diff --git a/tests/test_models_encodings.py b/tests/test_models_encodings.py
index d0c255ae49..7862a1e0f4 100644
--- a/tests/test_models_encodings.py
+++ b/tests/test_models_encodings.py
@@ -40,7 +40,7 @@ def test_unary_encoder(backend, nqubits):
 
 
 @pytest.mark.parametrize("seed", [None, 10, np.random.default_rng(10)])
-@pytest.mark.parametrize("nqubits", [8, 16])
+@pytest.mark.parametrize("nqubits", [8])
 def test_unary_encoder_random_gaussian(backend, nqubits, seed):
     """Tests if encoded vector are random variables sampled from
     Gaussian distribution with 0.0 mean and variance close to the norm
diff --git a/tests/test_quantum_info_metrics.py b/tests/test_quantum_info_metrics.py
index 06efa6c61f..2903102839 100644
--- a/tests/test_quantum_info_metrics.py
+++ b/tests/test_quantum_info_metrics.py
@@ -598,7 +598,7 @@ def test_entangling_capability(backend):
         entangling_capability(circuit, samples, backend=backend)
 
     nqubits = 2
-    samples = 500
+    samples = 100
 
     c1 = Circuit(nqubits)
     c1.add([gates.RX(q, 0, trainable=True) for q in range(nqubits)])
@@ -631,7 +631,7 @@ def test_expressibility(backend):
         expressibility(circuit, t, samples, backend=backend)
 
     nqubits = 2
-    samples = 500
+    samples = 100
     t = 1
 
     c1 = Circuit(nqubits)
@@ -652,9 +652,9 @@ def test_expressibility(backend):
     backend.assert_allclose(expr_1 < expr_2 < expr_3, True)
 
 
-@pytest.mark.parametrize("samples", [int(1e2)])
+@pytest.mark.parametrize("samples", [int(1e1)])
 @pytest.mark.parametrize("power_t", [2])
-@pytest.mark.parametrize("nqubits", [2, 3, 4])
+@pytest.mark.parametrize("nqubits", [2, 3])
 def test_frame_potential(backend, nqubits, power_t, samples):
     depth = int(np.ceil(nqubits * power_t))
 
diff --git a/tests/test_quantum_info_random.py b/tests/test_quantum_info_random.py
index 7704970cc6..01000ab822 100644
--- a/tests/test_quantum_info_random.py
+++ b/tests/test_quantum_info_random.py
@@ -37,7 +37,7 @@ def test_uniform_sampling_U3(backend, seed):
     Y = backend.cast(matrices.Y, dtype=matrices.Y.dtype)
     Z = backend.cast(matrices.Z, dtype=matrices.Z.dtype)
 
-    ngates = int(1e4)
+    ngates = int(1e3)
     phases = uniform_sampling_U3(ngates, seed=seed, backend=backend)
 
     # expectation values in the 3 directions should be the same
diff --git a/tests/test_quantum_info_utils.py b/tests/test_quantum_info_utils.py
index 4820a0fe5a..f873078bb4 100644
--- a/tests/test_quantum_info_utils.py
+++ b/tests/test_quantum_info_utils.py
@@ -180,16 +180,16 @@ def test_haar_integral_errors(backend):
         test = haar_integral(nqubits, power_t, samples=samples, backend=backend)
 
 
-@pytest.mark.parametrize("power_t", [1, 2, 3])
+@pytest.mark.parametrize("power_t", [1, 2])
 @pytest.mark.parametrize("nqubits", [2, 3])
 def test_haar_integral(backend, nqubits, power_t):
-    samples = int(1e4)
+    samples = int(1e3)
 
     haar_int_exact = haar_integral(nqubits, power_t, samples=None, backend=backend)
 
     haar_int_sampled = haar_integral(nqubits, power_t, samples=samples, backend=backend)
 
-    backend.assert_allclose(haar_int_sampled, haar_int_exact, atol=1e-2)
+    backend.assert_allclose(haar_int_sampled, haar_int_exact, atol=1e-1)
 
 
 def test_pqc_integral(backend):