diff --git a/test/embedding/test_ae_feature_map.py b/test/embedding/test_ae_feature_map.py
index 21010d5..b68b66e 100644
--- a/test/embedding/test_ae_feature_map.py
+++ b/test/embedding/test_ae_feature_map.py
@@ -41,7 +41,7 @@ def test_rnd_state(self):
 
         state = Statevector(circuit)
 
-        self.assertTrue(np.allclose(state_vector, state))
+        self.assertTrue(np.allclose(state_vector, state, rtol=1e-03, atol=1e-05))
 
     def test_non_normalized_state(self):
         # This test is important because during NQE training
@@ -61,7 +61,7 @@ def test_non_normalized_state(self):
         state = Statevector(circuit)
 
         state_vector = state_vector / np.linalg.norm(state_vector)
-        self.assertTrue(np.allclose(state_vector, state))
+        self.assertTrue(np.allclose(state_vector, state, rtol=1e-03, atol=1e-05))
 
     def test_sparse_state(self):
         # Ensure that embedding is preventing division by zero.
@@ -80,4 +80,36 @@ def test_sparse_state(self):
         state = Statevector(circuit)
 
         state_vector = state_vector / np.linalg.norm(state_vector)
-        self.assertTrue(np.allclose(state_vector, state))
+        self.assertTrue(np.allclose(state_vector, state, rtol=1e-03, atol=1e-05))
+
+    def test_fixed_state(self):
+        n_qubits = 6
+        state_vector = [-0.00750879, -0.19509541,  0.13329143, -0.05016399,
+                         0.15207381,  0.07559872,  0.06584141,  0.02580133,
+                         0.07838591,  0.1797185,  -0.08671011,  0.12130839,
+                        -0.01520751, -0.16350927, -0.14340019,  0.06187,
+                        -0.15696974, -0.07004782, -0.10200592, -0.13275576,
+                         0.03975269, -0.06866376, -0.19666519, -0.19759358,
+                        -0.06338929,  0.01957956, -0.04101603, -0.17654797,
+                         0.13379771,  0.13126602, -0.06316672, -0.16629322,
+                         0.19740418, -0.01161936,  0.00660316,  0.18850766,
+                         0.05001657,  0.05713368, -0.10440029, -0.19293127,
+                        -0.21320381, -0.15065956,  0.20290586, -0.03929146,
+                         0.0254895,   0.03343766, -0.17821551, -0.1903655,
+                         0.13099242, -0.11116633,  0.15833651, -0.02708352,
+                        -0.03610143,  0.20473973,  0.03146476, -0.03039286,
+                        -0.14796486,  0.00971746, -0.15238775,  0.12996466,
+                        -0.19630254, -0.08859373, -0.12830557,  0.12446281]
+
+#        state_vector = state_vector / np.linalg.norm(state_vector)
+
+        feature_map = AeFeatureMap(n_qubits, normalize=False, set_global_phase=True)
+
+        circuit = QuantumCircuit(n_qubits)
+        circuit.compose(feature_map, list(range(n_qubits)), inplace=True)
+        circuit.assign_parameters(state_vector, inplace=True)
+
+        state = Statevector(circuit)
+
+        state_vector = state_vector / np.linalg.norm(state_vector)
+        self.assertTrue(np.allclose(state_vector, state, rtol=1e-03, atol=1e-05))