code

# Define a simple graph
G = nx.Graph()
G.add_edges_from([(0, 1), (1, 2), (2, 3), (3, 0), (0, 2)])

# Define the optimization problem
qp = QuadraticProgram()
for i in range(len(G.nodes)):
    qp.binary_var(f'x{i}')

# Define the objective function
linear = np.zeros(len(G.nodes))
quadratic = np.zeros((len(G.nodes), len(G.nodes)))

for i, j in G.edges:
    quadratic[i, j] = 1  # Penalizing edges within the same set

qp.minimize(quadratic=quadratic)

print(qp)
] # If diffuser is a gate, create a QuantumCircuit and add it
diffuser_circuit = QuantumCircuit(2)
diffuser_circuit.append(diffuser(2), range(2))

# Print circuit diagrams
print(oracle.draw())  # Print Oracle Circuit
print(diffuser_circuit.draw())  # Print Diffuser Circuit
print(grover.draw())  # Print Full Grover Circuit

10] print(oracle)
print(diffuser(2))
11] print(grover.draw())

] # Initialize quantum circuit
qc = QuantumCircuit(2)
qc.h([0, 1])
qc.measure_all()  # Measure all qubits

# Set up the Aer simulator
simulator = Aer.get_backend('qasm_simulator')
Transpile and run the circuit
compiled_circuit = transpile(qc, simulator)
result = simulator.run(compiled_circuit).result()

# Get and plot the results
counts = result.get_counts()
plot_histogram(counts)

14] qc.draw('mpl')