ADAM bug when calculating the gradient in batches #178 (backport #183)

qiskit_algorithms/optimizers/adam_amsgrad.py

@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2019, 2023.
+# (C) Copyright IBM 2019, 2024.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -209,7 +209,9 @@ def minimize(
             The result of the optimization, containing e.g. the result as attribute ``x``.
         """
         if jac is None:
-            jac = Optimizer.wrap_function(Optimizer.gradient_num_diff, (fun, self._eps))
+            jac = Optimizer.wrap_function(
+                Optimizer.gradient_num_diff, (fun, self._eps, self._max_evals_grouped)
+            )
 
         derivative = jac(x0)
         self._t = 0

releasenotes/notes/fix_aqgd_max_grouped_evals-fbe108c005a9b7ac.yaml

@@ -0,0 +1,9 @@
+---
+fixes:
+  - |
+    Fixed the AQGD optimizer grouping objective function calls by default so that a single point is now passed to the
+    objective function. For algorithms that can handle more than one gradient evaluations in their objective function,
+    such as a VQE in the algorithms here, the number of grouped evaluations can be controlled via the max_grouped_evals
+    parameter. Grouped evaluations allows a list of points to be handed over so that they can potentially be assessed
+    more efficiently in a single job.
+

test/optimizers/test_adam.py

@@ -0,0 +1,86 @@
+# This code is part of a Qiskit project.
+#
+# (C) Copyright IBM 2024.
+#
+# This code is licensed under the Apache License, Version 2.0. You may
+# obtain a copy of this license in the LICENSE.txt file in the root directory
+# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
+#
+# Any modifications or derivative works of this code must retain this
+# copyright notice, and modified files need to carry a notice indicating
+# that they have been altered from the originals.
+
+"""Tests for the ADAM optimizer."""
+
+from test import QiskitAlgorithmsTestCase
+
+from ddt import ddt, data
+import numpy as np
+
+from qiskit_algorithms.optimizers import ADAM, Optimizer
+from qiskit_algorithms.utils import algorithm_globals
+
+
+@ddt
+class TestADAM(QiskitAlgorithmsTestCase):
+    """Tests for the ADAM optimizer."""
+
+    def setUp(self):
+        super().setUp()
+        algorithm_globals.random_seed = 52
+        # Feature vector
+        self.x = np.array([1, 2, 3, 4])
+        # Target value
+        self.y = 5
+
+    def objective(self, w):
+        """
+        Objective function to minimize mean squared error.
+
+        Parameters:
+        w : numpy array
+            The weights (including bias) for the linear model.
+
+        Returns:
+        float
+            The mean squared error.
+        """
+        # Extract weights and bias from the parameter vector
+        new_shape = (5, int(len(w) / 5))
+        w = np.reshape(w, new_shape)
+
+        weights = w[:-1, :]
+        bias = w[-1, :]
+        # Calculate the predicted values
+        y_pred = np.dot(self.x, weights) + bias
+        # Calculate the mean squared error
+        mse = np.mean((self.y - y_pred) ** 2)
+        return mse
+
+    def run_optimizer(self, optimizer: Optimizer, weights: np.ndarray, max_nfev: int):
+        """Test the optimizer.
+
+        Args:
+            optimizer: The optimizer instance to test.
+            weights: The weights to optimize.
+            max_nfev: The maximal allowed number of function evaluations.
+        """
+
+        # Minimize
+        res = optimizer.minimize(self.objective, np.array(weights), None)
+        error = res.fun
+        nfev = res.nfev
+
+        self.assertAlmostEqual(error, 0, places=3)
+        self.assertLessEqual(nfev, max_nfev)
+
+    @data(1, 5)
+    def test_adam_max_evals(self, max_evals_grouped):
+        """adam test"""
+        # Initialize weights (including bias)
+        w = np.zeros(len(self.x) + 1)
+        # Initialize optimizer
+        optimizer = ADAM(maxiter=10000, tol=1e-06)
+        # Test one evaluation at a time
+        optimizer.set_max_evals_grouped(max_evals_grouped)
+        self.run_optimizer(optimizer, w, max_nfev=10000)