Bug fix on QNSPSA

qiskit/algorithms/optimizers/qnspsa.py

@@ -228,9 +228,10 @@ def _point_sample(self, loss, x, eps, delta1, delta2):
         gradient_estimate = (loss_values[0] - loss_values[1]) / (2 * eps) * delta1
 
         # compute the preconditioner point estimate
+        fidelity_values = np.asarray(fidelity_values, dtype=float)
         diff = fidelity_values[2] - fidelity_values[0]
-        diff -= fidelity_values[3] - fidelity_values[1]
-        diff /= 2 * eps**2
+        diff = diff - (fidelity_values[3] - fidelity_values[1])
+        diff = diff / (2 * eps**2)
 
         rank_one = np.outer(delta1, delta2)
         # -0.5 factor comes from the fact that we need -0.5 * fidelity

releasenotes/notes/qnspsa-float-bug-fix-4035f7e1eb61dec2.yaml

@@ -0,0 +1,5 @@
+---
+fixes:
+  - |
+    Fixed bug in :class:`.QNSPSA` which raised a type error when the computed fidelities
+    happened to be integers but the perturbation was of type float.

test/python/algorithms/optimizers/test_spsa.py

@@ -255,3 +255,28 @@ def objective(x):
         with self.subTest("check number of function calls"):
             expected_nfev = 8  # 7 * maxiter + 1
             self.assertEqual(result.nfev, expected_nfev)
+
+    def test_point_sample(self):
+        """Test point sample function in QNSPSA"""
+        # pylint: disable=invalid-name
+
+        def fidelity(x, _y):
+            x = np.asarray(x)
+            return np.ones_like(x, dtype=float)  # some float
+
+        def objective(x):
+            return x
+
+        def get_perturbation():
+            def perturbation():
+                while True:
+                    yield 1
+
+            return perturbation
+
+        qnspsa = QNSPSA(fidelity, maxiter=1, learning_rate=0.1, perturbation=get_perturbation())
+        initial_point = 1.0
+        result = qnspsa.minimize(objective, initial_point)
+
+        expected_nfev = 8  # 7 * maxiter + 1
+        self.assertEqual(result.nfev, expected_nfev)