quantum_opimization_demo.py

# QuantumOptimizationDemo.py
# A conceptual demonstration of using quantum computing for optimization problems.
# Note: This script is purely educational and requires a quantum computing simulation environment or API.

from qiskit import Aer, execute, QuantumCircuit
from qiskit.aqua.algorithms import VQE, NumPyMinimumEigensolver
from qiskit.aqua.components.optimizers import SPSA
from qiskit.circuit.library import TwoLocal
from qiskit.optimization import QuadraticProgram
from qiskit.optimization.algorithms import MinimumEigenOptimizer

def solve_optimization_problem():
    """
    Solves an example optimization problem using a quantum algorithm.
    Demonstrates the potential of quantum computing for solving complex problems.
    """

    # Define the problem: Maximize f(x) = -(x-1)^2
    qp = QuadraticProgram()
    qp.binary_var(name="x")
    qp.maximize(linear=[-2], quadratic=[[1]])

    # Use the Variational Quantum Eigensolver (VQE) algorithm
    # with a simple ansatz circuit
    optimizer = SPSA(maxiter=100)
    ansatz = TwoLocal(rotation_blocks='ry', entanglement_blocks='cz')
    vqe = VQE(ansatz=ansatz, optimizer=optimizer, quantum_instance=Aer.get_backend('qasm_simulator'))

    # Solve the problem
    solver = MinimumEigenOptimizer(vqe)
    result = solver.solve(qp)

    print(f"Solution: x = {result.x[0]}, f(x) = {result.fval}")

if __name__ == "__main__":
    solve_optimization_problem()