diff --git a/doc/source/whatsnew/v1.3.0.rst b/doc/source/whatsnew/v1.3.0.rst
index 85d9acff353be..4ef8fe116596f 100644
--- a/doc/source/whatsnew/v1.3.0.rst
+++ b/doc/source/whatsnew/v1.3.0.rst
@@ -695,7 +695,7 @@ Conversion
 - Bug in :class:`Index` construction silently ignoring a passed ``dtype`` when the data cannot be cast to that dtype (:issue:`21311`)
 - Bug in :meth:`StringArray.astype` falling back to numpy and raising when converting to ``dtype='categorical'`` (:issue:`40450`)
 - Bug in :class:`DataFrame` construction with a dictionary containing an arraylike with ``ExtensionDtype`` and ``copy=True`` failing to make a copy (:issue:`38939`)
--
+- Bug in :meth:`qcut` raising error when taking ``Float64DType`` as input (:issue:`40730`)
 
 Strings
 ^^^^^^^
diff --git a/pandas/core/reshape/tile.py b/pandas/core/reshape/tile.py
index 41e1ff41d9ba2..7b9c3883d74e3 100644
--- a/pandas/core/reshape/tile.py
+++ b/pandas/core/reshape/tile.py
@@ -24,8 +24,8 @@
     is_datetime_or_timedelta_dtype,
     is_extension_array_dtype,
     is_integer,
-    is_integer_dtype,
     is_list_like,
+    is_numeric_dtype,
     is_scalar,
     is_timedelta64_dtype,
 )
@@ -488,7 +488,7 @@ def _coerce_to_type(x):
     # Will properly support in the future.
     # https://github.com/pandas-dev/pandas/pull/31290
     # https://github.com/pandas-dev/pandas/issues/31389
-    elif is_extension_array_dtype(x.dtype) and is_integer_dtype(x.dtype):
+    elif is_extension_array_dtype(x.dtype) and is_numeric_dtype(x.dtype):
         x = x.to_numpy(dtype=np.float64, na_value=np.nan)
 
     if dtype is not None:
diff --git a/pandas/tests/reshape/test_qcut.py b/pandas/tests/reshape/test_qcut.py
index 7996c15ae8e64..c12d28f6f1380 100644
--- a/pandas/tests/reshape/test_qcut.py
+++ b/pandas/tests/reshape/test_qcut.py
@@ -293,8 +293,8 @@ def test_qcut_bool_coercion_to_int(bins, box, compare):
 
 
 @pytest.mark.parametrize("q", [2, 5, 10])
-def test_qcut_nullable_integer(q, any_nullable_int_dtype):
-    arr = pd.array(np.arange(100), dtype=any_nullable_int_dtype)
+def test_qcut_nullable_integer(q, any_nullable_numeric_dtype):
+    arr = pd.array(np.arange(100), dtype=any_nullable_numeric_dtype)
     arr[::2] = pd.NA
 
     result = qcut(arr, q)