Update LQ's MCM treatment following dynamic_one_shot update.

.github/CHANGELOG.md

@@ -4,19 +4,22 @@
 
 ### Breaking changes
 
+* `dynamic_one_shot` uses shot-vectors in the auxiliary tape to tell the device how many times to repeat the tape. Lightning-Qubit is updated accordingly.
+  [(#724)](https://github.com/PennyLaneAI/pennylane-lightning/pull/724)
+
 * `dynamic_one_shot` deals with post-selection during the post-processing phase, so Lightning-Qubit does not return `None`-valued measurements for mismatching samples anymore.
   [(#720)](https://github.com/PennyLaneAI/pennylane-lightning/pull/720)
-  
+
 ### Improvements
 
 * The various OpenMP configurations of Lightning-Qubit are tested in parallel on different Github Actions runners.
   [(#712)](https://github.com/PennyLaneAI/pennylane-lightning/pull/712)
-  
+
 * Update Linux wheels to use manylinux_2_28 images.
   [(#667)](https://github.com/PennyLaneAI/pennylane-lightning/pull/667)
 
 * Add support for `qml.expval` and `qml.var` in the `lightning.tensor` device for the `quimb` interface and the MPS method.
-  [(#686)](https://github.com/PennyLaneAI/pennylane-lightning/pull/686)  
+  [(#686)](https://github.com/PennyLaneAI/pennylane-lightning/pull/686)
 
 * Changed the name of `lightning.tensor` to `default.tensor` with the `quimb` backend.
   [(#719)](https://github.com/PennyLaneAI/pennylane-lightning/pull/719)

pennylane_lightning/core/_version.py

@@ -16,4 +16,4 @@
    Version number (major.minor.patch[-label])
 """
 
-__version__ = "0.37.0-dev6"
+__version__ = "0.37.0-dev7"

pennylane_lightning/lightning_qubit/lightning_qubit.py

@@ -78,11 +78,23 @@ def simulate(circuit: QuantumScript, state: LightningStateVector, mcmc: dict = N
     state.reset_state()
     has_mcm = any(isinstance(op, MidMeasureMP) for op in circuit.operations)
     if circuit.shots and has_mcm:
-        mid_measurements = {}
-        final_state = state.get_final_state(circuit, mid_measurements=mid_measurements)
-        return LightningMeasurements(final_state, **mcmc).measure_final_state(
-            circuit, mid_measurements=mid_measurements
+        results = []
+        aux_circ = qml.tape.QuantumScript(
+            circuit.operations,
+            circuit.measurements,
+            shots=[1],
+            trainable_params=circuit.trainable_params,
         )
+        for _ in range(circuit.shots.total_shots):
+            state.reset_state()
+            mid_measurements = {}
+            final_state = state.get_final_state(aux_circ, mid_measurements=mid_measurements)
+            results.append(
+                LightningMeasurements(final_state, **mcmc).measure_final_state(
+                    aux_circ, mid_measurements=mid_measurements
+                )
+            )
+        return tuple(results)
     final_state = state.get_final_state(circuit)
     return LightningMeasurements(final_state, **mcmc).measure_final_state(circuit)