diff --git a/qiskit_experiments/library/randomized_benchmarking/clifford_utils.py b/qiskit_experiments/library/randomized_benchmarking/clifford_utils.py
index ea89ceb816..666f8b0cc9 100644
--- a/qiskit_experiments/library/randomized_benchmarking/clifford_utils.py
+++ b/qiskit_experiments/library/randomized_benchmarking/clifford_utils.py
@@ -741,7 +741,6 @@ def _layer_indices_from_num(num: Integral) -> Tuple[Integral, Integral, Integral
     return idx0, idx1, idx2
 
 
-@lru_cache(maxsize=24 * 24)
 def _tensor_1q_nums(first: Integral, second: Integral) -> Integral:
     """Return the 2-qubit Clifford integer that is the tensor product of 1-qubit Cliffords."""
     return _CLIFFORD_TENSOR_1Q[first, second]
diff --git a/releasenotes/notes/layer-fidelity-1e09dea9e5b69515.yaml b/releasenotes/notes/layer-fidelity-1e09dea9e5b69515.yaml
index f360229551..6ca0fe3183 100644
--- a/releasenotes/notes/layer-fidelity-1e09dea9e5b69515.yaml
+++ b/releasenotes/notes/layer-fidelity-1e09dea9e5b69515.yaml
@@ -2,7 +2,7 @@
 features:
   - |
     Add a new experiment class :class:`.LayerFidelity` to measure
-    `layer fidelity and EPLG (error per layered gates)<https://arxiv.org/abs/2311.05933>`_,
+    `layer fidelity and EPLG (error per layered gate) <https://arxiv.org/abs/2311.05933>`_,
     which is a holistic benchmark to characterize the full quality of the devices at scale.
 
     It has an experimental feature: its :meth:`circuits`