diff --git a/backends/cadence/aot/ops_registrations.py b/backends/cadence/aot/ops_registrations.py
index da82a1ea3ec..6789d17f16b 100644
--- a/backends/cadence/aot/ops_registrations.py
+++ b/backends/cadence/aot/ops_registrations.py
@@ -489,6 +489,13 @@ def register_fake(
     "rope_rotate_stacked_halves.out(Tensor input, Tensor sin_tensor, Tensor cos_tensor, Tensor? pos, *, Tensor(a!) out) -> Tensor(a!)"
 )
 
+lib.define(
+    "quantized_rope_rotate_stacked_halves(Tensor input, Tensor sin_tensor, Tensor cos_tensor, Tensor? pos, float in_scale, int in_zero_point, float out_scale, int out_zero_point) -> (Tensor out)"
+)
+lib.define(
+    "quantized_rope_rotate_stacked_halves.out(Tensor input, Tensor sin_tensor, Tensor cos_tensor, Tensor? pos, float in_scale, int in_zero_point, float out_scale, int out_zero_point, *, Tensor(a!) out) -> Tensor(a!)"
+)
+
 lib.define(
     "quantized_softmax(Tensor input, Tensor mask, int dim, int mask_type, Tensor pos, Tensor in_scale, Tensor in_zero_point, Tensor out_scale, Tensor out_zero_point) -> (Tensor out)"
 )
@@ -3136,6 +3143,20 @@ def rope_rotate_stacked_halves_meta(
     return input.new_empty(input.shape, dtype=input.dtype)
 
 
+@register_fake("cadence::quantized_rope_rotate_stacked_halves")
+def quantized_rope_rotate_stacked_halves_meta(
+    input: torch.Tensor,
+    sin_tensor: torch.Tensor,
+    cos_tensor: torch.Tensor,
+    pos: Optional[torch.Tensor],
+    in_scale: float,
+    in_zero_point: int,
+    out_scale: float,
+    out_zero_point: int,
+) -> torch.Tensor:
+    return rope_rotate_stacked_halves_meta(input, sin_tensor, cos_tensor, pos)
+
+
 @register_fake("cadence::idma_copy")
 def copy_idma_copy_impl(
     src: torch.Tensor,
diff --git a/backends/cadence/aot/ref_implementations.py b/backends/cadence/aot/ref_implementations.py
index d3a5c853a4a..16768a8b68e 100644
--- a/backends/cadence/aot/ref_implementations.py
+++ b/backends/cadence/aot/ref_implementations.py
@@ -2265,6 +2265,38 @@ def rope_rotate_stacked_halves(
     return rotated.view(original_shape)
 
 
+@impl_tracked(m, "quantized_rope_rotate_stacked_halves")
+def quantized_rope_rotate_stacked_halves(
+    input_tensor: torch.Tensor,
+    sin_tensor: torch.Tensor,
+    cos_tensor: torch.Tensor,
+    pos: torch.Tensor | None,
+    in_scale: float,
+    in_zero_point: int,
+    out_scale: float,
+    out_zero_point: int,
+) -> torch.Tensor:
+    dtype = input_tensor.dtype
+    dtype_limits = torch.iinfo(dtype)
+    dequantized = dequantize_per_tensor_common(
+        input_tensor,
+        in_scale,
+        in_zero_point,
+        dtype_limits.min,
+        dtype_limits.max,
+        dtype,
+    )
+    rotated = rope_rotate_stacked_halves(dequantized, sin_tensor, cos_tensor, pos)
+    return quantize_per_tensor_common(
+        rotated,
+        out_scale,
+        out_zero_point,
+        dtype_limits.min,
+        dtype_limits.max,
+        dtype,
+    )
+
+
 @impl_tracked(m, "im2row")
 def im2row(
     input_tensor: torch.Tensor,