Translate parameterized gates only in gradient calculations (Qiskit/q…

…iskit#9067)

* translate parameterized gates only

* TranslateParameterized to proper pass

* update reno w/ new trafo pass

* Only add cregs if required

* rm unused import

* directly construct DAG

Co-authored-by: Matthew Treinish <[email protected]>

* target support

* qubit-wise support of unrolling

* globally check for supported gates in target

that's because this pass is run before qubit mapping

* lint!

* updates after merge from main

Co-authored-by: Matthew Treinish <[email protected]>
Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com>

qiskit_algorithms/gradients/base_estimator_gradient.py

@@ -22,14 +22,14 @@
 
 import numpy as np
 
-from qiskit import transpile
 from qiskit.algorithms import AlgorithmJob
 from qiskit.circuit import Parameter, ParameterExpression, QuantumCircuit
 from qiskit.opflow import PauliSumOp
 from qiskit.primitives import BaseEstimator
 from qiskit.primitives.utils import _circuit_key
 from qiskit.providers import Options
 from qiskit.quantum_info.operators.base_operator import BaseOperator
+from qiskit.transpiler.passes import TranslateParameterizedGates
 
 from .estimator_gradient_result import EstimatorGradientResult
 from .utils import (
@@ -160,18 +160,15 @@ def _preprocess(
             The list of gradient circuits, the list of parameter values, and the list of parameters.
             parameter_values and parameters are updated to match the gradient circuit.
         """
+        translator = TranslateParameterizedGates(supported_gates)
         g_circuits, g_parameter_values, g_parameter_sets = [], [], []
         for circuit, parameter_value_, parameter_set in zip(
             circuits, parameter_values, parameter_sets
         ):
             circuit_key = _circuit_key(circuit)
             if circuit_key not in self._gradient_circuit_cache:
-                transpiled_circuit = transpile(
-                    circuit, basis_gates=supported_gates, optimization_level=0
-                )
-                self._gradient_circuit_cache[circuit_key] = _assign_unique_parameters(
-                    transpiled_circuit
-                )
+                unrolled = translator(circuit)
+                self._gradient_circuit_cache[circuit_key] = _assign_unique_parameters(unrolled)
             gradient_circuit = self._gradient_circuit_cache[circuit_key]
             g_circuits.append(gradient_circuit.gradient_circuit)
             g_parameter_values.append(

qiskit_algorithms/gradients/base_sampler_gradient.py

@@ -21,12 +21,12 @@
 from collections.abc import Sequence
 from copy import copy
 
-from qiskit import transpile
 from qiskit.algorithms import AlgorithmJob
 from qiskit.circuit import Parameter, ParameterExpression, QuantumCircuit
 from qiskit.primitives import BaseSampler
 from qiskit.primitives.utils import _circuit_key
 from qiskit.providers import Options
+from qiskit.transpiler.passes import TranslateParameterizedGates
 
 from .sampler_gradient_result import SamplerGradientResult
 from .utils import (
@@ -141,18 +141,15 @@ def _preprocess(
             The list of gradient circuits, the list of parameter values, and the list of parameters.
             parameter_values and parameters are updated to match the gradient circuit.
         """
+        translator = TranslateParameterizedGates(supported_gates)
         g_circuits, g_parameter_values, g_parameter_sets = [], [], []
         for circuit, parameter_value_, parameter_set in zip(
             circuits, parameter_values, parameter_sets
         ):
             circuit_key = _circuit_key(circuit)
             if circuit_key not in self._gradient_circuit_cache:
-                transpiled_circuit = transpile(
-                    circuit, basis_gates=supported_gates, optimization_level=0
-                )
-                self._gradient_circuit_cache[circuit_key] = _assign_unique_parameters(
-                    transpiled_circuit
-                )
+                unrolled = translator(circuit)
+                self._gradient_circuit_cache[circuit_key] = _assign_unique_parameters(unrolled)
             gradient_circuit = self._gradient_circuit_cache[circuit_key]
             g_circuits.append(gradient_circuit.gradient_circuit)
             g_parameter_values.append(

qiskit_algorithms/gradients/utils.py

@@ -23,7 +23,6 @@
 
 import numpy as np
 
-from qiskit import transpile
 from qiskit.circuit import (
     ClassicalRegister,
     Gate,
@@ -46,6 +45,7 @@
     RZZGate,
     XGate,
 )
+from qiskit.transpiler.passes.basis import TranslateParameterizedGates
 
