diff --git a/aten/src/ATen/native/TensorTransformations.cpp b/aten/src/ATen/native/TensorTransformations.cpp
index 7d44463fa830..162468ecc711 100644
--- a/aten/src/ATen/native/TensorTransformations.cpp
+++ b/aten/src/ATen/native/TensorTransformations.cpp
@@ -34,6 +34,11 @@
 namespace at::native {
 
 Tensor flip(const Tensor& self, IntArrayRef dims) {
+  TORCH_CHECK(
+      self.scalar_type() != at::kQUInt4x2 &&
+          self.scalar_type() != at::kQUInt2x4,
+      "flip is not supported for tensor with data type ",
+      self.scalar_type());
   const int64_t total_dims = self.dim();
   // It wraps the dims and checks that there are no repeated dims
   auto flip_dims_b = at::dim_list_to_bitset(dims, total_dims);
diff --git a/test/test_shape_ops.py b/test/test_shape_ops.py
index f89bb81745f8..a60b04c4f8b5 100644
--- a/test/test_shape_ops.py
+++ b/test/test_shape_ops.py
@@ -581,6 +581,16 @@ class TestShapeOps(TestCase):
         self.compare_with_numpy(torch_fn, np_fn, t_in)
         del t_in
 
+    @onlyCPU
+    @unittest.expectedFailure
+    @dtypes(torch.quint4x2, torch.quint2x4)
+    def test_flip_unsupported_dtype(self, dtype):
+        scale, zero_point = 0.1, 5
+        qt = torch.quantize_per_tensor(
+            torch.randn(16, 16), scale=scale, zero_point=zero_point, dtype=dtype
+        )
+        torch.flip(qt, dims=(0,))
+
     def _test_fliplr_flipud(self, torch_fn, np_fn, min_dim, max_dim, device, dtype):
         for dim in range(min_dim, max_dim + 1):
             shape = self._rand_shape(dim, 5, 10)