subgr_2.py

# Initialization
max_trials = 3
qi = QuantumInstance(Aer.get_backend('statevector_simulator'), seed_transpiler=seed, seed_simulator=seed, shots=1024)
rng = np.random.default_rng(seed=seed) # RNG
optim = SLSQP(maxiter=1000) # Init classical optimizer.

def trial():
  # Randomize initial parameters.
  initial_point = (rng.uniform(size=len(qc.parameters)) - 1/2) * np.pi
  
  vqe = VQE(qc, quantum_instance=qi, initial_point=initial_point, optimizer=optim) obj = vqe.compute_minimum_eigenvalue(observable(qc.num_qubits))
  return obj.optimal_value, obj
  
# Run a number of trials and select the one presenting # the least minimum eigenvalue.
results = [trial() for _ in tqdm(range(max_trials))] 
results = sorted(results, key=lambda obj: obj[0]) 
result = results[0][1]