dynamic_one_shot uses tapes with shot-vectors and jitting takes advantage of it

doc/releases/changelog-dev.md

@@ -11,6 +11,9 @@
 
 <h4>Mid-circuit measurements and dynamic circuits</h4>
 
+* The `dynamic_one_shot` transform uses a single auxiliary tape with a shot vector and `default.qubit` implements the loop over shots with `jax.vmap`.
+  [(#5617)](https://github.com/PennyLaneAI/pennylane/pull/5617)
+
 * The `dynamic_one_shot` transform can be compiled with `jax.jit`.
   [(#5557)](https://github.com/PennyLaneAI/pennylane/pull/5557)
 

pennylane/_qubit_device.py

@@ -275,8 +275,19 @@ def execute(self, circuit, **kwargs):
         self.check_validity(circuit.operations, circuit.observables)
 
         has_mcm = any(isinstance(op, MidMeasureMP) for op in circuit.operations)
-        if has_mcm:
-            kwargs["mid_measurements"] = {}
+        if has_mcm and "mid_measurements" not in kwargs:
+            results = []
+            aux_circ = qml.tape.QuantumScript(
+                circuit.operations,
+                circuit.measurements,
+                shots=[1],
+                trainable_params=circuit.trainable_params,
+            )
+            for _ in circuit.shots:
+                kwargs["mid_measurements"] = {}
+                self.reset()
+                results.append(self.execute(aux_circ, **kwargs))
+            return tuple(results)
         # apply all circuit operations
         self.apply(
             circuit.operations,

pennylane/devices/qubit/simulate.py

@@ -279,9 +279,33 @@ def simulate(
 
     has_mcm = any(isinstance(op, MidMeasureMP) for op in circuit.operations)
     if circuit.shots and has_mcm:
-        return simulate_one_shot_native_mcm(
-            circuit, debugger=debugger, rng=rng, prng_key=prng_key, interface=interface
+        results = []
+        aux_circ = qml.tape.QuantumScript(
+            circuit.operations,
+            circuit.measurements,
+            shots=[1],
+            trainable_params=circuit.trainable_params,
         )
+        keys = jax_random_split(prng_key, num=circuit.shots.total_shots)
+        if qml.math.get_deep_interface(circuit.data) == "jax":
+            # pylint: disable=import-outside-toplevel
+            import jax
+
+            def simulate_partial(k):
+                return simulate_one_shot_native_mcm(
+                    aux_circ, debugger=debugger, rng=rng, prng_key=k, interface=interface
+                )
+
+            results = jax.vmap(simulate_partial, in_axes=(0,))(keys)
+            results = tuple(zip(*results))
+        else:
+            for i in range(circuit.shots.total_shots):
+                results.append(
+                    simulate_one_shot_native_mcm(
+                        aux_circ, debugger=debugger, rng=rng, prng_key=keys[i], interface=interface
+                    )
+                )
+        return tuple(results)
 
     ops_key, meas_key = jax_random_split(prng_key)
     state, is_state_batched = get_final_state(

pennylane/transforms/dynamic_one_shot.py

@@ -87,8 +87,7 @@ def func(x, y):
     few-shots several-mid-circuit-measurement limit, whereas ``qml.defer_measurements`` is favorable
     in the opposite limit.
     """
-
-    if not any(isinstance(o, MidMeasureMP) for o in tape.operations):
+    if not any(is_mcm(o) for o in tape.operations):
         return (tape,), null_postprocessing
 
     for m in tape.measurements:
@@ -103,9 +102,7 @@ def func(x, y):
         raise qml.QuantumFunctionError("dynamic_one_shot is only supported with finite shots.")
 
     samples_present = any(isinstance(mp, SampleMP) for mp in tape.measurements)
-    postselect_present = any(
-        op.postselect is not None for op in tape.operations if isinstance(op, MidMeasureMP)
-    )
+    postselect_present = any(op.postselect is not None for op in tape.operations if is_mcm(op))
     if postselect_present and samples_present and tape.batch_size is not None:
         raise ValueError(
             "Returning qml.sample is not supported when postselecting mid-circuit "
@@ -119,38 +116,33 @@ def func(x, y):
         broadcast_fn = None
 
     aux_tapes = [init_auxiliary_tape(t) for t in tapes]
-    # Shape of output_tapes is (batch_size * total_shots,) with broadcasting,
-    # and (total_shots,) otherwise
-    output_tapes = [at for at in aux_tapes for _ in range(tape.shots.total_shots)]
 
     def processing_fn(results, has_partitioned_shots=None, batched_results=None):
         if batched_results is None and batch_size is not None:
             # If broadcasting, recursively process the results for each batch. For each batch
             # there are tape.shots.total_shots results. The length of the first axis of final_results
             # will be batch_size.
-            results = list(results)
             final_results = []
-            for _ in range(batch_size):
-                final_results.append(
-                    processing_fn(results[0 : tape.shots.total_shots], batched_results=False)
-                )
-                del results[0 : tape.shots.total_shots]
+            for result in results:
+                final_results.append(processing_fn((result,), batched_results=False))
             return broadcast_fn(final_results)
 
         if has_partitioned_shots is None and tape.shots.has_partitioned_shots:
             # If using shot vectors, recursively process the results for each shot bin. The length
             # of the first axis of final_results will be the length of the shot vector.
-            results = list(results)
+            results = list(results[0])
             final_results = []
             for s in tape.shots:
                 final_results.append(
                     processing_fn(results[0:s], has_partitioned_shots=False, batched_results=False)
                 )
                 del results[0:s]
             return tuple(final_results)
+        if not tape.shots.has_partitioned_shots:
+            results = results[0]
         return parse_native_mid_circuit_measurements(tape, aux_tapes, results)
 
