added scale with space

Signed-off-by: Melvin Strobl <[email protected]>

qml_essentials/expressibility.py

@@ -82,6 +82,7 @@ def state_fidelities(
         n_input_samples: int,
         input_domain: List[float],
         model: Model,
+        scale_with_space: bool = True,
         **kwargs: Any,
     ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
         """
@@ -94,6 +95,8 @@ def state_fidelities(
             n_input_samples (int): Number of input samples.
             input_domain (List[float]): Input domain.
             model (Callable): Function that models the quantum circuit.
+            scale_with_space (bool): Whether to scale the number of
+                samples (exp) and bins (lin) with the number of qubits.
             kwargs (Any): Additional keyword arguments for the model function.
 
         Returns:
@@ -103,6 +106,10 @@ def state_fidelities(
 
         x = np.linspace(*input_domain, n_input_samples, requires_grad=False)
 
+        if scale_with_space:
+            n_samples = np.power(2, model.n_qubits) * n_samples
+            n_bins = model.n_qubits * n_bins
+
         fidelities = Expressibility._sample_state_fidelities(
             x_samples=x,
             n_samples=n_samples,
@@ -168,6 +175,7 @@ def haar_integral(
         n_qubits: int,
         n_bins: int,
         cache: bool = True,
+        scale_with_space: bool = True,
     ) -> Tuple[np.ndarray, np.ndarray]:
         """
         Calculates theoretical probability density function for random Haar states
@@ -185,10 +193,13 @@ def haar_integral(
                 - y component (probabilities): the haar probability density
                   funtion for random Haar states
         """
+        if scale_with_space:
+            n_bins = n_qubits * n_bins
+
         x = np.linspace(0, 1, n_bins)
 
         if cache:
-            name = f"haar_{n_qubits}q_{n_bins}s.npy"
+            name = f"haar_{n_qubits}q_{n_bins}s_{'scaled' if scale_with_space else 'unscaled'}.npy"
 
             cache_folder = ".cache"
             if not os.path.exists(cache_folder):