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 circuit.h(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 circuit.draw("mpl")
# Plot a histogram plot_histogram(counts)
The program above can be broken down into six steps:
Visualize the circuit
Simulate the experiment
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.
Step 3 : Add Gates¶
You can add gates (operations) to manipulate the registers of your circuit.
Consider the following three lines of code:
circuit.h(0) 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
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.
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
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
For example, a
settings.conf file for setting a Matplotlib drawer is:
[default] 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)
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
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.
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.
Now that you have learnt the basics, consider these learning resources: