diff --git a/tensorflow_quantum/python/optimizers/spsa_minimizer.py b/tensorflow_quantum/python/optimizers/spsa_minimizer.py
index 471b46d9e..0b2cc11bd 100644
--- a/tensorflow_quantum/python/optimizers/spsa_minimizer.py
+++ b/tensorflow_quantum/python/optimizers/spsa_minimizer.py
@@ -135,7 +135,7 @@ def minimize(expectation_value_function,
              tolerance=1e-5,
              max_iterations=200,
              alpha=0.602,
-             learning_rate=1.0,
+             learning_rate=0.31,
              perturb=1.0,
              gamma=0.101,
              blocking=False,
@@ -277,7 +277,7 @@ def _body(state):
             """Main optimization loop."""
             new_learning_rate = learning_rate_init / (
                 (tf.cast(state.num_iterations + 1, tf.float32) +
-                 0.01 * tf.cast(max_iterations, tf.float32))**state.alpha)
+                 0.1 * tf.cast(max_iterations, tf.float32))**state.alpha)
             new_perturb = perturb_init / (tf.cast(state.num_iterations + 1,
                                                   tf.float32)**state.gamma)
 
diff --git a/tensorflow_quantum/python/optimizers/spsa_minimizer_test.py b/tensorflow_quantum/python/optimizers/spsa_minimizer_test.py
index 1de86d86c..270c47de2 100644
--- a/tensorflow_quantum/python/optimizers/spsa_minimizer_test.py
+++ b/tensorflow_quantum/python/optimizers/spsa_minimizer_test.py
@@ -106,7 +106,7 @@ def test_quadratic_function_optimization(self):
         func = lambda x: tf.math.reduce_sum(np.power(x, 2) * coefficient)
 
         result = spsa_minimizer.minimize(func, tf.random.uniform(shape=[n]))
-        self.assertAlmostEqual(func(result.position).numpy(), 0, delta=2e-4)
+        self.assertAlmostEqual(func(result.position).numpy(), 0, delta=1e-4)
         self.assertTrue(result.converged)
 
     def test_noisy_sin_function_optimization(self):