 ################################################################################
 ## Gradient circuits and Enum
@@ -211,13 +211,16 @@ def _make_lin_comb_qfi_circuit(
         "y",
         "z",
     ]
-
-    circuit2 = transpile(circuit, basis_gates=supported_gates, optimization_level=0)
+    unroller = TranslateParameterizedGates(supported_gates)
+    circuit2 = unroller(circuit)
 
     qr_aux = QuantumRegister(1, "aux")
-    cr_aux = ClassicalRegister(1, "aux")
     circuit2.add_register(qr_aux)
-    circuit2.add_bits(cr_aux)
+
+    if add_measurement:
+        cr_aux = ClassicalRegister(1, "aux")
+        circuit2.add_bits(cr_aux)
+
     circuit2.h(qr_aux)
     circuit2.data.insert(0, circuit2.data.pop())
 

test/gradients/logging_primitives.py

@@ -0,0 +1,49 @@
+# This code is part of Qiskit.
+#
+# (C) Copyright IBM 2022.
+#
+# 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.
+
+"""Test primitives that check what kind of operations are in the circuits they execute."""
+
+from qiskit.primitives import Estimator, Sampler
+
+
+class LoggingEstimator(Estimator):
+    """An estimator checking what operations were in the circuits it executed."""
+
+    def __init__(
+        self,
+        circuits=None,
+        observables=None,
+        parameters=None,
+        options=None,
+        operations_callback=None,
+    ):
+        super().__init__(circuits, observables, parameters, options)
+        self.operations_callback = operations_callback
+
+    def _run(self, circuits, observables, parameter_values, **run_options):
+        if self.operations_callback is not None:
+            ops = [circuit.count_ops() for circuit in circuits]
+            self.operations_callback(ops)
+        return super()._run(circuits, observables, parameter_values, **run_options)
+
+
+class LoggingSampler(Sampler):
+    """A sampler checking what operations were in the circuits it executed."""
+
+    def __init__(self, operations_callback):
+        super().__init__()
+        self.operations_callback = operations_callback
+
+    def _run(self, circuits, parameter_values, **run_options):
+        ops = [circuit.count_ops() for circuit in circuits]
+        self.operations_callback(ops)
+        return super()._run(circuits, parameter_values, **run_options)

test/gradients/test_estimator_gradient.py

@@ -14,7 +14,6 @@
 """Test Estimator Gradients"""
 
 import unittest
-from test import combine
 
 import numpy as np
 from ddt import ddt, data, unpack
@@ -35,6 +34,8 @@
 from qiskit.quantum_info.random import random_pauli_list
 from qiskit.test import QiskitTestCase
 
+from .logging_primitives import LoggingEstimator
+
 gradient_factories = [
     lambda estimator: FiniteDiffEstimatorGradient(estimator, epsilon=1e-6, method="central"),
     lambda estimator: FiniteDiffEstimatorGradient(estimator, epsilon=1e-6, method="forward"),
@@ -48,7 +49,7 @@
 class TestEstimatorGradient(QiskitTestCase):
     """Test Estimator Gradient"""
 
-    @combine(grad=gradient_factories)
+    @data(*gradient_factories)
     def test_gradient_operators(self, grad):
         """Test the estimator gradient for different operators"""
         estimator = Estimator()
@@ -70,7 +71,7 @@ def test_gradient_operators(self, grad):
         value = gradient.run([qc], [op], [param]).result().gradients[0]
         self.assertAlmostEqual(value[0], correct_result, 3)
 
-    @combine(grad=gradient_factories)
+    @data(*gradient_factories)
     def test_single_circuit_observable(self, grad):
         """Test the estimator gradient for a single circuit and observable"""
         estimator = Estimator()
@@ -86,7 +87,7 @@ def test_single_circuit_observable(self, grad):
         value = gradient.run(qc, op, [param]).result().gradients[0]
         self.assertAlmostEqual(value[0], correct_result, 3)
 
