Add VJP/JVP capabilities to DefaultQubit2

doc/releases/changelog-dev.md

@@ -88,6 +88,9 @@
 * When given a callable, `qml.ctrl` now does its custom pre-processing on all queued operators from the callable.
   [(#4370)](https://github.com/PennyLaneAI/pennylane/pull/4370)
 
+* The experimental `DefaultQubit2` device now supports computing VJPs and JVPs using the adjoint method.
+  [(#4374)](https://github.com/PennyLaneAI/pennylane/pull/4374)
+
 <h3>Breaking changes 💔</h3>
 
 * `Operator.expand` now uses the output of `Operator.decomposition` instead of what it queues.

pennylane/devices/experimental/default_qubit_2.py

@@ -16,6 +16,7 @@
 """
 
 from functools import partial
+from numbers import Number
 from typing import Union, Callable, Tuple, Optional, Sequence
 import concurrent.futures
 import os
@@ -29,9 +30,9 @@
 
 from . import Device
 from .execution_config import ExecutionConfig, DefaultExecutionConfig
-from ..qubit.simulate import simulate
+from ..qubit.simulate import simulate, get_final_state, measure_final_state
 from ..qubit.preprocess import preprocess, validate_and_expand_adjoint
-from ..qubit.adjoint_jacobian import adjoint_jacobian
+from ..qubit.adjoint_jacobian import adjoint_jacobian, adjoint_vjp, adjoint_jvp
 
 Result_or_ResultBatch = Union[Result, ResultBatch]
 QuantumTapeBatch = Sequence[QuantumTape]
@@ -169,9 +170,10 @@ def supports_derivatives(
         ):
             return True
 
-        if execution_config.gradient_method == "adjoint":
+        if execution_config.gradient_method == "adjoint" and execution_config.use_device_gradient:
             if circuit is None:
                 return True
+
             return isinstance(validate_and_expand_adjoint(circuit), QuantumScript)
 
         return False
@@ -264,24 +266,247 @@ def compute_derivatives(
             self.tracker.update(derivative_batches=1, derivatives=len(circuits))
             self.tracker.record()
 
-        if execution_config.gradient_method == "adjoint":
-            max_workers = self._get_max_workers(execution_config)
-            if max_workers is None:
-                res = tuple(adjoint_jacobian(circuit) for circuit in circuits)
-            else:
-                vanilla_circuits = [convert_to_numpy_parameters(c) for c in circuits]
-                with concurrent.futures.ProcessPoolExecutor(max_workers=max_workers) as executor:
-                    exec_map = executor.map(adjoint_jacobian, vanilla_circuits)
-                    res = tuple(circuit for circuit in exec_map)
+        max_workers = self._get_max_workers(execution_config)
+        if max_workers is None:
+            res = tuple(adjoint_jacobian(circuit) for circuit in circuits)
+        else:
+            vanilla_circuits = [convert_to_numpy_parameters(c) for c in circuits]
+            with concurrent.futures.ProcessPoolExecutor(max_workers=max_workers) as executor:
+                exec_map = executor.map(adjoint_jacobian, vanilla_circuits)
+                res = tuple(circuit for circuit in exec_map)
 
-                # reset _rng to mimic serial behavior
-                self._rng = np.random.default_rng(self._rng.integers(2**31 - 1))
+            # reset _rng to mimic serial behavior
+            self._rng = np.random.default_rng(self._rng.integers(2**31 - 1))
 
-            return res[0] if is_single_circuit else res
+        return res[0] if is_single_circuit else res
 
