def test_reuse(self):
"""Test re-using a VQE algorithm instance."""
vqe = VQE()
with self.subTest(msg='assert running empty raises AlgorithmError'):
with self.assertRaises(AlgorithmError):
_ = vqe.run()
var_form = TwoLocal(rotation_blocks=['ry', 'rz'],
entanglement_blocks='cz')
vqe.var_form = var_form
with self.subTest(msg='assert missing operator raises AlgorithmError'):
with self.assertRaises(AlgorithmError):
_ = vqe.run()
vqe.operator = self.h2_op
with self.subTest(msg='assert missing backend raises AlgorithmError'):
with self.assertRaises(AlgorithmError):
_ = vqe.run()
vqe.quantum_instance = self.statevector_simulator
with self.subTest(msg='assert VQE works once all info is available'):
result = vqe.run()
self.assertAlmostEqual(result.eigenvalue.real,
self.h2_energy,
places=5)
operator = PrimitiveOp(
np.array([[1, 0, 0, 0], [0, -1, 0, 0], [0, 0, 2, 0], [0, 0, 0,
3]]))
with self.subTest(msg='assert minimum eigensolver interface works'):
result = vqe.compute_minimum_eigenvalue(operator)
self.assertAlmostEqual(result.eigenvalue.real, -1.0, places=5)
def _compute_gradients(self,
excitation_pool: List[PauliSumOp],
theta: List[float],
vqe: VQE,
) -> List[Tuple[float, PauliSumOp]]:
"""
Computes the gradients for all available excitation operators.
Args:
excitation_pool: pool of excitation operators
theta: list of (up to now) optimal parameters
vqe: the variational quantum eigensolver instance used for solving
Returns:
List of pairs consisting of gradient and excitation operator.
"""
res = []
# compute gradients for all excitation in operator pool
for exc in excitation_pool:
# push next excitation to variational form
vqe.var_form.push_hopping_operator(exc)
# NOTE: because we overwrite the var_form inside of the VQE, we need to update the VQE's
# internal _var_form_params, too. We can do this by triggering the var_form setter. Once
# the VQE does not store this pure var_form property any longer this can be removed.
vqe.var_form = vqe.var_form
# We also need to invalidate the internally stored expectation operator because it needs
# to be updated for the new var_form.
vqe._expect_op = None
# evaluate energies
parameter_sets = theta + [-self._delta] + theta + [self._delta]
energy_results = vqe._energy_evaluation(np.asarray(parameter_sets))
# compute gradient
gradient = (energy_results[0] - energy_results[1]) / (2 * self._delta)
res.append((np.abs(gradient), exc))
# pop excitation from variational form
vqe.var_form.pop_hopping_operator()
return res
