• MerLin 0.3is an open-source framework developed byQuandelafor the systematic exploration of photonic and hybrid quantum machine learning (QML). • Built on the Perceval SDK, it utilizes Strong Linear Optical Simulation (SLOS) to perform exact quantum state computation within a PyTorch-native environment. • The architecture is centered on theQuantumLayer, atorch.nn.Modulethat enables end-to-end differentiable training of linear-optical circuits. • By precomputing sparse photon-number transition graphs, the framework accelerates gradient-based optimization of circuit parameters, such as phase shifters and beam-splitters, directly within standard classical AI pipelines. • The framework supports multiple data encoding methodologies, including angle encoding for Fourier-like feature mapping and amplitude encoding for state-vector initialization.AQuantumBridgeabstraction allows for cross-paradigm architectural comparisons by mapping qubit-based gates into photonic dual-rail or QLOQ encodings. • MerLin is designed for hardware-aware execution through theMerlinProcessorinterface, which facilitates offloading hybrid model components to physical quantum processing units (QPUs), such as Quandela’s Belenos system.

Article Summaries:

  • MerLin 0.3 is an open-source framework developed by Quandela for the systematic exploration of photonic and hybrid quantum machine learning (QML). Built on the Perceval SDK, it utilizes Strong Linear Optical Simulation (SLOS) to perform exact quantum state computation within a PyTorch-native environment. The architecture is centered on the QuantumLayer , a torch.nn.Module that enables end-to-end differentiable training of linear-optical circuits. By precomputing sparse photon-number transition graphs, the framework accelerates gradient-based optimization of circuit parameters, such as phase sh

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