-        raise NotImplementedError(
-            f"{self.name} cannot compute derivatives via {execution_config.gradient_method}"
-        )
+    def execute_and_compute_derivatives(
+        self,
+        circuits: QuantumTape_or_Batch,
+        execution_config: ExecutionConfig = DefaultExecutionConfig,
+    ):
+        is_single_circuit = False
+        if isinstance(circuits, QuantumScript):
+            is_single_circuit = True
+            circuits = [circuits]
+
+        if self.tracker.active:
+            for c in circuits:
+                self.tracker.update(resources=c.specs["resources"])
+            self.tracker.update(
+                execute_and_derivative_batches=1,
+                executions=len(circuits),
+                derivatives=len(circuits),
+            )
+            self.tracker.record()
+
+        max_workers = self._get_max_workers(execution_config)
+        if max_workers is None:
+            results = tuple(
+                _adjoint_jac_wrapper(c, rng=self._rng, debugger=self._debugger) for c in circuits
+            )
+            results, jacs = tuple(zip(*results))
+        else:
+            self._validate_multiprocessing_circuits(circuits)
+
+            vanilla_circuits = [convert_to_numpy_parameters(c) for c in circuits]
+            seeds = self._rng.integers(2**31 - 1, size=len(vanilla_circuits))
+
+            with concurrent.futures.ProcessPoolExecutor(max_workers=max_workers) as executor:
+                results = tuple(executor.map(_adjoint_jac_wrapper, vanilla_circuits, seeds))
+
+            results, jacs = tuple(zip(*results))
+
+            # reset _rng to mimic serial behavior
+            self._rng = np.random.default_rng(self._rng.integers(2**31 - 1))
+
+        return (results[0], jacs[0]) if is_single_circuit else (results, jacs)
+
+    def supports_jvp(
+        self,
+        execution_config: Optional[ExecutionConfig] = None,
+        circuit: Optional[QuantumTape] = None,
+    ) -> bool:
+        """Whether or not this device defines a custom jacobian vector product.
+
+        ``DefaultQubit2`` supports backpropagation derivatives with analytic results, as well as
+        adjoint differentiation.
+
+        Args:
+            execution_config (ExecutionConfig): The configuration of the desired derivative calculation
+            circuit (QuantumTape): An optional circuit to check derivatives support for.
+
+        Returns:
+            bool: Whether or not a derivative can be calculated provided the given information
+        """
+        return self.supports_derivatives(execution_config, circuit)
+
+    def compute_jvp(
+        self,
+        circuits: QuantumTape_or_Batch,
+        tangents: Tuple[Number],
+        execution_config: ExecutionConfig = DefaultExecutionConfig,
+    ):
+        is_single_circuit = False
+        if isinstance(circuits, QuantumScript):
+            is_single_circuit = True
+            circuits = [circuits]
+            tangents = [tangents]
+
+        if self.tracker.active:
+            self.tracker.update(jvp_batches=1, jvps=len(circuits))
+            self.tracker.record()
+
+        max_workers = self._get_max_workers(execution_config)
+        if max_workers is None:
+            res = tuple(adjoint_jvp(circuit, tans) for circuit, tans in zip(circuits, tangents))
+        else:
+            vanilla_circuits = [convert_to_numpy_parameters(c) for c in circuits]
+            with concurrent.futures.ProcessPoolExecutor(max_workers=max_workers) as executor:
+                res = tuple(executor.map(adjoint_jvp, vanilla_circuits, tangents))
+
+            # reset _rng to mimic serial behavior
+            self._rng = np.random.default_rng(self._rng.integers(2**31 - 1))
+
+        return res[0] if is_single_circuit else res
+
+    def execute_and_compute_jvp(
+        self,
+        circuits: QuantumTape_or_Batch,
+        tangents: Tuple[Number],
+        execution_config: ExecutionConfig = DefaultExecutionConfig,
+    ):
+        is_single_circuit = False
+        if isinstance(circuits, QuantumScript):
+            is_single_circuit = True
+            circuits = [circuits]
+            tangents = [tangents]
+
+        if self.tracker.active:
+            for c in circuits:
+                self.tracker.update(resources=c.specs["resources"])
+            self.tracker.update(
+                execute_and_jvp_batches=1, executions=len(circuits), jvps=len(circuits)
+            )
+            self.tracker.record()
+
+        max_workers = self._get_max_workers(execution_config)
+        if max_workers is None:
+            results = tuple(
+                _adjoint_jvp_wrapper(c, t, rng=self._rng, debugger=self._debugger)
+                for c, t in zip(circuits, tangents)
+            )
+            results, jvps = tuple(zip(*results))
+        else:
+            self._validate_multiprocessing_circuits(circuits)
+
+            vanilla_circuits = [convert_to_numpy_parameters(c) for c in circuits]
+            seeds = self._rng.integers(2**31 - 1, size=len(vanilla_circuits))
+
+            with concurrent.futures.ProcessPoolExecutor(max_workers=max_workers) as executor:
+                results = tuple(
+                    executor.map(_adjoint_jvp_wrapper, vanilla_circuits, tangents, seeds)
+                )
+
+            results, jvps = tuple(zip(*results))
+
+            # reset _rng to mimic serial behavior
+            self._rng = np.random.default_rng(self._rng.integers(2**31 - 1))
+
+        return (results[0], jvps[0]) if is_single_circuit else (results, jvps)
+
+    def supports_vjp(
+        self,
+        execution_config: Optional[ExecutionConfig] = None,
+        circuit: Optional[QuantumTape] = None,
+    ) -> bool:
+        """Whether or not this device defines a custom vector jacobian product.
+
+        ``DefaultQubit2`` supports backpropagation derivatives with analytic results, as well as
+        adjoint differentiation.
+
+        Args:
+            execution_config (ExecutionConfig): A description of the hyperparameters for the desired computation.
+            circuit (None, QuantumTape): A specific circuit to check differentation for.
+
+        Returns:
+            bool: Whether or not a derivative can be calculated provided the given information
+        """
+        return self.supports_derivatives(execution_config, circuit)
+
+    def compute_vjp(
+        self,
+        circuits: QuantumTape_or_Batch,
+        cotangents: Tuple[Number],
+        execution_config: ExecutionConfig = DefaultExecutionConfig,
+    ):
+        is_single_circuit = False
+        if isinstance(circuits, QuantumScript):
+            is_single_circuit = True
+            circuits = [circuits]
+            cotangents = [cotangents]
+
+        if self.tracker.active:
+            self.tracker.update(vjp_batches=1, vjps=len(circuits))
+            self.tracker.record()
+
+        max_workers = self._get_max_workers(execution_config)
+        if max_workers is None:
+            res = tuple(adjoint_vjp(circuit, cots) for circuit, cots in zip(circuits, cotangents))
+        else:
+            vanilla_circuits = [convert_to_numpy_parameters(c) for c in circuits]
+            with concurrent.futures.ProcessPoolExecutor(max_workers=max_workers) as executor:
+                res = tuple(executor.map(adjoint_vjp, vanilla_circuits, cotangents))
+
+            # reset _rng to mimic serial behavior
+            self._rng = np.random.default_rng(self._rng.integers(2**31 - 1))
+
+        return res[0] if is_single_circuit else res
+
+    def execute_and_compute_vjp(
+        self,
+        circuits: QuantumTape_or_Batch,
+        cotangents: Tuple[Number],
+        execution_config: ExecutionConfig = DefaultExecutionConfig,
+    ):
+        is_single_circuit = False
+        if isinstance(circuits, QuantumScript):
+            is_single_circuit = True
+            circuits = [circuits]
+            cotangents = [cotangents]
+
+        if self.tracker.active:
+            for c in circuits:
+                self.tracker.update(resources=c.specs["resources"])
+            self.tracker.update(
+                execute_and_vjp_batches=1, executions=len(circuits), vjps=len(circuits)
+            )
+            self.tracker.record()
+
+        max_workers = self._get_max_workers(execution_config)
+        if max_workers is None:
+            results = tuple(
+                _adjoint_vjp_wrapper(c, t, rng=self._rng, debugger=self._debugger)
+                for c, t in zip(circuits, cotangents)
+            )
+            results, vjps = tuple(zip(*results))
+        else:
+            self._validate_multiprocessing_circuits(circuits)
+
+            vanilla_circuits = [convert_to_numpy_parameters(c) for c in circuits]
+            seeds = self._rng.integers(2**31 - 1, size=len(vanilla_circuits))
+
+            with concurrent.futures.ProcessPoolExecutor(max_workers=max_workers) as executor:
+                results = tuple(
+                    executor.map(_adjoint_vjp_wrapper, vanilla_circuits, cotangents, seeds)
+                )
+
+            results, vjps = tuple(zip(*results))
+
+            # reset _rng to mimic serial behavior
+            self._rng = np.random.default_rng(self._rng.integers(2**31 - 1))
+
+        return (results[0], vjps[0]) if is_single_circuit else (results, vjps)
 
