convert to std::optional to avoid (pointers + default value of nullptr);

split setRandomSeed and setRNG into different functions
PennyLaneAI · Sep 30, 2024 · 3047599 · 3047599
1 parent cfb16b4
commit 3047599
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Showing 2 changed files with 15 additions and 10 deletions.
diff --git a/pennylane_lightning/core/src/measurements/MeasurementsBase.hpp b/pennylane_lightning/core/src/measurements/MeasurementsBase.hpp
@@ -76,19 +76,20 @@ template <class StateVectorT, class Derived> class MeasurementsBase {
 
     /**
      * @brief Randomly set the seed of the internal random generator
-     * If catalyst already provides a random generator, use the provided one
      *
-     * @param catalyst_rng Seed
      */
-    void setRandomSeed(const std::mt19937 *&catalyst_rng = nullptr) {
-        if (catalyst_rng != nullptr) {
-            rng = *catalyst_rng;
-            return;
-        }
+    void setRandomSeed() {
         std::random_device rd;
         setSeed(rd());
     }
 
+    /**
+     * @brief Set the internal random generator to an already existing instance
+     *
+     * @param catalyst_rng Seed
+     */
+    void setRNG(std::mt19937 rng) { this->rng = std::move(rng); }
+
     /**
      * @brief Calculate the expectation value for a general Observable.
      *

diff --git a/pennylane_lightning/core/src/simulators/lightning_qubit/measurements/MeasurementsLQubit.hpp b/pennylane_lightning/core/src/simulators/lightning_qubit/measurements/MeasurementsLQubit.hpp
@@ -579,7 +579,7 @@ class Measurements final
      */
     std::vector<std::size_t>
     generate_samples(const std::size_t num_samples,
-                     const std::mt19937 *&catalyst_rng = nullptr) {
+                     const std::optional<std::mt19937> &catalyst_rng) {
         const std::size_t num_qubits = this->_statevector.getNumQubits();
         std::vector<std::size_t> wires(num_qubits);
         std::iota(wires.begin(), wires.end(), 0);
@@ -598,10 +598,14 @@ class Measurements final
     std::vector<std::size_t>
     generate_samples(const std::vector<std::size_t> &wires,
                      const std::size_t num_samples,
-                     const std::mt19937 *&catalyst_rng = nullptr) {
+                     const std::optional<std::mt19937> &catalyst_rng) {
         const std::size_t n_wires = wires.size();
         std::vector<std::size_t> samples(num_samples * n_wires);
-        this->setRandomSeed(catalyst_rng);
+        if (catalyst_rng.has_value()) {
+            this->setRNG(catalyst_rng);
+        } else {
+            this->setRandomSeed();
+        }
         DiscreteRandomVariable<PrecisionT> drv{this->rng, probs(wires)};
         // The Python layer expects a 2D array with dimensions (n_samples x
         // n_wires) and hence the linear index is `s * n_wires + (n_wires - 1 -