12. CODING EXAMPLES (PYTHON AND QISKIT)  

Example 1: 

from qiskit import QuantumCircuit, Aer, execute 

# Create a quantum circuit with 2 qubits 

qc = QuantumCircuit(2, 2) 

# Apply Hadamard gate 

qc.h(0) 

qc.h(1) 

# Apply CNOT gate 

qc.cx(0, 1) 

# Perform measurement 

qc.measure([0, 1], [0, 1]) 

# Run in the simulator 

simulator = Aer.get_backend('qasm_simulator') 

job = execute(qc, simulator, shots=1000) 

result = job.result() 

# Display the results 

counts = result.get_counts(qc) 

print("\nTotal count for 00 and 11 are:",counts) 

This code creates a circuit with 2 qubits, applies a Hadamard gate and a CNOT gate, performs measurement, and simulates the result. 

Example 2: 

This time, let's tackle a basic data analysis problem and try to find the correlation between the data. In this example, we will work on a simple dataset and create a quantum circuit to measure the correlation between the data: 

1. Load Required Libraries and Dataset: 

       import numpy as np 

       from qiskit import QuantumCircuit, Aer, execute 

       from qiskit.visualization import plot_histogram 

2. Creating a Sample Dataset: Let's create a simple dataset consisting of 3 features: 

       np.random.seed(0) 

       data_points = 100 

       feature1 = np.random.rand(data_points) 

       feature2 = np.random.rand(data_points) 

       feature3 = np.random.rand(data_points) 

3. Creating a Quantum Circuit: We will create one qubit for each of the 3 features and design a circuit to measure the correlation between these qubits: 

       qc = QuantumCircuit(3, 3) 

       qc.h(range(3)) 

       qc.measure(range(3), range(3)) 

4. Performing Simulation: Let's run the created circuit on a simulator: 

       simulator = Aer.get_backend('qasm_simulator') 

       job = execute(qc, simulator, shots=1000) 

       result = job.result() 

       counts = result.get_counts(qc) 

5. Visualizing the Results: Visualize the obtained results in the form of a histogram: 

       plot_histogram(counts) 

This example demonstrates a basic quantum approach for data analysis. However, when working with real data, more complex and effective algorithms may be required. 

Details: 

1. qc = QuantumCircuit(3, 3): This line creates a quantum circuit with three qubits (QuantumCircuit(3)) and three classical bits (QuantumCircuit(3)). The first parameter specifies the number of qubits, and the second parameter specifies the number of classical bits. 

2. qc.h(range(3)): This line applies the Hadamard (H) gate to all three qubits, putting them into a superposition state. The range(3) expression selects the three qubits corresponding to indices 0, 1, and 2. 

3. qc.measure(range(3), range(3)): This line connects the quantum circuit to classical bits by performing measurement. The first parameter specifies which qubits will be measured (range(3) selects all qubits), and the second parameter indicates the order in which the measurement results will be written to classical bits (range(3) selects all classical bits). 

4. job = execute(qc, simulator, shots=1000): The shots parameter specifies how many times a quantum circuit will be executed. By running the quantum circuit multiple times, different measurement results can be obtained and analyzed statistically. For instance, shots=1000 means the quantum circuit will be executed 1000 times, obtaining a measurement result with each execution. This ensures that the obtained results are more reliable and statistically significant. 

Example 3: 

Grover's Search Algorithm: This algorithm is used to quickly find a target item in a database. 

from qiskit import QuantumCircuit, Aer, execute

n = 3  # Number of qubits 

qc = QuantumCircuit(n) 

# Create superposition 

qc.h(range(n)) 

qc.cz(0, 2) 

# Invert 

qc.h(range(n)) 

qc.z(range(n)) 

# Simulate the circuit and visualize the results 

backend = Aer.get_backend('qasm_simulator') 

job = execute(qc, backend, shots=1) 

result = job.result() 

counts = result.get_counts() 

print(counts) 

Example 4: 

Deutsch-Jozsa Algorithm: This algorithm is used to determine whether a function within a black box is constant or balanced. 

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

n = 3  # Number of qubits
qc = QuantumCircuit(n+1, n) 

# Preparation: set all qubits to |0⟩ and the last qubit to |1⟩
qc.x(n)
qc.barrier() 

# Create superposition
qc.h(range(n+1))
qc.barrier()  

for qubit in range(n):
    qc.cx(qubit, n) 

qc.barrier() 

# Flip the last qubits
qc.h(range(n))
qc.measure(range(n), range(n))  

# Simulate the circuit and visualize the results
backend = Aer.get_backend('qasm_simulator')
job = execute(qc, backend, shots=1)
result = job.result()
counts = result.get_counts() => print(counts) 

Example 5: 

Vernam Cipher: Encrypts by XORing each character with a random key. In this example, we can perform a simple Vernam Cipher encryption using a quantum circuit with 2 qubits. 

from qiskit import QuantumCircuit, Aer, execute 
from qiskit.visualization import plot_histogram
import random 

# Create key
key = [random.choice([0, 1]) for _ in range(2)] 

# Create text
message = 'hello'
binary_message = ''.join(format(ord(char), '08b') for char in message) 

# Create Qiskit circuit
qc = QuantumCircuit(len(message)*2, len(message)*2) 

# Apply key to the circuit
for i, bit in enumerate(key):
    if bit == 1:
        qc.x(i) 

# Apply message to the circuit
for i, bit in enumerate(binary_message):
    if bit == '1':
        qc.x(i+len(key)) 

# XOR operation
for i in range(len(message)*2):
    qc.cx(i, i+len(key)) 

qc.measure(range(len(message)*2), range(len(message)*2)) 

# Simulate the circuit and visualize the result
backend = Aer.get_backend('qasm_simulator')
job = execute(qc, backend, shots=1)
result = job.result()
counts = result.get_counts() => print(counts)

Result

Quantum programming, while still in its developmental stages, offers potentially revolutionary applications. This article provides information on how quantum computers operate differently from classical computers, the fundamentals of quantum mechanics and qubits, and the tools and technologies for quantum programming. Additionally, with examples of simulation environments provided by IBM and how to write quantum algorithms, one can take initial steps into the world of quantum programming. Discussions on how quantum programming can be applied in daily life and its possible future applications suggest that developments in this field will rapidly gain momentum. In conclusion, it can be said that quantum computing will hold a significant place in the computing world in the coming years and has the potential to transform daily life in many areas. My opinion is that within 3-4 years, all sectors will experience a significant transformation, and particularly, the banking sector should urgently begin working on information security.