diff --git a/docs/conf.py b/docs/conf.py
index ed865d6f9..4c239b619 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -138,15 +138,8 @@
 
 translations_list = [
     ("en", "English"),
-    ("bn_BN", "Bengali"),
-    ("fr_FR", "French"),
-    ("hi_IN", "Hindi"),
     ("ja_JP", "Japanese"),
-    ("ko_KR", "Korean"),
-    ("ru_RU", "Russian"),
     ("es_UN", "Spanish"),
-    ("ta_IN", "Tamil"),
-    ("tr_TR", "Turkish"),
 ]
 language = "en"
 
diff --git a/qiskit_machine_learning/kernels/algorithms/quantum_kernel_trainer.py b/qiskit_machine_learning/kernels/algorithms/quantum_kernel_trainer.py
index 8dc172ba4..d6af61a4c 100644
--- a/qiskit_machine_learning/kernels/algorithms/quantum_kernel_trainer.py
+++ b/qiskit_machine_learning/kernels/algorithms/quantum_kernel_trainer.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2021, 2023.
+# (C) Copyright IBM 2021, 2024.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -13,7 +13,6 @@
 """Quantum Kernel Trainer"""
 from __future__ import annotations
 
-import copy
 from functools import partial
 from typing import Sequence
 
@@ -198,13 +197,17 @@ def fit(
             msg = "Quantum kernel cannot be fit because there are no user parameters specified."
             raise ValueError(msg)
 
-        # Bind inputs to objective function
-        output_kernel = copy.deepcopy(self._quantum_kernel)
-
         # Randomly initialize the initial point if one was not passed
         if self._initial_point is None:
             self._initial_point = algorithm_globals.random.random(num_params)
 
+        # Bind inputs to objective function
+        output_kernel = type(self._quantum_kernel)(
+            feature_map=self._quantum_kernel.feature_map,
+            training_parameters=self._quantum_kernel.training_parameters,
+        )
+        output_kernel.assign_training_parameters(parameter_values=self.initial_point)
+
         # Perform kernel optimization
         loss_function = partial(
             self._loss.evaluate, quantum_kernel=self.quantum_kernel, data=data, labels=labels