Batching of circuits to overcome memory issues when using statevector simulator

.pylintdict

@@ -165,6 +165,7 @@ toctree
 todo
 traceback
 transpilation
+transpiled
 uncompiled
 unitaries
 univariate

qiskit_machine_learning/kernels/quantum_kernel.py

@@ -61,7 +61,7 @@ def __init__(
                 the `ZZFeatureMap` is used with two qubits.
             enforce_psd: Project to closest positive semidefinite matrix if x = y.
                 Only enforced when not using the state vector simulator. Default True.
-            batch_size: Number of circuits to batch together for computation. Default 1000.
+            batch_size: Number of circuits to batch together for computation. Default 900.
             quantum_instance: Quantum Instance or Backend
             user_parameters: Iterable containing ``Parameter`` objects which correspond to
                  quantum gates on the feature map circuit which may be tuned. If users intend to
@@ -285,7 +285,7 @@ def _compute_overlap(self, idx, results, is_statevector_sim, measurement_basis)
         """
         if is_statevector_sim:
             # |<0|Psi^dagger(y) x Psi(x)|0>|^2, take the amplitude
-            v_a, v_b = [results.get_statevector(int(i)) for i in idx]
+            v_a, v_b = [results[int(i)] for i in idx]
             tmp = np.vdot(v_a, v_b)
             kernel_value = np.vdot(tmp, tmp).real  # pylint: disable=no-member
         else:
@@ -420,16 +420,21 @@ def evaluate(self, x_vec: np.ndarray, y_vec: np.ndarray = None) -> np.ndarray:
                 is_statevector_sim=is_statevector_sim,
             )
             parameterized_circuit = self._quantum_instance.transpile(parameterized_circuit)[0]
-            circuits = [
-                parameterized_circuit.assign_parameters({feature_map_params: x})
-                for x in to_be_computed_data
-            ]
+            statevectors = []
 
-            results = self._quantum_instance.execute(circuits)
+            for min_idx in range(0, len(to_be_computed_data), self._batch_size):
+                max_idx = min(min_idx + self._batch_size, len(to_be_computed_data))
+                circuits = [
+                    parameterized_circuit.assign_parameters({feature_map_params: x})
+                    for x in to_be_computed_data[min_idx:max_idx]
+                ]
+                results = self._quantum_instance.execute(circuits)
+                for j in range(max_idx - min_idx):
+                    statevectors.append(results.get_statevector(j))
 
             offset = 0 if is_symmetric else len(x_vec)
             matrix_elements = [
-                self._compute_overlap(idx, results, is_statevector_sim, measurement_basis)
+                self._compute_overlap(idx, statevectors, is_statevector_sim, measurement_basis)
                 for idx in list(zip(mus, nus + offset))
             ]
 

releasenotes/notes/batch-circuits-statevector-522a842c6f68d954.yaml

@@ -0,0 +1,8 @@
+---
+fixes:
+  - |
+    Fixed a bug in ``QuantumKernel`` where for statevector simulator
+    all circuits were constructed and transpiled at once, leading to high memory
+    usage. Now the circuits are batched similarly to how it was previously
+    done for non-statevector simulators (same flag is used for both now;
+    previously ``batch_size`` was silently ignored by statevector simulator)