Python Advanced - Quantum Computing with Qiskit

Introduction to quantum computing using Qiskit in Python

Qiskit is an open-source quantum computing software development framework for leveraging today's quantum processors in research, education, and business. This tutorial explores how to use Qiskit for quantum computing in Python.

Installing Qiskit

To use Qiskit, you need to install it using pip:

pip install qiskit
            

Creating a Quantum Circuit

Here is an example of creating a simple quantum circuit with Qiskit:

from qiskit import QuantumCircuit, Aer, transpile, assemble, execute
from qiskit.visualization import plot_histogram

# Create a quantum circuit with 2 qubits and 2 classical bits
qc = QuantumCircuit(2, 2)

# Apply a Hadamard gate to the first qubit
qc.h(0)

# Apply a CNOT gate to the second qubit controlled by the first qubit
qc.cx(0, 1)

# Measure the qubits
qc.measure([0, 1], [0, 1])

# Draw the quantum circuit
print(qc.draw())

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

# Transpile and assemble the quantum circuit
tqc = transpile(qc, simulator)
qobj = assemble(tqc)

# Execute the quantum circuit
result = execute(qc, backend=simulator).result()

# Get the counts (measurement results)
counts = result.get_counts()

# Plot the histogram of results
plot_histogram(counts)
            

Quantum Gates and Operations

Here is an example of applying various quantum gates to a quantum circuit:

# Create a quantum circuit with 3 qubits
qc = QuantumCircuit(3)

# Apply X (NOT) gate to the first qubit
qc.x(0)

# Apply H (Hadamard) gate to the second qubit
qc.h(1)

# Apply Z gate to the third qubit
qc.z(2)

# Draw the quantum circuit
print(qc.draw())
            

Simulating Quantum Circuits

Here is an example of simulating a quantum circuit using Aer's statevector_simulator:

# Use the Aer's statevector_simulator
statevector_simulator = Aer.get_backend('statevector_simulator')

# Execute the quantum circuit
result = execute(qc, backend=statevector_simulator).result()

# Get the statevector
statevector = result.get_statevector()

# Print the statevector
print(statevector)
            

Quantum Algorithms

Here is an example of implementing the Grover's algorithm with Qiskit:

from qiskit import QuantumCircuit, Aer, transpile, assemble, execute
from qiskit.visualization import plot_histogram

# Create a quantum circuit for Grover's algorithm
grover_circuit = QuantumCircuit(2, 2)

# Apply Hadamard gate to both qubits
grover_circuit.h([0, 1])

# Apply the Oracle (X, CX, X)
grover_circuit.x(0)
grover_circuit.cx(0, 1)
grover_circuit.x(0)

# Apply Hadamard gate to both qubits
grover_circuit.h([0, 1])

# Measure the qubits
grover_circuit.measure([0, 1], [0, 1])

# Draw the quantum circuit
print(grover_circuit.draw())

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

# Transpile and assemble the quantum circuit
tqc = transpile(grover_circuit, simulator)
qobj = assemble(tqc)

# Execute the quantum circuit
result = execute(grover_circuit, backend=simulator).result()

# Get the counts (measurement results)
counts = result.get_counts()

# Plot the histogram of results
plot_histogram(counts)
            

Running on Real Quantum Hardware

Here is an example of running a quantum circuit on IBM's real quantum hardware:

from qiskit import IBMQ

# Load your IBMQ account
IBMQ.load_account()

# Get the least busy backend
provider = IBMQ.get_provider(hub='ibm-q')
backend = provider.get_backend('ibmq_quito')

# Execute the quantum circuit on the real quantum hardware
job = execute(qc, backend=backend, shots=1024)

# Monitor the job
from qiskit.tools.monitor import job_monitor
job_monitor(job)

# Get the results
result = job.result()

# Get the counts (measurement results)
counts = result.get_counts()

# Plot the histogram of results
plot_histogram(counts)
            

Quantum Machine Learning

Here is an example of using Qiskit Machine Learning to create a simple quantum classifier:

from qiskit_machine_learning.algorithms import QSVM
from qiskit_machine_learning.kernels import QuantumKernel
from qiskit import BasicAer

# Define the quantum kernel
quantum_kernel = QuantumKernel(feature_map=qc, quantum_instance=BasicAer.get_backend('qasm_simulator'))

# Create the QSVM classifier
qsvm = QSVM(quantum_kernel)

# Train the QSVM classifier
qsvm.fit(X_train, y_train)

# Predict using the QSVM classifier
predictions = qsvm.predict(X_test)

# Evaluate the classifier
accuracy = accuracy_score(y_test, predictions)
print(f"Accuracy: {accuracy:.2f}")
            

Summary

In this tutorial, you learned about quantum computing using Qiskit in Python. Qiskit is a powerful open-source quantum computing software development framework that allows you to create, simulate, and run quantum computing algorithms. Understanding how to create quantum circuits, apply quantum gates, simulate quantum circuits, implement quantum algorithms, and run on real quantum hardware can help you leverage Qiskit for various quantum computing applications.