def setUp(self) -> None: super().setUp() self.seed = 97 backend = BasicAer.get_backend('statevector_simulator') q_instance = QuantumInstance(backend, seed_simulator=self.seed, seed_transpiler=self.seed) self.sampler = CircuitSampler(q_instance, attach_results=True) self.expect = MatrixExpectation()
class TestCVaRExpectation(QiskitAquaTestCase): """Test the CVaR expectation object.""" def test_construction(self): """Test the correct operator expression is constructed.""" alpha = 0.5 base_expecation = PauliExpectation() cvar_expecation = CVaRExpectation(alpha=alpha, expectation=base_expecation) with self.subTest('single operator'): op = ~StateFn(Z) @ Plus expected = CVaRMeasurement(Z, alpha) @ Plus cvar = cvar_expecation.convert(op) self.assertEqual(cvar, expected) with self.subTest('list operator'): op = ~StateFn(ListOp([Z ^ Z, I ^ Z])) @ (Plus ^ Plus) expected = ListOp([ CVaRMeasurement((Z ^ Z), alpha) @ (Plus ^ Plus), CVaRMeasurement((I ^ Z), alpha) @ (Plus ^ Plus) ]) cvar = cvar_expecation.convert(op) self.assertEqual(cvar, expected) def test_unsupported_expectation(self): """Assert passing an AerPauliExpectation raises an error.""" expecation = AerPauliExpectation() with self.assertRaises(NotImplementedError): _ = CVaRExpectation(alpha=1, expectation=expecation) def test_unsupported_operations(self): """Assert unsupported operations raise an error.""" cvar = CVaRExpectation(0.2) with self.assertRaises(NotImplementedError): cvar.compute_variance(Z) @data(PauliExpectation(), MatrixExpectation()) def test_underlying_expectation(self, base_expecation): """Test the underlying expectation works correctly.""" cvar_expecation = CVaRExpectation(alpha=0.3, expectation=base_expecation) circuit = QuantumCircuit(2) circuit.z(0) circuit.cp(0.5, 0, 1) circuit.t(1) op = ~StateFn(CircuitOp(circuit)) @ (Plus ^ 2) cvar = cvar_expecation.convert(op) expected = base_expecation.convert(op) # test if the operators have been transformed in the same manner self.assertEqual(cvar.oplist[0].primitive, expected.oplist[0].primitive)
class TestVQE(QiskitAquaTestCase): """ Test VQE """ def setUp(self): super().setUp() self.seed = 50 aqua_globals.random_seed = self.seed self.h2_op = -1.052373245772859 * (I ^ I) \ + 0.39793742484318045 * (I ^ Z) \ - 0.39793742484318045 * (Z ^ I) \ - 0.01128010425623538 * (Z ^ Z) \ + 0.18093119978423156 * (X ^ X) self.h2_energy = -1.85727503 self.ryrz_wavefunction = TwoLocal(rotation_blocks=['ry', 'rz'], entanglement_blocks='cz') self.ry_wavefunction = TwoLocal(rotation_blocks='ry', entanglement_blocks='cz') self.qasm_simulator = QuantumInstance( BasicAer.get_backend('qasm_simulator'), shots=1024, seed_simulator=self.seed, seed_transpiler=self.seed) self.statevector_simulator = QuantumInstance( BasicAer.get_backend('statevector_simulator'), shots=1, seed_simulator=self.seed, seed_transpiler=self.seed) def test_basic_aer_statevector(self): """Test the VQE on BasicAer's statevector simulator.""" wavefunction = self.ryrz_wavefunction vqe = VQE(self.h2_op, wavefunction, L_BFGS_B()) result = vqe.run( QuantumInstance(BasicAer.get_backend('statevector_simulator'), basis_gates=['u1', 'u2', 'u3', 'cx', 'id'], coupling_map=[[0, 1]], seed_simulator=aqua_globals.random_seed, seed_transpiler=aqua_globals.random_seed)) with self.subTest(msg='test eigenvalue'): self.assertAlmostEqual(result.eigenvalue.real, self.h2_energy) with self.subTest(msg='test dimension of optimal point'): self.assertEqual(len(result.optimal_point), 16) with self.subTest(msg='assert cost_function_evals is set'): self.assertIsNotNone(result.cost_function_evals) with self.subTest(msg='assert optimizer_time is set'): self.assertIsNotNone(result.optimizer_time) def test_circuit_input(self): """Test running the VQE on a plain QuantumCircuit object.""" wavefunction = QuantumCircuit(2).compose(EfficientSU2(2)) optimizer = SLSQP(maxiter=50) vqe = VQE(self.h2_op, wavefunction, optimizer=optimizer) result = vqe.run(self.statevector_simulator) self.assertAlmostEqual(result.eigenvalue.real, self.h2_energy, places=5) @data( (MatrixExpectation(), 1), (AerPauliExpectation(), 1), (PauliExpectation(), 2), ) @unpack def test_construct_circuit(self, expectation, num_circuits): """Test construct circuits returns QuantumCircuits and the right number of them.""" wavefunction = EfficientSU2(2, reps=1) vqe = VQE(self.h2_op, wavefunction, expectation=expectation) params = [0] * wavefunction.num_parameters circuits = vqe.construct_circuit(params) self.assertEqual(len(circuits), num_circuits) for circuit in circuits: self.assertIsInstance(circuit, QuantumCircuit) def test_legacy_operator(self): """Test the VQE accepts and converts the legacy WeightedPauliOperator.""" pauli_dict = { 'paulis': [{ "coeff": { "imag": 0.0, "real": -1.052373245772859 }, "label": "II" }, { "coeff": { "imag": 0.0, "real": 0.39793742484318045 }, "label": "IZ" }, { "coeff": { "imag": 0.0, "real": -0.39793742484318045 }, "label": "ZI" }, { "coeff": { "imag": 0.0, "real": -0.01128010425623538 }, "label": "ZZ" }, { "coeff": { "imag": 0.0, "real": 0.18093119978423156 }, "label": "XX" }] } h2_op = WeightedPauliOperator.from_dict(pauli_dict) vqe = VQE(h2_op) self.assertEqual(vqe.operator, self.h2_op) def test_missing_varform_params(self): """Test specifying a variational form with no parameters raises an error.""" circuit = QuantumCircuit(self.h2_op.num_qubits) vqe = VQE(self.h2_op, circuit) with self.assertRaises(RuntimeError): vqe.run(BasicAer.get_backend('statevector_simulator')) @data( (SLSQP(maxiter=50), 5, 4), (SPSA(maxiter=150), 3, 2), # max_evals_grouped=n or =2 if n>2 ) @unpack def test_max_evals_grouped(self, optimizer, places, max_evals_grouped): """ VQE Optimizers test """ vqe = VQE(self.h2_op, self.ryrz_wavefunction, optimizer, max_evals_grouped=max_evals_grouped, quantum_instance=self.statevector_simulator) result = vqe.run() self.assertAlmostEqual(result.eigenvalue.real, self.h2_energy, places=places) def test_basic_aer_qasm(self): """Test the VQE on BasicAer's QASM simulator.""" optimizer = SPSA(maxiter=300, last_avg=5) wavefunction = self.ry_wavefunction vqe = VQE(self.h2_op, wavefunction, optimizer, max_evals_grouped=1) # TODO benchmark this later. result = vqe.run(self.qasm_simulator) self.assertAlmostEqual(result.eigenvalue.real, -1.86823, places=2) def test_with_aer_statevector(self): """Test VQE with Aer's statevector_simulator.""" try: # pylint: disable=import-outside-toplevel from qiskit import Aer except Exception as ex: # pylint: disable=broad-except self.skipTest( "Aer doesn't appear to be installed. Error: '{}'".format( str(ex))) return backend = Aer.get_backend('statevector_simulator') wavefunction = self.ry_wavefunction optimizer = L_BFGS_B() vqe = VQE(self.h2_op, wavefunction, optimizer, max_evals_grouped=1) quantum_instance = QuantumInstance( backend, seed_simulator=aqua_globals.random_seed, seed_transpiler=aqua_globals.random_seed) result = vqe.run(quantum_instance) self.assertAlmostEqual(result.eigenvalue.real, self.h2_energy, places=6) def test_with_aer_qasm(self): """Test VQE with Aer's qasm_simulator.""" try: # pylint: disable=import-outside-toplevel from qiskit import Aer except Exception as ex: # pylint: disable=broad-except self.skipTest( "Aer doesn't appear to be installed. Error: '{}'".format( str(ex))) return backend = Aer.get_backend('qasm_simulator') optimizer = SPSA(maxiter=200, last_avg=5) wavefunction = self.ry_wavefunction vqe = VQE(self.h2_op, wavefunction, optimizer, expectation=PauliExpectation()) quantum_instance = QuantumInstance( backend, seed_simulator=aqua_globals.random_seed, seed_transpiler=aqua_globals.random_seed) result = vqe.run(quantum_instance) self.assertAlmostEqual(result.eigenvalue.real, -1.86305, places=2) def test_with_aer_qasm_snapshot_mode(self): """Test the VQE using Aer's qasm_simulator snapshot mode.""" try: # pylint: disable=import-outside-toplevel from qiskit import Aer except Exception as ex: # pylint: disable=broad-except self.skipTest( "Aer doesn't appear to be installed. Error: '{}'".format( str(ex))) return backend = Aer.get_backend('qasm_simulator') optimizer = L_BFGS_B() wavefunction = self.ry_wavefunction vqe = VQE(self.h2_op, wavefunction, optimizer, expectation=AerPauliExpectation()) quantum_instance = QuantumInstance( backend, shots=1, seed_simulator=aqua_globals.random_seed, seed_transpiler=aqua_globals.random_seed) result = vqe.run(quantum_instance) self.assertAlmostEqual(result.eigenvalue.real, self.h2_energy, places=6) def test_callback(self): """Test the callback on VQE.""" history = {'eval_count': [], 'parameters': [], 'mean': [], 'std': []} def store_intermediate_result(eval_count, parameters, mean, std): history['eval_count'].append(eval_count) history['parameters'].append(parameters) history['mean'].append(mean) history['std'].append(std) optimizer = COBYLA(maxiter=3) wavefunction = self.ry_wavefunction vqe = VQE(self.h2_op, wavefunction, optimizer, callback=store_intermediate_result) vqe.run(self.qasm_simulator) self.assertTrue( all(isinstance(count, int) for count in history['eval_count'])) self.assertTrue( all(isinstance(mean, float) for mean in history['mean'])) self.assertTrue(all(isinstance(std, float) for std in history['std'])) for params in history['parameters']: self.assertTrue(all(isinstance(param, float) for param in params)) def test_reuse(self): """Test re-using a VQE algorithm instance.""" vqe = VQE() with self.subTest(msg='assert running empty raises AquaError'): with self.assertRaises(AquaError): _ = 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 AquaError'): with self.assertRaises(AquaError): _ = vqe.run() vqe.operator = self.h2_op with self.subTest(msg='assert missing backend raises AquaError'): with self.assertRaises(AquaError): _ = 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 test_vqe_optimizer(self): """ Test running same VQE twice to re-use optimizer, then switch optimizer """ vqe = VQE(self.h2_op, optimizer=SLSQP(), quantum_instance=QuantumInstance( BasicAer.get_backend('statevector_simulator'))) def run_check(): result = vqe.compute_minimum_eigenvalue() self.assertAlmostEqual(result.eigenvalue.real, -1.85727503, places=5) run_check() with self.subTest('Optimizer re-use'): run_check() with self.subTest('Optimizer replace'): vqe.optimizer = L_BFGS_B() run_check() def test_vqe_expectation_select(self): """Test expectation selection with Aer's qasm_simulator.""" try: # pylint: disable=import-outside-toplevel from qiskit import Aer except Exception as ex: # pylint: disable=broad-except self.skipTest( "Aer doesn't appear to be installed. Error: '{}'".format( str(ex))) return backend = Aer.get_backend('qasm_simulator') with self.subTest('Defaults'): vqe = VQE(self.h2_op, quantum_instance=backend) self.assertIsInstance(vqe.expectation, PauliExpectation) with self.subTest('Include custom'): vqe = VQE(self.h2_op, include_custom=True, quantum_instance=backend) self.assertIsInstance(vqe.expectation, AerPauliExpectation) with self.subTest('Set explicitly'): vqe = VQE(self.h2_op, expectation=AerPauliExpectation(), quantum_instance=backend) self.assertIsInstance(vqe.expectation, AerPauliExpectation) @unittest.skip(reason="IBMQ testing not available in general.") def test_ibmq(self): """ IBMQ VQE Test """ from qiskit import IBMQ provider = IBMQ.load_account() backend = provider.get_backend('ibmq_qasm_simulator') ansatz = TwoLocal(rotation_blocks=['ry', 'rz'], entanglement_blocks='cz') opt = SLSQP(maxiter=1) opt.set_max_evals_grouped(100) vqe = VQE(self.h2_op, ansatz, SLSQP(maxiter=2)) result = vqe.run(backend) print(result) self.assertAlmostEqual(result.eigenvalue.real, self.h2_energy) np.testing.assert_array_almost_equal(result.eigenvalue.real, self.h2_energy, 5) self.assertEqual(len(result.optimal_point), 16) self.assertIsNotNone(result.cost_function_evals) self.assertIsNotNone(result.optimizer_time) @data(MatrixExpectation(), None) def test_backend_change(self, user_expectation): """Test that VQE works when backend changes.""" vqe = VQE( operator=self.h2_op, var_form=TwoLocal(rotation_blocks=['ry', 'rz'], entanglement_blocks='cz'), optimizer=SLSQP(maxiter=2), expectation=user_expectation, quantum_instance=BasicAer.get_backend('statevector_simulator')) result0 = vqe.run() if user_expectation is not None: with self.subTest('User expectation kept.'): self.assertEqual(vqe.expectation, user_expectation) else: with self.subTest('Expectation created.'): self.assertIsInstance(vqe.expectation, ExpectationBase) try: vqe.set_backend(BasicAer.get_backend('qasm_simulator')) except Exception as ex: # pylint: disable=broad-except self.fail("Failed to change backend. Error: '{}'".format(str(ex))) return result1 = vqe.run() if user_expectation is not None: with self.subTest( 'Change backend with user expectation, it is kept.'): self.assertEqual(vqe.expectation, user_expectation) else: with self.subTest( 'Change backend without user expectation, one created.'): self.assertIsInstance(vqe.expectation, ExpectationBase) with self.subTest('Check results.'): self.assertEqual(len(result0.optimal_point), len(result1.optimal_point))
class TestMatrixExpectation(QiskitAquaTestCase): """Pauli Change of Basis Expectation tests.""" def setUp(self) -> None: super().setUp() self.seed = 97 backend = BasicAer.get_backend('statevector_simulator') q_instance = QuantumInstance(backend, seed_simulator=self.seed, seed_transpiler=self.seed) self.sampler = CircuitSampler(q_instance, attach_results=True) self.expect = MatrixExpectation() def test_pauli_expect_pair(self): """ pauli expect pair test """ op = (Z ^ Z) # wf = (Pl^Pl) + (Ze^Ze) wf = CX @ (H ^ I) @ Zero converted_meas = self.expect.convert(~StateFn(op) @ wf) self.assertAlmostEqual(converted_meas.eval(), 0, delta=.1) sampled = self.sampler.convert(converted_meas) self.assertAlmostEqual(sampled.eval(), 0, delta=.1) def test_pauli_expect_single(self): """ pauli expect single test """ paulis = [Z, X, Y, I] states = [Zero, One, Plus, Minus, S @ Plus, S @ Minus] for pauli, state in itertools.product(paulis, states): converted_meas = self.expect.convert(~StateFn(pauli) @ state) matmulmean = state.adjoint().to_matrix() @ pauli.to_matrix() @ state.to_matrix() self.assertAlmostEqual(converted_meas.eval(), matmulmean, delta=.1) sampled = self.sampler.convert(converted_meas) self.assertAlmostEqual(sampled.eval(), matmulmean, delta=.1) def test_pauli_expect_op_vector(self): """ pauli expect op vector test """ paulis_op = ListOp([X, Y, Z, I]) converted_meas = self.expect.convert(~StateFn(paulis_op)) plus_mean = (converted_meas @ Plus) np.testing.assert_array_almost_equal(plus_mean.eval(), [1, 0, 0, 1], decimal=1) sampled_plus = self.sampler.convert(plus_mean) np.testing.assert_array_almost_equal(sampled_plus.eval(), [1, 0, 0, 1], decimal=1) minus_mean = (converted_meas @ Minus) np.testing.assert_array_almost_equal(minus_mean.eval(), [-1, 0, 0, 1], decimal=1) sampled_minus = self.sampler.convert(minus_mean) np.testing.assert_array_almost_equal(sampled_minus.eval(), [-1, 0, 0, 1], decimal=1) zero_mean = (converted_meas @ Zero) np.testing.assert_array_almost_equal(zero_mean.eval(), [0, 0, 1, 1], decimal=1) sampled_zero = self.sampler.convert(zero_mean) np.testing.assert_array_almost_equal(sampled_zero.eval(), [0, 0, 1, 1], decimal=1) sum_zero = (Plus + Minus) * (.5 ** .5) sum_zero_mean = (converted_meas @ sum_zero) np.testing.assert_array_almost_equal(sum_zero_mean.eval(), [0, 0, 1, 1], decimal=1) sampled_zero = self.sampler.convert(sum_zero) np.testing.assert_array_almost_equal((converted_meas @ sampled_zero).eval(), [0, 0, 1, 1], decimal=1) for i, op in enumerate(paulis_op.oplist): mat_op = op.to_matrix() np.testing.assert_array_almost_equal(zero_mean.eval()[i], Zero.adjoint().to_matrix() @ mat_op @ Zero.to_matrix(), decimal=1) np.testing.assert_array_almost_equal(plus_mean.eval()[i], Plus.adjoint().to_matrix() @ mat_op @ Plus.to_matrix(), decimal=1) np.testing.assert_array_almost_equal(minus_mean.eval()[i], Minus.adjoint().to_matrix() @ mat_op @ Minus.to_matrix(), decimal=1) def test_pauli_expect_state_vector(self): """ pauli expect state vector test """ states_op = ListOp([One, Zero, Plus, Minus]) paulis_op = X converted_meas = self.expect.convert(~StateFn(paulis_op) @ states_op) np.testing.assert_array_almost_equal(converted_meas.eval(), [0, 0, 1, -1], decimal=1) sampled = self.sampler.convert(converted_meas) np.testing.assert_array_almost_equal(sampled.eval(), [0, 0, 1, -1], decimal=1) # Small test to see if execution results are accessible for composed_op in sampled: self.assertIn('statevector', composed_op[1].execution_results) def test_pauli_expect_op_vector_state_vector(self): """ pauli expect op vector state vector test """ paulis_op = ListOp([X, Y, Z, I]) states_op = ListOp([One, Zero, Plus, Minus]) valids = [[+0, 0, 1, -1], [+0, 0, 0, 0], [-1, 1, 0, -0], [+1, 1, 1, 1]] converted_meas = self.expect.convert(~StateFn(paulis_op)) np.testing.assert_array_almost_equal((converted_meas @ states_op).eval(), valids, decimal=1) sampled = self.sampler.convert(states_op) np.testing.assert_array_almost_equal((converted_meas @ sampled).eval(), valids, decimal=1) def test_multi_representation_ops(self): """ Test observables with mixed representations """ mixed_ops = ListOp([X.to_matrix_op(), H, H + I, X]) converted_meas = self.expect.convert(~StateFn(mixed_ops)) plus_mean = (converted_meas @ Plus) sampled_plus = self.sampler.convert(plus_mean) np.testing.assert_array_almost_equal(sampled_plus.eval(), [1, .5**.5, (1 + .5**.5), 1], decimal=1)
def setUp(self) -> None: super().setUp() backend = BasicAer.get_backend('statevector_simulator') self.sampler = CircuitSampler(backend, attach_results=True) self.expect = MatrixExpectation()