Native circuit library

.pylintdict

@@ -8,6 +8,7 @@ ae
 aer
 Aer's
 aerjob
+Äquivalenzverbot
 aij
 al
 algo
@@ -688,6 +689,7 @@ trotterized
 trotterizing
 trunc
 ub
+Über
 ucc
 uccd
 UCCS
@@ -753,8 +755,6 @@ zmatrix
 zv
 ZZ
 zzz
-äguivalenzverbot
-über
 ucc
 uccd
 UCCS
@@ -763,5 +763,6 @@ versioning
 vir
 Watrous's
 wf
+vir
 Ze
-vir
+Zoufal

CHANGELOG.md

@@ -105,6 +105,8 @@ Removed
     -   arithmetic circuits in qiskit/aqua/circuits (#895)
     -   boolean logic gates (#896)
     -   quantum Fourier transformation (#909)
+    -   the RY, RYRZ and SwapRZ variational forms (#920)
+    -   the PauliExpansion, First- and SecondOrderExpansion feature maps  (#920)
 
 Fixed
 -----

README.md

@@ -219,8 +219,8 @@ from qiskit.chemistry.components.initial_states import HartreeFock
 init_state = HartreeFock(num_qubits, num_spin_orbitals, num_particles)
 
 # setup the variational form for VQE
-from qiskit.aqua.components.variational_forms import RYRZ
-var_form = RYRZ(num_qubits, initial_state=init_state)
+from qiskit.circuit.library import TwoLocal
+var_form = TwoLocal(num_qubits, ['ry', 'rz'], 'cz', initial_state=init_state)
 
 # setup and run VQE
 from qiskit.aqua.algorithms import VQE

qiskit/aqua/algorithms/classifiers/qsvm/qsvm.py

@@ -15,6 +15,7 @@
 """The Quantum SVM algorithm."""
 
 from typing import Dict, Optional, Union
+import warnings
 import logging
 import sys
 
@@ -29,7 +30,7 @@
 from qiskit.aqua import AquaError
 from qiskit.aqua.utils.dataset_helper import get_num_classes
 from qiskit.aqua.utils import split_dataset_to_data_and_labels
-from qiskit.aqua.components.feature_maps import FeatureMap
+from qiskit.aqua.components.feature_maps import FeatureMap, RawFeatureVector
 from qiskit.aqua.components.multiclass_extensions import MulticlassExtension
 from ._qsvm_estimator import _QSVM_Estimator
 from ._qsvm_binary import _QSVM_Binary
@@ -129,6 +130,14 @@ def __init__(self, feature_map: Union[QuantumCircuit, FeatureMap],
             self.feature_map_params_x = ParameterVector('x', self.feature_map.feature_dimension)
             self.feature_map_params_y = ParameterVector('y', self.feature_map.feature_dimension)
         else:
+            if not isinstance(feature_map, RawFeatureVector):
+                warnings.warn("""
+                The {} object as input for the QSVM is deprecated as of 0.7.0 and will
+                be removed no earlier than 3 months after the release.
+                You should pass a QuantumCircuit object instead.
+                See also qiskit.circuit.library.data_preparation for a collection
+                of suitable circuits.""".format(type(feature_map)),
+                              DeprecationWarning, stacklevel=2)
             self.feature_map_params_x = ParameterVector('x', feature_map.feature_dimension)
             self.feature_map_params_y = ParameterVector('y', feature_map.feature_dimension)
 

qiskit/aqua/algorithms/classifiers/vqc.py

@@ -30,7 +30,7 @@
 from qiskit.aqua.utils import split_dataset_to_data_and_labels
 from qiskit.aqua.algorithms import VQAlgorithm
 from qiskit.aqua.components.optimizers import Optimizer
-from qiskit.aqua.components.feature_maps import FeatureMap
+from qiskit.aqua.components.feature_maps import FeatureMap, RawFeatureVector
 from qiskit.aqua.components.variational_forms import VariationalForm
 
 logger = logging.getLogger(__name__)
@@ -85,12 +85,12 @@ def __init__(
         # VariationalForm is not deprecated on level of the VQAlgorithm yet as UCCSD still
         # derives from there, therefore we're adding a warning here
         if isinstance(var_form, VariationalForm):
-            warnings.warn('The qiskit.aqua.components.variational_form.VariationalForm object as '
-                          'input for the VQC is deprecated as of 0.7.0 and will be removed no '
-                          'earlier than 3 months after the release. You should pass a '
-                          'QuantumCircuit object instead. '
-                          'See also qiskit.circuit.library.n_local for a collection of '
-                          'suitable circuits.',
+            warnings.warn("""
+            The {} object as input for the VQC is deprecated as of 0.7.0 and will
+            be removed no earlier than 3 months after the release.
+            You should pass a QuantumCircuit object instead.
+            See also qiskit.circuit.library.n_local for a collection
+            of suitable circuits.""".format(type(feature_map)),
                           DeprecationWarning, stacklevel=2)
 
