According to the maintainer`s relevant suggestions, the bugs have been fixed.

Author: WeiguoMa

tensorcircuit/backends/jax_backend.py

@@ -352,8 +352,8 @@ def mod(self, x: Tensor, y: Tensor) -> Tensor:
     def floor(self, x: Tensor) -> Tensor:
         return jnp.floor(x)
 
-    def clip(self, x: Tensor, lower: Tensor, upper: Tensor) -> Tensor:
-        return jnp.clip(x, lower, upper)
+    def clip(self, x: Tensor, a_min: Tensor, a_max: Tensor) -> Tensor:
+        return jnp.clip(x, a_min, a_max)
 
     def right_shift(self, x: Tensor, y: Tensor) -> Tensor:
         return jnp.right_shift(x, y)

tensorcircuit/backends/numpy_backend.py

@@ -253,8 +253,8 @@ def mod(self, x: Tensor, y: Tensor) -> Tensor:
     def floor(self, x: Tensor) -> Tensor:
         return np.floor(x)
 
-    def clip(self, x: Tensor, a_min: Tensor, a_max: Tensor) -> Tensor:
-        return np.clip(x, a_min, a_max)
+    def clip(self, a: Tensor, a_min: Tensor, a_max: Tensor) -> Tensor:
+        return np.clip(a, a_min, a_max)
 
     def right_shift(self, x: Tensor, y: Tensor) -> Tensor:
         return np.right_shift(x, y)

tensorcircuit/backends/pytorch_backend.py

@@ -432,8 +432,8 @@ def mod(self, x: Tensor, y: Tensor) -> Tensor:
     def floor(self, x: Tensor) -> Tensor:
         return torchlib.floor(x)
 
-    def clip(self, x: Tensor, a_min: Tensor, a_max: Tensor) -> Tensor:
-        return torchlib.clamp(x, a_min, a_max)
+    def clip(self, a: Tensor, a_min: Tensor, a_max: Tensor) -> Tensor:
+        return torchlib.clamp(a, a_min, a_max)
 
     def right_shift(self, x: Tensor, y: Tensor) -> Tensor:
         return torchlib.bitwise_right_shift(x, y)

tensorcircuit/backends/tensorflow_backend.py

@@ -577,17 +577,9 @@ def clip(self, a: Tensor, a_min: Tensor, a_max: Tensor) -> Tensor:
         return tf.clip_by_value(a, a_min, a_max)
 
     def floor(self, x: Tensor) -> Tensor:
-        dtype_str = x.dtype.name if hasattr(x.dtype, "name") else str(x.dtype)
-        if x.dtype.is_floating:
-            return tf.math.floor(x)
-        elif x.dtype.is_integer:
+        if x.dtype.is_integer:
             return x
-        elif x.dtype.is_complex:
-            raise TypeError(
-                f"tf.math.floor does not support complex dtype ({dtype_str})"
-            )
-        else:
-            raise TypeError(f"Unsupported dtype for floor: {dtype_str}")
+        return tf.math.floor(x)
 
     def concat(self, a: Sequence[Tensor], axis: int = 0) -> Tensor:
         return tf.concat(a, axis=axis)