Allow lightning.qubit/kokkos::generate_samples to take in seeds to make the generated samples deterministic

pennylane_lightning/core/_version.py

@@ -16,4 +16,4 @@
    Version number (major.minor.patch[-label])
 """
 
-__version__ = "0.39.0-dev33"
+__version__ = "0.39.0-dev34"

pennylane_lightning/core/src/measurements/MeasurementsBase.hpp

@@ -76,10 +76,15 @@ 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 seed Seed
+     * @param catalyst_rng Seed
      */
-    void setRandomSeed() {
+    void setRandomSeed(const std::mt19937 *&catalyst_rng = nullptr) {
+        if (catalyst_rng != nullptr) {
+            rng = *catalyst_rng;
+            return;
+        }
         std::random_device rd;
         setSeed(rd());
     }

pennylane_lightning/core/src/simulators/lightning_qubit/measurements/MeasurementsLQubit.hpp

@@ -573,30 +573,35 @@ class Measurements final
      * Reference: https://en.wikipedia.org/wiki/Alias_method
      *
      * @param num_samples The number of samples to generate.
+     * @param catalyst_rng The seeded random number generator from catalyst.
      * @return 1-D vector of samples in binary, each sample is
      * separated by a stride equal to the number of qubits.
      */
-    std::vector<std::size_t> generate_samples(const std::size_t num_samples) {
+    std::vector<std::size_t>
+    generate_samples(const std::size_t num_samples,
+                     const std::mt19937 *&catalyst_rng = nullptr) {
         const std::size_t num_qubits = this->_statevector.getNumQubits();
         std::vector<std::size_t> wires(num_qubits);
         std::iota(wires.begin(), wires.end(), 0);
-        return generate_samples(wires, num_samples);
+        return generate_samples(wires, num_samples, catalyst_rng);
     }
 
     /**
      * @brief Generate samples.
      *
      * @param wires Sample are generated for the specified wires.
      * @param num_samples The number of samples to generate.
+     * @param catalyst_rng The seeded random number generator from catalyst.
      * @return 1-D vector of samples in binary, each sample is
      * separated by a stride equal to the number of qubits.
      */
     std::vector<std::size_t>
     generate_samples(const std::vector<std::size_t> &wires,
-                     const std::size_t num_samples) {
+                     const std::size_t num_samples,
+                     const std::mt19937 *&catalyst_rng = nullptr) {
         const std::size_t n_wires = wires.size();
         std::vector<std::size_t> samples(num_samples * n_wires);
-        this->setRandomSeed();
+        this->setRandomSeed(catalyst_rng);
         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 -