         super().__init__(
@@ -532,12 +532,14 @@ def feature_map(self, feature_map: Union[FeatureMap, QuantumCircuit]):
             self._feature_map_params = list(feature_map.parameters)
             self._feature_map = feature_map
         elif isinstance(feature_map, FeatureMap):
-            warnings.warn('The qiskit.aqua.components.feature_maps.FeatureMap object is deprecated '
-                          'as of 0.7.0 and will be removed no earlier than 3 months after the '
-                          'release. You should pass a QuantumCircuit object instead. '
-                          'See also qiskit.circuit.library.data_preparation for a collection of '
-                          'suitable circuits.',
-                          DeprecationWarning, stacklevel=2)
+            # raw feature vector is not yet replaced
+            if not isinstance(feature_map, RawFeatureVector):
+                warnings.warn('The qiskit.aqua.components.feature_maps.FeatureMap object is '
+                              'deprecated as of 0.7.0 and will be removed no earlier than 3 months '
+                              'after the release. You should pass a QuantumCircuit object instead. '
+                              'See also qiskit.circuit.library.data_preparation for a collection '
+                              'of suitable circuits.',
+                              DeprecationWarning, stacklevel=2)
 
             self._num_qubits = feature_map.num_qubits
             self._feature_map_params = ParameterVector('x', length=feature_map.feature_dimension)

qiskit/aqua/algorithms/distribution_learners/qgan.py

@@ -12,11 +12,7 @@
 # copyright notice, and modified files need to carry a notice indicating
 # that they have been altered from the originals.
 
-"""
-Quantum Generative Adversarial Network.
-`Quantum Generative Adversarial Networks for learning and loading random distributions
-    <https://www.nature.com/articles/s41534-019-0223-2>`_
-"""
+"""Quantum Generative Adversarial Network."""
 
 from typing import Optional, Union
 import csv
@@ -26,13 +22,17 @@
 import numpy as np
 from scipy.stats import entropy
 
+from qiskit.circuit import QuantumCircuit
 from qiskit.providers import BaseBackend
 from qiskit.aqua import QuantumInstance, AquaError, aqua_globals
 from qiskit.aqua.algorithms import QuantumAlgorithm
 from qiskit.aqua.components.neural_networks.discriminative_network import DiscriminativeNetwork
 from qiskit.aqua.components.neural_networks.generative_network import GenerativeNetwork
 from qiskit.aqua.components.neural_networks.quantum_generator import QuantumGenerator
 from qiskit.aqua.components.neural_networks.numpy_discriminator import NumPyDiscriminator
+from qiskit.aqua.components.optimizers import Optimizer
+from qiskit.aqua.components.uncertainty_models import UnivariateVariationalDistribution
+from qiskit.aqua.components.uncertainty_models import MultivariateVariationalDistribution
 from qiskit.aqua.utils.dataset_helper import discretize_and_truncate
 from qiskit.aqua.utils.validation import validate_min
 
@@ -42,11 +42,9 @@
 
 
 class QGAN(QuantumAlgorithm):
-    """
-    The Quantum Generative Adversarial Network algorithm.
+    """The Quantum Generative Adversarial Network algorithm.
 
-    `qGAN <https://arxiv.org/abs/1904.00043>`__ is a hybrid quantum-classical algorithm used
-    for generative modeling tasks.
+    The qGAN [1] is a hybrid quantum-classical algorithm used for generative modeling tasks.
 
     This adaptive algorithm uses the interplay of a generative
     :class:`~qiskit.aqua.components.neural_networks.GenerativeNetwork` and a
@@ -59,6 +57,12 @@ class QGAN(QuantumAlgorithm):
     samples. Eventually, the quantum generator learns the training data's underlying probability
     distribution. The trained quantum generator loads a quantum state which is a model of the
     target distribution.
+
+    **References:**
+
+    [1] Zoufal et al.,
+        `Quantum Generative Adversarial Networks for learning and loading random distributions
+        <https://www.nature.com/articles/s41534-019-0223-2>`_
     """
 
     def __init__(self, data: np.ndarray, bounds: Optional[np.ndarray] = None,
@@ -188,16 +192,19 @@ def generator(self):
         return self._generator
 
     # pylint: disable=unused-argument
-    def set_generator(self, generator_circuit=None,
-                      generator_init_params=None, generator_optimizer=None):
-        """
-        Initialize generator.
+    def set_generator(self, generator_circuit: Optional[Union[QuantumCircuit,
+                                                              UnivariateVariationalDistribution,
+                                                              MultivariateVariationalDistribution]
+                                                        ] = None,
+                      generator_init_params: Optional[np.ndarray] = None,
+                      generator_optimizer: Optional[Optimizer] = None):
+        """Initialize generator.
 
