import numpy as np
import networkx as nx
from qiskit import Aer
from qiskit.algorithms import QAOA, NumPyMinimumEigensolver
from qiskit.algorithms.optimizers import COBYLA
from qiskit.utils import QuantumInstance
from qiskit_optimization import QuadraticProgram
from qiskit_optimization.algorithms import MinimumEigenOptimizer

num_time_slots = 24

# Define the QUBO problem
qubo = QuadraticProgram()

# Add binary variables for charging (c) and discharging (d) states
for t in range(num_time_slots):
    qubo.binary_var(f'c_{t}')
    qubo.binary_var(f'd_{t}')

# Define the objective function
# (In practice, you need to calculate Jij and hi based on the solar farm data)
Jij = np.random.rand(num_time_slots, num_time_slots) * 0.5
hi_c = -1 + np.random.rand(num_time_slots)
hi_d = 1 - np.random.rand(num_time_slots)

# Set linear and quadratic terms of the objective function
linear_terms = {}
quadratic_terms = {}
for t in range(num_time_slots):
    linear_terms[f'c_{t}'] = hi_c[t]
    linear_terms[f'd_{t}'] = hi_d[t]
    for s in range(num_time_slots):
        if t != s:
            quadratic_terms[(f'c_{t}', f'c_{s}')] = Jij[t, s]
            quadratic_terms[(f'd_{t}', f'd_{s}')] = Jij[t, s]

qubo.minimize(linear=linear_terms, quadratic=quadratic_terms)

# Set up the quantum instance
backend = Aer.get_backend('qasm_simulator')
quantum_instance = QuantumInstance(backend, seed_simulator=42, seed_transpiler=42, shots=10000)

# Set up the QAOA algorithm and optimizer
optimizer = COBYLA(maxiter=500)
qaoa = QAOA(optimizer=optimizer, reps=5, quantum_instance=quantum_instance)

# Set up the minimum eigen optimizer
min_eig_optimizer = MinimumEigenOptimizer(qaoa)

# Solve the problem
result = min_eig_optimizer.solve(qubo)
print("QAOA result:", result)

# Solve the problem using a classical solver (NumPyMinimumEigensolver)
exact_solver = MinimumEigenOptimizer(NumPyMinimumEigensolver())
exact_result = exact_solver.solve(qubo)
print("Classical result:", exact_result)