     # pylint: disable=missing-function-docstring
     def _get_max_workers(self, execution_config=None):
@@ -352,3 +577,24 @@ def _validate_multiprocessing_workers(max_workers):
             environment variable `{varname}={num_threads_suggest}`.""",
             UserWarning,
         )
+
+
+def _adjoint_jac_wrapper(c, rng=None, debugger=None):
+    state, is_state_batched = get_final_state(c, debugger=debugger)
+    jac = adjoint_jacobian(c, state=state)
+    res = measure_final_state(c, state, is_state_batched, rng=rng)
+    return res, jac
+
+
+def _adjoint_jvp_wrapper(c, t, rng=None, debugger=None):
+    state, is_state_batched = get_final_state(c, debugger=debugger)
+    jvp = adjoint_jvp(c, t, state=state)
+    res = measure_final_state(c, state, is_state_batched, rng=rng)
+    return res, jvp
+
+
+def _adjoint_vjp_wrapper(c, t, rng=None, debugger=None):
+    state, is_state_batched = get_final_state(c, debugger=debugger)
+    vjp = adjoint_vjp(c, t, state=state)
+    res = measure_final_state(c, state, is_state_batched, rng=rng)
+    return res, vjp

pennylane/devices/qubit/__init__.py

@@ -35,4 +35,4 @@
 from .measure import measure
 from .preprocess import preprocess
 from .sampling import sample_state, measure_with_samples
-from .simulate import simulate
+from .simulate import simulate, get_final_state, measure_final_state