Merge pull request #1422 from manoelmarques/stable-0.8.1

[Stable] Qiskit Aqua Release 0.8.1

Author: manoelmarques

.github/actions/install-libraries/action.yml

@@ -26,5 +26,4 @@ runs:
           pip install -U pip
           pip install -U setuptools wheel
         fi
-        # sudo add-apt-repository -y ppa:ubuntu-toolchain-r/test
       shell: bash

.github/actions/install-master-dependencies/action.yml

@@ -24,7 +24,6 @@ runs:
         pip install https://github.com/Qiskit/qiskit-terra/archive/master.zip
         pip install https://github.com/Qiskit/qiskit-ignis/archive/master.zip
         pip install https://github.com/Qiskit/qiskit-ibmq-provider/archive/master.zip
-        # sudo apt-get -y update
         sudo apt-get -y install g++-7
         sudo apt-get -y install libopenblas-dev
         git clone https://github.com/Qiskit/qiskit-aer.git /tmp/qiskit-aer

.github/workflows/main.yml

@@ -250,8 +250,9 @@ jobs:
           if [ -f "$HOME/miniconda/etc/profile.d/conda.sh" ]; then
             source "$HOME/miniconda/etc/profile.d/conda.sh"
             conda activate
+            # PSI4 has problems with dataclasses
+            pip uninstall -y dataclasses
           fi
-          # sudo apt-get -y update
           sudo apt-get -y install libgfortran5
           pip install https://github.com/rpmuller/pyquante2/archive/master.zip
           pip install pyscf
@@ -461,7 +462,6 @@ jobs:
         shell: bash
       - name: Install Dependencies
         run: |
-          # sudo apt-get -y update
           sudo apt-get -y install libgfortran5
           pip install https://github.com/rpmuller/pyquante2/archive/master.zip
           pip install pyscf

qiskit/aqua/VERSION.txt

@@ -1 +1 @@
-0.8.0
+0.8.1

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

@@ -76,9 +76,10 @@ def train(self, data, labels):
         """
         scaling = 1.0 if self._qalgo.quantum_instance.is_statevector else None
         kernel_matrix = self._qalgo.construct_kernel_matrix(data)
+        lambda2 = self._qalgo.lambda2
         labels = labels * 2 - 1  # map label from 0 --> -1 and 1 --> 1
         labels = labels.astype(np.float)
-        [alpha, b, support] = optimize_svm(kernel_matrix, labels, scaling=scaling)
+        [alpha, b, support] = optimize_svm(kernel_matrix, labels, scaling=scaling, lambda2=lambda2)
         support_index = np.where(support)
         alphas = alpha[support_index]
         svms = data[support_index]

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

@@ -80,6 +80,7 @@ def __init__(self, feature_map: Union[QuantumCircuit, FeatureMap],
                  test_dataset: Optional[Dict[str, np.ndarray]] = None,
                  datapoints: Optional[np.ndarray] = None,
                  multiclass_extension: Optional[MulticlassExtension] = None,
+                 lambda2: float = 0.001,
                  quantum_instance: Optional[
                      Union[QuantumInstance, BaseBackend, Backend]] = None) -> None:
         """
@@ -90,6 +91,7 @@ def __init__(self, feature_map: Union[QuantumCircuit, FeatureMap],
             datapoints: Prediction dataset.
             multiclass_extension: If number of classes is greater than 2 then a multiclass scheme
                 must be supplied, in the form of a multiclass extension.
+            lambda2: L2 norm regularization factor
             quantum_instance: Quantum Instance or Backend
 
         Raises:
@@ -118,6 +120,7 @@ def __init__(self, feature_map: Union[QuantumCircuit, FeatureMap],
         self.setup_training_data(training_dataset)
         self.setup_test_data(test_dataset)
         self.setup_datapoint(datapoints)
+        self.lambda2 = lambda2
 
         self.feature_map = feature_map
         self.num_qubits = self.feature_map.num_qubits

qiskit/aqua/algorithms/minimum_eigen_solvers/qaoa/var_form.py

@@ -16,7 +16,7 @@
 
 import numpy as np
 
-from qiskit.aqua.operators import (OperatorBase, X, I, H, Zero, CircuitStateFn,
+from qiskit.aqua.operators import (OperatorBase, X, I, H, CircuitStateFn,
                                    EvolutionFactory, LegacyBaseOperator)
 from qiskit.aqua.components.variational_forms import VariationalForm
 from qiskit.aqua.components.initial_states import InitialState
@@ -80,13 +80,12 @@ def construct_circuit(self, parameters, q=None):
                 self.num_parameters, len(parameters)
             ))
 
-        circuit = (H ^ self._num_qubits)
         # initialize circuit, possibly based on given register/initial state
         if self._initial_state is not None:
-            init_state = CircuitStateFn(self._initial_state.construct_circuit('circuit'))
+            stateVector = CircuitStateFn(self._initial_state.construct_circuit('circuit'))
+            circuit = stateVector.to_circuit_op()
         else:
-            init_state = Zero
-        circuit = circuit.compose(init_state)
+            circuit = (H ^ self._num_qubits)
 
         for idx in range(self._p):
             circuit = (self._cost_operator * parameters[idx]).exp_i().compose(circuit)

qiskit/aqua/algorithms/quantum_algorithm.py

@@ -60,8 +60,8 @@ def run(self,
             AquaError: If a quantum instance or backend has not been provided
         """
         if quantum_instance is None and self.quantum_instance is None:
-            raise AquaError("Quantum device or backend "
-                            "is needed since you are running quantum algorithm.")
+            raise AquaError("A QuantumInstance or Backend "
+                            "must be supplied to run the quantum algorithm.")
         if isinstance(quantum_instance, (BaseBackend, Backend)):
             self.set_backend(quantum_instance, **kwargs)
         else:

qiskit/aqua/components/neural_networks/pytorch_discriminator.py

@@ -90,7 +90,7 @@ def load_model(self, load_dir: str):
         Args:
             load_dir: file with stored pytorch discriminator model to be loaded
         """
-        torch.load(load_dir)
+        self._discriminator = torch.load(load_dir)
 
     @property
     def discriminator_net(self):

qiskit/aqua/components/optimizers/nlopts/nloptimizer.py

@@ -93,6 +93,8 @@ def optimize(self, num_vars, objective_function, gradient_function=None,
                  variable_bounds=None, initial_point=None):
         super().optimize(num_vars, objective_function,
                          gradient_function, variable_bounds, initial_point)
+        if variable_bounds is None:
+            variable_bounds = [(None, None)] * num_vars
         return self._minimize(self._optimizer_names[self.get_nlopt_optimizer()],
                               objective_function,
                               variable_bounds,
@@ -101,7 +103,7 @@ def optimize(self, num_vars, objective_function, gradient_function=None,
     def _minimize(self,
                   name: str,
                   objective_function: Callable,
-                  variable_bounds: Optional[List[Tuple[float, float]]] = None,
+                  variable_bounds: Optional[List[Tuple[float, float]]],
                   initial_point: Optional[np.ndarray] = None,
                   max_evals: int = 1000) -> Tuple[float, float, int]:
         """Minimize using objective function