Andrea's suggestions

src/qibo/quantum_info/utils.py

@@ -372,16 +372,6 @@ def total_variation_distance(
     if isinstance(prob_dist_q, list):
         prob_dist_q = backend.cast(prob_dist_q, dtype=np.float64)
 
-    if (len(prob_dist_p.shape) != 1) or (len(prob_dist_q.shape) != 1):
-        raise_error(
-            TypeError,
-            "Probability arrays must have dims (k,) but have "
-            + f"dims {prob_dist_p.shape} and {prob_dist_q.shape}.",
-        )
-
-    if (len(prob_dist_p) == 0) or (len(prob_dist_q) == 0):
-        raise_error(TypeError, "At least one of the arrays is empty.")
-
     if validate:
         if (any(prob_dist_p < 0) or any(prob_dist_p > 1.0)) or (
             any(prob_dist_q < 0) or any(prob_dist_q > 1.0)

tests/test_quantum_info_utils.py

@@ -216,18 +216,6 @@ def test_hellinger_shot_error(backend, validate, kind):
 @pytest.mark.parametrize("kind", [None, list])
 @pytest.mark.parametrize("validate", [False, True])
 def test_total_variation_distance(backend, validate, kind):
-    with pytest.raises(TypeError):
-        prob = np.random.rand(1, 2)
-        prob_q = np.random.rand(1, 5)
-        prob = backend.cast(prob, dtype=prob.dtype)
-        prob_q = backend.cast(prob_q, dtype=prob_q.dtype)
-        test = total_variation_distance(prob, prob_q, backend=backend)
-    with pytest.raises(TypeError):
-        prob = np.random.rand(1, 2)[0]
-        prob_q = np.array([])
-        prob = backend.cast(prob, dtype=prob.dtype)
-        prob_q = backend.cast(prob_q, dtype=prob_q.dtype)
-        test = total_variation_distance(prob, prob_q, backend=backend)
     with pytest.raises(ValueError):
         prob = np.array([-1, 2.0])
         prob_q = np.random.rand(1, 5)[0]