-    return output_tapes, processing_fn
+    return aux_tapes, processing_fn
 
 
 @dynamic_one_shot.custom_qnode_transform
@@ -177,6 +169,12 @@ def _dynamic_one_shot_qnode(self, qnode, targs, tkwargs):
     return self.default_qnode_transform(qnode, targs, tkwargs)
 
 
+def is_mcm(operation):
+    """Returns True if the operation is a mid-circuit measurement and False otherwise."""
+    mcm = isinstance(operation, MidMeasureMP)
+    return mcm or "MidCircuitMeasure" in str(type(operation))
+
+
 def init_auxiliary_tape(circuit: qml.tape.QuantumScript):
     """Creates an auxiliary circuit to perform one-shot mid-circuit measurement calculations.
 
@@ -197,11 +195,14 @@ def init_auxiliary_tape(circuit: qml.tape.QuantumScript):
             else:
                 new_measurements.append(m)
     for op in circuit:
-        if isinstance(op, MidMeasureMP):
+        if is_mcm(op):
             new_measurements.append(qml.sample(MeasurementValue([op], lambda res: res)))
 
     return qml.tape.QuantumScript(
-        circuit.operations, new_measurements, shots=1, trainable_params=circuit.trainable_params
+        circuit.operations,
+        new_measurements,
+        shots=[1] * circuit.shots.total_shots,
+        trainable_params=circuit.trainable_params,
     )
 
 
@@ -228,7 +229,7 @@ def measurement_with_no_shots(measurement):
 
     interface = qml.math.get_deep_interface(circuit.data)
 
-    all_mcms = [op for op in aux_tapes[0].operations if isinstance(op, MidMeasureMP)]
+    all_mcms = [op for op in aux_tapes[0].operations if is_mcm(op)]
     n_mcms = len(all_mcms)
     post_process_tape = qml.tape.QuantumScript(
         aux_tapes[0].operations,
@@ -248,7 +249,7 @@ def measurement_with_no_shots(measurement):
     ).reshape((1, -1))
     is_valid = qml.math.all(mcm_samples * has_postselect == postselect, axis=1)
     has_valid = qml.math.any(is_valid)
-    mid_meas = [op for op in circuit.operations if isinstance(op, MidMeasureMP)]
+    mid_meas = [op for op in circuit.operations if is_mcm(op)]
     mcm_samples = [mcm_samples[:, i : i + 1] for i in range(n_mcms)]
     mcm_samples = dict((k, v) for k, v in zip(mid_meas, mcm_samples))
 

tests/devices/default_qubit/test_default_qubit_native_mcm.py

@@ -693,7 +693,7 @@ def func(x, y, z):
         assert "pure_callback" not in jaxpr
 
     func2 = jax.jit(func)
-    results2 = func2(*jax.numpy.array(params))
+    results2 = func2(*params)
 
     measures = [
         qml.probs,

tests/transforms/test_dynamic_one_shot.py

@@ -88,7 +88,7 @@ def test_len_tapes(n_shots):
     """Test that the transform produces the correct number of tapes."""
     tape = qml.tape.QuantumScript([MidMeasureMP(0)], [qml.expval(qml.PauliZ(0))], shots=n_shots)
     tapes, _ = qml.dynamic_one_shot(tape)
-    assert len(tapes) == n_shots
+    assert len(tapes) == 1
 
 
 @pytest.mark.parametrize("n_batch", range(1, 4))
@@ -102,7 +102,7 @@ def test_len_tape_batched(n_batch, n_shots):
         shots=n_shots,
     )
     tapes, _ = qml.dynamic_one_shot(tape)
-    assert len(tapes) == n_shots * n_batch
+    assert len(tapes) == n_batch
 
 
 @pytest.mark.parametrize(
@@ -123,7 +123,7 @@ def test_len_measurements_obs(measure, aux_measure, n_meas):
         [qml.Hadamard(0)] + [MidMeasureMP(0)] * n_mcms, [measure(op=qml.PauliZ(0))], shots=n_shots
     )
     tapes, _ = qml.dynamic_one_shot(tape)
-    assert len(tapes) == n_shots
+    assert len(tapes) == 1
     aux_tape = tapes[0]
     assert len(aux_tape.measurements) == n_meas + n_mcms
     assert isinstance(aux_tape.measurements[0], aux_measure)
@@ -150,7 +150,7 @@ def test_len_measurements_mcms(measure, aux_measure, n_meas):
         shots=n_shots,
     )
     tapes, _ = qml.dynamic_one_shot(tape)
-    assert len(tapes) == n_shots
+    assert len(tapes) == 1
     aux_tape = tapes[0]
     assert len(aux_tape.measurements) == n_meas + n_mcms
     assert isinstance(aux_tape.measurements[0], aux_measure)