-    @combine(grad=gradient_factories)
+    @data(*gradient_factories)
     def test_gradient_p(self, grad):
         """Test the estimator gradient for p"""
         estimator = Estimator()
@@ -104,7 +105,7 @@ def test_gradient_p(self, grad):
             for j, value in enumerate(gradients):
                 self.assertAlmostEqual(value, correct_results[i][j], 3)
 
-    @combine(grad=gradient_factories)
+    @data(*gradient_factories)
     def test_gradient_u(self, grad):
         """Test the estimator gradient for u"""
         estimator = Estimator()
@@ -125,7 +126,7 @@ def test_gradient_u(self, grad):
             for j, value in enumerate(gradients):
                 self.assertAlmostEqual(value, correct_results[i][j], 3)
 
-    @combine(grad=gradient_factories)
+    @data(*gradient_factories)
     def test_gradient_efficient_su2(self, grad):
         """Test the estimator gradient for EfficientSU2"""
         estimator = Estimator()
@@ -153,7 +154,7 @@ def test_gradient_efficient_su2(self, grad):
             gradients = gradient.run([qc], [op], [param]).result().gradients[0]
             np.testing.assert_allclose(gradients, correct_results[i], atol=1e-3)
 
-    @combine(grad=gradient_factories)
+    @data(*gradient_factories)
     def test_gradient_2qubit_gate(self, grad):
         """Test the estimator gradient for 2 qubit gates"""
         estimator = Estimator()
@@ -178,7 +179,7 @@ def test_gradient_2qubit_gate(self, grad):
                 gradients = gradient.run([qc], [op], [param]).result().gradients[0]
                 np.testing.assert_allclose(gradients, correct_results[i], atol=1e-3)
 
-    @combine(grad=gradient_factories)
+    @data(*gradient_factories)
     def test_gradient_parameter_coefficient(self, grad):
         """Test the estimator gradient for parameter variables with coefficients"""
         estimator = Estimator()
@@ -199,7 +200,7 @@ def test_gradient_parameter_coefficient(self, grad):
             gradients = gradient.run([qc], [op], [param]).result().gradients[0]
             np.testing.assert_allclose(gradients, correct_results[i], atol=1e-3)
 
-    @combine(grad=gradient_factories)
+    @data(*gradient_factories)
     def test_gradient_parameters(self, grad):
         """Test the estimator gradient for parameters"""
         estimator = Estimator()
@@ -218,7 +219,7 @@ def test_gradient_parameters(self, grad):
             gradients = gradient.run([qc], [op], [param], parameters=[[a]]).result().gradients[0]
             np.testing.assert_allclose(gradients, correct_results[i], atol=1e-3)
 
-    @combine(grad=gradient_factories)
+    @data(*gradient_factories)
     def test_gradient_multi_arguments(self, grad):
         """Test the estimator gradient for multiple arguments"""
         estimator = Estimator()
@@ -257,9 +258,7 @@ def test_gradient_multi_arguments(self, grad):
         np.testing.assert_allclose(gradients2[1], correct_results2[1], atol=1e-3)
         np.testing.assert_allclose(gradients2[2], correct_results2[2], atol=1e-3)
 
-    @combine(
-        grad=[FiniteDiffEstimatorGradient, ParamShiftEstimatorGradient, LinCombEstimatorGradient]
-    )
+    @data(FiniteDiffEstimatorGradient, ParamShiftEstimatorGradient, LinCombEstimatorGradient)
     def test_gradient_validation(self, grad):
         """Test estimator gradient's validation"""
         estimator = Estimator()
@@ -318,7 +317,7 @@ def test_spsa_gradient(self):
         gradients = gradient.run([qc], [op], param_list).result().gradients
         np.testing.assert_allclose(gradients, correct_results, atol=1e-3)
 
-    @combine(grad=[ParamShiftEstimatorGradient, LinCombEstimatorGradient])
+    @data(ParamShiftEstimatorGradient, LinCombEstimatorGradient)
     def test_gradient_random_parameters(self, grad):
         """Test param shift and lin comb w/ random parameters"""
         rng = np.random.default_rng(123)
@@ -370,13 +369,11 @@ def test_lin_comb_imag_gradient(self, derivative_type, expected_gradient_value):
         result = gradient.run([c], [Pauli("I")], [[0.0]]).result()
         self.assertAlmostEqual(result.gradients[0][0], expected_gradient_value)
 