         Args:
-            generator_circuit (VariationalForm): parameterized quantum circuit which sets
+            generator_circuit: parameterized quantum circuit which sets
                 the structure of the quantum generator
-            generator_init_params(numpy.ndarray): initial parameters for the generator circuit
-            generator_optimizer (Optimizer): optimizer to be used for the training of the generator
+            generator_init_params: initial parameters for the generator circuit
+            generator_optimizer: optimizer to be used for the training of the generator
         """
         self._generator = QuantumGenerator(self._bounds, self._num_qubits,
                                            generator_circuit, generator_init_params,

qiskit/aqua/algorithms/minimum_eigen_solvers/vqe.py

@@ -25,13 +25,14 @@
 
 from qiskit import ClassicalRegister, QuantumCircuit
 from qiskit.circuit import Parameter
+from qiskit.circuit.library import RealAmplitudes
 from qiskit.providers import BaseBackend
 from qiskit.aqua import QuantumInstance, AquaError
 from qiskit.aqua.algorithms import QuantumAlgorithm
 from qiskit.aqua.operators import (OperatorBase, ExpectationBase, ExpectationFactory, StateFn,
                                    CircuitStateFn, LegacyBaseOperator, ListOp, I, CircuitSampler)
 from qiskit.aqua.components.optimizers import Optimizer, SLSQP
-from qiskit.aqua.components.variational_forms import VariationalForm, RY
+from qiskit.aqua.components.variational_forms import VariationalForm
 from qiskit.aqua.utils.validation import validate_min
 from ..vq_algorithm import VQAlgorithm, VQResult
 from .minimum_eigen_solver import MinimumEigensolver, MinimumEigensolverResult
@@ -117,10 +118,8 @@ def __init__(self,
         """
         validate_min('max_evals_grouped', max_evals_grouped, 1)
         if var_form is None:
-            # TODO after ansatz refactor num qubits can be set later so we do not have to have
-            #      an operator to create a default
             if operator is not None:
-                var_form = RY(operator.num_qubits)
+                var_form = RealAmplitudes()
 
         if optimizer is None:
             optimizer = SLSQP()
@@ -227,6 +226,7 @@ def _check_operator_varform(self):
                 # try to set the number of qubits on the variational form, if possible
                 try:
                     self.var_form.num_qubits = self.operator.num_qubits
+                    self._var_form_params = list(self.var_form.parameters)
                 except AttributeError:
                     raise AquaError("The number of qubits of the variational form does not match "
                                     "the operator, and the variational form does not allow setting "
@@ -311,8 +311,7 @@ def construct_circuit(self,
         if not self.expectation:
             self._try_set_expectation_value_from_factory()
 
-        observable_meas = self.expectation.convert(StateFn(self.operator,
-                                                           is_measurement=True))
+        observable_meas = self.expectation.convert(StateFn(self.operator, is_measurement=True))
         ansatz_circuit_op = CircuitStateFn(wave_function)
         return observable_meas.compose(ansatz_circuit_op).reduce()
 
@@ -331,6 +330,8 @@ def _run(self) -> 'VQEResult':
         if self.operator is None:
             raise AquaError("The operator was never provided.")
 
+        self._check_operator_varform()
+
         self._quantum_instance.circuit_summary = True
 
         self._eval_count = 0
@@ -345,6 +346,7 @@ def _run(self) -> 'VQEResult':
         self._ret['min_val'] = vqresult.optimal_value
         self._ret['opt_params'] = vqresult.optimal_point
         self._ret['eval_time'] = vqresult.optimizer_time
+        self._ret['opt_params_dict'] = vqresult.optimal_parameters
 
         if self._ret['num_optimizer_evals'] is not None and \
                 self._eval_count >= self._ret['num_optimizer_evals']:
@@ -396,7 +398,6 @@ def compute_minimum_eigenvalue(
         super().compute_minimum_eigenvalue(operator, aux_operators)
         return self._run()
 
