib Figure, which displays nicely in Jupyter notebooks.

from qiskit.visualization import plot_histogram
 
plot_histogram(
    job.result().quasi_dists
)

Output:

Next steps

import qiskit.tools.jupyter
%qiskit_version_table