Integration with qiskit-symb

.github/workflows/deploy_ghpages.yml

@@ -34,6 +34,7 @@ jobs:
             apt-get -y install --fix-missing git-core
             apt-get -y install build-essential
             pip install -r doc/requirements.txt
+            cp -a /symb_statevectors/. /doc
       - name:  Upload generated HTML as artifact
         uses: actions/upload-artifact@v4
         with:

README.md

@@ -123,12 +123,21 @@ To check the installation, open a python shell and type:
 import pyriemann_qiskit
 ```
 
-To enable Qiskit GPU optimization when using quantum simulation, run:
+To enable Qiskit GPU optimization (for Linux) when using quantum simulation, run:
+
+```
+pip install .[optim_linux]
+```
+
+To use symbolic quantum simulation, run:
 
 ```
 pip install .[optim]
 ```
 
+Which will enable [qiskit-symb](https://github.com/SimoneGasperini/qiskit-symb)
+integration.
+
 Note, Qiskit only provide binaries for Linux. For other platforms, or if you want to
 enable specific NVIDIA optimization for quantum, you need to build the binary
 [yourself](https://github.com/Qiskit/qiskit-aer/blob/main/CONTRIBUTING.md#building-with-gpu-support).

doc/requirements.txt

@@ -23,3 +23,4 @@ moabb==1.1.0
 pyriemann==0.6
 docplex>=2.21.207
 firebase_admin==6.5.0
+qiskit-symb

pyriemann_qiskit/classification.py

@@ -81,7 +81,7 @@ class QuanticClassifierBase(BaseEstimator, ClassifierMixin):
         If true, will output all intermediate results and logs.
     shots : int, default=1024
         Number of repetitions of each circuit, for sampling.
-    gen_feature_map : Callable[int, QuantumCircuit | FeatureMap], \
+    gen_feature_map : Callable[[int, str], QuantumCircuit | FeatureMap], \
                       default=Callable[int, ZZFeatureMap]
         Function generating a feature map to encode data into a quantum state.
     seed : int | None, default=None
@@ -357,7 +357,7 @@ class QuanticSVM(QuanticClassifierBase):
         If true, will output all intermediate results and logs.
     shots : int, default=1024
         Number of repetitions of each circuit, for sampling.
-    gen_feature_map : Callable[int, QuantumCircuit | FeatureMap], \
+    gen_feature_map : Callable[[int, str], QuantumCircuit | FeatureMap], \
                       default=Callable[int, ZZFeatureMap]
         Function generating a feature map to encode data into a quantum state.
     seed : int | None, default=None
@@ -420,8 +420,10 @@ def _init_algo(self, n_features):
         if self.quantum:
             quantum_kernel = get_quantum_kernel(
                 self._feature_map,
+                self.gen_feature_map,
                 self._quantum_instance,
                 self.use_fidelity_state_vector_kernel,
+                not self.pegasos,
             )
             if self.pegasos:
                 self._log("[Warning] `gamma` is not supported by PegasosQSVC")
@@ -496,7 +498,7 @@ class QuanticVQC(QuanticClassifierBase):
         If true, will output all intermediate results and logs
     shots : int, default=1024
         Number of repetitions of each circuit, for sampling
-    gen_feature_map : Callable[int, QuantumCircuit | FeatureMap], \
+    gen_feature_map : Callable[[int, str], QuantumCircuit | FeatureMap], \
                       default=Callable[int, ZZFeatureMap]
         Function generating a feature map to encode data into a quantum state.
     seed : int | None, default=None

pyriemann_qiskit/pipelines.py

@@ -458,7 +458,7 @@ class QuantumMDMVotingClassifier(BasePipeline):
         If true, will output all intermediate results and logs.
     shots : int (default:1024)
         Number of repetitions of each circuit, for sampling.
-    gen_feature_map : Callable[int, QuantumCircuit | FeatureMap] \
+    gen_feature_map : Callable[[int, str], QuantumCircuit | FeatureMap] \
                       (default : Callable[int, ZZFeatureMap])
         Function generating a feature map to encode data into a quantum state.
 

pyriemann_qiskit/utils/hyper_params_factory.py

@@ -25,8 +25,12 @@ def gen_x_feature_map(reps=2):
 
     Returns
     -------
-    ret : XFeatureMap
-        An instance of XFeatureMap.
+    ret : Callable[[int, str], XFeatureMap]
+        A callable that takes into arguments:
+
+        - the number of features
+        - the prefix string for the parameters
+        And returns an instance of XFeatureMap.
 
     Raises
     ------
@@ -41,16 +45,18 @@ def gen_x_feature_map(reps=2):
     Notes
     -----
     .. versionadded:: 0.2.0
+    .. versionchanged:: 0.4.0
+        Possibility to specify parameter prefix.
     """
     if reps < 1:
         raise ValueError(f"Parameter reps must be superior or equal to 1 (Got {reps})")
 
-    return lambda n_features: PauliFeatureMap(
+    return lambda n_features, param_prefix="x": PauliFeatureMap(
         feature_dimension=n_features,
         paulis=["X"],
         reps=reps,
         data_map_func=None,
-        parameter_prefix="x",
+        parameter_prefix=param_prefix,
         insert_barriers=False,
         name="XFeatureMap",
     )
@@ -70,8 +76,12 @@ def gen_z_feature_map(reps=2):
 
     Returns
     -------
-    ret : ZFeatureMap
-        An instance of ZFeatureMap.
+    ret : Callable[[int, str], ZFeatureMap]
+        A callable that takes into arguments:
+
+        - the number of features
+        - the prefix string for the parameters
+        And returns an instance of ZFeatureMap.
 
     Raises
     ------
@@ -86,11 +96,17 @@ def gen_z_feature_map(reps=2):
     Notes
     -----
     .. versionadded:: 0.2.0
+    .. versionchanged:: 0.4.0
+        Possibility to specify parameter prefix.
     """
     if reps < 1:
         raise ValueError(f"Parameter reps must be superior or equal to 1 (Got {reps})")
 
-    return lambda n_features: ZFeatureMap(feature_dimension=n_features, reps=reps)
+    return lambda n_features, param_prefix="x": ZFeatureMap(
+        feature_dimension=n_features,
+        reps=reps,
+        parameter_prefix=param_prefix,
+    )
 
 
 def gen_zz_feature_map(reps=2, entanglement="linear"):
@@ -114,8 +130,12 @@ def gen_zz_feature_map(reps=2, entanglement="linear"):
 
     Returns
     -------
-    ret : ZZFeatureMap
-        An instance of ZZFeatureMap.
+    ret : Callable[[int, str], ZZFeatureMap]
+        A callable that takes into arguments:
+
+        - the number of features
+        - the prefix string for the parameters
+        And returns an instance of ZZFeatureMap.
 
     Raises
     ------
@@ -132,12 +152,17 @@ def gen_zz_feature_map(reps=2, entanglement="linear"):
     Notes
     -----
     .. versionadded:: 0.0.1
+    .. versionchanged:: 0.4.0
+        Possibility to specify parameter prefix.
     """
     if reps < 1:
         raise ValueError(f"Parameter reps must be superior or equal to 1 (Got {reps})")
 
-    return lambda n_features: ZZFeatureMap(
-        feature_dimension=n_features, reps=reps, entanglement=entanglement
+    return lambda n_features, param_prefix="x": ZZFeatureMap(
+        feature_dimension=n_features,
+        reps=reps,
+        entanglement=entanglement,
+        parameter_prefix=param_prefix,
     )