-    # This is the objective function to be passed to the optimizer that is used for evaluation
     def _energy_evaluation(self, parameters: Union[List[float], np.ndarray]
                            ) -> Union[float, List[float]]:
         """Evaluate energy at given parameters for the variational form.
@@ -408,11 +409,18 @@ def _energy_evaluation(self, parameters: Union[List[float], np.ndarray]
 
         Returns:
             Energy of the hamiltonian of each parameter.
+
+
+        Raises:
+            RuntimeError: If the variational form has no parameters.
         """
         if not self._expect_op:
             self._expect_op = self.construct_circuit(self._var_form_params)
 
         num_parameters = self.var_form.num_parameters
+        if self._var_form.num_parameters == 0:
+            raise RuntimeError('The var_form cannot have 0 parameters.')
+
         parameter_sets = np.reshape(parameters, (-1, num_parameters))
         # Create dict associating each parameter with the lists of parameterization values for it
         param_bindings = dict(zip(self._var_form_params, parameter_sets.transpose().tolist()))
@@ -448,10 +456,9 @@ def get_optimal_circuit(self) -> QuantumCircuit:
         if 'opt_params' not in self._ret:
             raise AquaError("Cannot find optimal circuit before running the "
                             "algorithm to find optimal params.")
-        if isinstance(self.var_form, QuantumCircuit):
-            param_dict = dict(zip(self._var_form_params, self._ret['opt_params']))
-            return self.var_form.assign_parameters(param_dict)
-        return self._var_form.construct_circuit(self._ret['opt_params'])
+        if isinstance(self.var_form, VariationalForm):
+            return self._var_form.construct_circuit(self._ret['opt_params'])
+        return self.var_form.assign_parameters(self._ret['opt_params_dict'])
 
     def get_optimal_vector(self) -> Union[List[float], Dict[str, int]]:
         """Get the simulation outcome of the optimal circuit. """

qiskit/aqua/algorithms/vq_algorithm.py

@@ -31,7 +31,7 @@
 
 from qiskit.circuit import QuantumCircuit, ParameterVector
 from qiskit.providers import BaseBackend
-from qiskit.aqua import QuantumInstance, AquaError
+from qiskit.aqua import QuantumInstance
 from qiskit.aqua.algorithms import AlgorithmResult, QuantumAlgorithm
 from qiskit.aqua.components.optimizers import Optimizer, SLSQP
 from qiskit.aqua.components.variational_forms import VariationalForm
@@ -73,6 +73,7 @@ def __init__(self,
         self._cost_fn = cost_fn
         self._initial_point = initial_point
         self._var_form = var_form
+        self._var_form_params = None
         if var_form is not None:
             self.var_form = var_form
 
@@ -97,12 +98,7 @@ def var_form(self, var_form: Optional[Union[QuantumCircuit, VariationalForm]]):
             self._var_form_params = None
             self._var_form = var_form
         else:
-            raise ValueError(
-                "Unsupported type '{}' of var_form".format(
-                    type(var_form) if var_form else var_form))
-
-        if var_form is not None and len(self._var_form_params) == 0:
-            raise AquaError('Passing a variational form with no parameters is not supported.')
+            raise ValueError('Unsupported type "{}" of var_form'.format(type(var_form)))
 
     @property
     def optimizer(self) -> Optional[Optimizer]:
@@ -164,7 +160,7 @@ def find_minimum(self,
 
         nparms = var_form.num_parameters
 
-        if hasattr(var_form, 'parameter_bounds'):
+        if hasattr(var_form, 'parameter_bounds') and var_form.parameter_bounds is not None:
             bounds = var_form.parameter_bounds
         else:
             bounds = [(None, None)] * nparms
@@ -210,6 +206,7 @@ def find_minimum(self,
         result.optimizer_time = eval_time
         result.optimal_value = opt_val
         result.optimal_point = opt_params
+        result.optimal_parameters = dict(zip(self._var_form_params, opt_params))
 
         return result
 
@@ -311,6 +308,16 @@ def optimal_point(self, value: np.ndarray) -> None:
         """ Sets optimal point """
         self.data['optimal_point'] = value
 
+    @property
+    def optimal_parameters(self) -> dict:
+        """ Returns the optimal parameters in a dictionary """
+        return self.get('optimal_parameters')
+
+    @optimal_parameters.setter
+    def optimal_parameters(self, value: dict) -> None:
+        """ Sets optimal parameters """
+        self.data['optimal_parameters'] = value
+
     def __getitem__(self, key: object) -> object:
         if key == 'num_optimizer_evals':
             warnings.warn('num_optimizer_evals deprecated, use optimizer_evals property.',