diff --git a/src/qibo/quantum_info/random_ensembles.py b/src/qibo/quantum_info/random_ensembles.py
index 84cb79af48..18e2eedaa1 100644
--- a/src/qibo/quantum_info/random_ensembles.py
+++ b/src/qibo/quantum_info/random_ensembles.py
@@ -384,7 +384,7 @@ def random_quantum_channel(
     return super_op
 
 
-def random_statevector(dims: int, haar: bool = False, seed=None, backend=None):
+def random_statevector(dims: int, seed=None, backend=None):
     """Creates a random statevector :math:`\\ket{\\psi}`.
 
     .. math::
@@ -396,10 +396,6 @@ def random_statevector(dims: int, haar: bool = False, seed=None, backend=None):
 
     Args:
         dims (int): dimension of the matrix.
-        haar (bool, optional): if ``True``, statevector is created by sampling a
-            Haar random unitary :math:`U_{\\text{haar}}` and acting with it on a
-            random computational basis state :math:`\\ket{k}`, i.e.
-            :math:`\\ket{\\psi} = U_{\\text{haar}} \\ket{k}`. Defaults to ``False``.
         seed (int or :class:`numpy.random.Generator`, optional): Either a generator of
             random numbers or a fixed seed to initialize a generator. If ``None``,
             initializes a generator with a random seed. Defaults to ``None``.
@@ -414,9 +410,6 @@ def random_statevector(dims: int, haar: bool = False, seed=None, backend=None):
     if dims <= 0:
         raise_error(ValueError, "dim must be of type int and >= 1")
 
-    if not isinstance(haar, bool):
-        raise_error(TypeError, f"haar must be type bool, but it is type {type(haar)}.")
-
     if (
         seed is not None
         and not isinstance(seed, int)
@@ -426,23 +419,12 @@ def random_statevector(dims: int, haar: bool = False, seed=None, backend=None):
             TypeError, "seed must be either type int or numpy.random.Generator."
         )
 
-    if backend is None:  # pragma: no cover
-        backend = GlobalBackend()
-
-    local_state = (
-        np.random.default_rng(seed) if seed is None or isinstance(seed, int) else seed
-    )
+    backend, local_state = _set_backend_and_local_state(seed, backend)
 
-    if not haar:
-        # sample real and imag parts of complex amplitude in [-1, 1]
-        state = 1j * (2 * local_state.random(dims) - 1)
-        state += 2 * local_state.random(dims) - 1
-        state /= np.linalg.norm(state)
-        state = backend.cast(state, dtype=state.dtype)
-    else:
-        # select a random column of a haar random unitary
-        k = local_state.integers(low=0, high=dims)
-        state = random_unitary(dims, measure="haar", seed=seed, backend=backend)[:, k]
+    state = local_state.standard_normal(dims).astype(complex)
+    state += 1.0j * local_state.standard_normal(dims)
+    state /= np.linalg.norm(state)
+    state = backend.cast(state, dtype=state.dtype)
 
     return state
 
@@ -566,7 +548,7 @@ def random_density_matrix(
                 random_gaussian_matrix(dims, rank, seed=local_state, backend=backend),
             )
             state = np.dot(state, np.transpose(np.conj(state)))
-            state = state / np.trace(state)
+            state /= np.trace(state)
 
     state = backend.cast(state, dtype=state.dtype)
 
diff --git a/src/qibo/quantum_info/utils.py b/src/qibo/quantum_info/utils.py
index 24a6998569..c1e411360a 100644
--- a/src/qibo/quantum_info/utils.py
+++ b/src/qibo/quantum_info/utils.py
@@ -469,9 +469,7 @@ def haar_integral(
             rand_unit_density, dtype=rand_unit_density.dtype
         )
         for _ in range(samples):
-            haar_state = np.reshape(
-                random_statevector(dim, haar=True, backend=backend), (-1, 1)
-            )
+            haar_state = np.reshape(random_statevector(dim, backend=backend), (-1, 1))
 
             rho = haar_state @ np.conj(np.transpose(haar_state))
 
diff --git a/tests/test_quantum_info_random.py b/tests/test_quantum_info_random.py
index bafb4efa84..0749723637 100644
--- a/tests/test_quantum_info_random.py
+++ b/tests/test_quantum_info_random.py
@@ -223,27 +223,22 @@ def test_random_quantum_channel(backend, representation, measure, rank, order):
     )
 
 
-@pytest.mark.parametrize("haar", [False, True])
 @pytest.mark.parametrize("seed", [None, 10, np.random.default_rng(10)])
-def test_random_statevector(backend, haar, seed):
+def test_random_statevector(backend, seed):
     with pytest.raises(TypeError):
         dims = "10"
         random_statevector(dims, backend=backend)
     with pytest.raises(ValueError):
         dims = 0
         random_statevector(dims, backend=backend)
-    with pytest.raises(TypeError):
-        dims = 2
-        random_statevector(dims, haar=1, backend=backend)
     with pytest.raises(TypeError):
         dims = 2
         random_statevector(dims, seed=0.1, backend=backend)
 
     # tests if random statevector is a pure state
     dims = 4
-    state = random_statevector(dims, haar=haar, seed=seed, backend=backend)
-    backend.assert_allclose(purity(state) <= 1.0 + PRECISION_TOL, True)
-    backend.assert_allclose(purity(state) >= 1.0 - PRECISION_TOL, True)
+    state = random_statevector(dims, seed=seed, backend=backend)
+    backend.assert_allclose(abs(purity(state) - 1.0) < PRECISION_TOL, True)
 
 
 @pytest.mark.parametrize("normalize", [False, True])