-    @combine(
-        grad=[
-            FiniteDiffEstimatorGradient,
-            ParamShiftEstimatorGradient,
-            LinCombEstimatorGradient,
-            SPSAEstimatorGradient,
-        ],
+    @data(
+        FiniteDiffEstimatorGradient,
+        ParamShiftEstimatorGradient,
+        LinCombEstimatorGradient,
+        SPSAEstimatorGradient,
     )
     def test_options(self, grad):
         """Test estimator gradient's run options"""
@@ -427,6 +424,45 @@ def test_options(self, grad):
             # Only default + estimator options. Not run.
             self.assertEqual(options.get("shots"), 200)
 
+    @data(
+        FiniteDiffEstimatorGradient,
+        ParamShiftEstimatorGradient,
+        LinCombEstimatorGradient,
+        SPSAEstimatorGradient,
+    )
+    def test_operations_preserved(self, gradient_cls):
+        """Test non-parameterized instructions are preserved and not unrolled."""
+        x = Parameter("x")
+        circuit = QuantumCircuit(2)
+        circuit.initialize([0.5, 0.5, 0.5, 0.5])  # this should remain as initialize
+        circuit.crx(x, 0, 1)  # this should get unrolled
+
+        values = [np.pi / 2]
+        expect = -1 / (2 * np.sqrt(2))
+
+        observable = SparsePauliOp(["XX"])
+
+        ops = []
+
+        def operations_callback(op):
+            ops.append(op)
+
+        estimator = LoggingEstimator(operations_callback=operations_callback)
+
+        if gradient_cls in [SPSAEstimatorGradient, FiniteDiffEstimatorGradient]:
+            gradient = gradient_cls(estimator, epsilon=0.01)
+        else:
+            gradient = gradient_cls(estimator)
+
+        job = gradient.run([circuit], [observable], [values])
+        result = job.result()
+
+        with self.subTest(msg="assert initialize is preserved"):
+            self.assertTrue(all("initialize" in ops_i[0].keys() for ops_i in ops))
+
+        with self.subTest(msg="assert result is correct"):
+            self.assertAlmostEqual(result.gradients[0].item(), expect, places=5)
+
 
 if __name__ == "__main__":
     unittest.main()

test/gradients/test_qfi.py

@@ -26,6 +26,8 @@
 from qiskit.primitives import Estimator
 from qiskit.test import QiskitTestCase
 
+from .logging_primitives import LoggingEstimator
+
 
 class TestQFI(QiskitTestCase):
     """Test QFI"""
@@ -251,6 +253,34 @@ def test_options(self):
             self.assertEqual(result.options.get("shots"), 300)
             self.assertEqual(options.get("shots"), 200)
 
+    def test_operations_preserved(self):
+        """Test non-parameterized instructions are preserved and not unrolled."""
+        x, y = Parameter("x"), Parameter("y")
+        circuit = QuantumCircuit(2)
+        circuit.initialize([0.5, 0.5, 0.5, 0.5])  # this should remain as initialize
+        circuit.crx(x, 0, 1)  # this should get unrolled
+        circuit.ry(y, 0)
+
+        values = [np.pi / 2, np.pi]
+        expect = np.diag([0.25, 0.5])
+
+        ops = []
+
+        def operations_callback(op):
+            ops.append(op)
+
+        estimator = LoggingEstimator(operations_callback=operations_callback)
+        qfi = LinCombQFI(estimator)
+
+        job = qfi.run([circuit], [values])
+        result = job.result()
+
+        with self.subTest(msg="assert initialize is preserved"):
+            self.assertTrue(all("initialize" in ops_i[0].keys() for ops_i in ops))
+
+        with self.subTest(msg="assert result is correct"):
+            np.testing.assert_allclose(result.qfis[0], expect, atol=1e-5)
+
 
 if __name__ == "__main__":
     unittest.main()