Identifying where quantum models may offer practical benefits in near term quantum machine learning (QML) requires moving beyond isolated algorithmic proposals toward systematic and empirical exploration across models, datasets, and hardware constraints. We introduce MerLin, an open source framework designed as a discovery engine for photonic and hybrid quantum machine learning. MerLin integrates optimized strong simulation of linear optical circuits into standard PyTorch and scikit learn workflows, enabling end to end differentiable training of quantum layers. MerLin is designed around systematic benchmarking and reproducibility. As an initial contribution, we reproduce eighteen state of the art photonic and hybrid QML works spanning kernel methods, reservoir computing, convolutional and recurrent architectures, generative models, and modern training paradigms. These reproductions are released as reusable, modular experiments that can be directly extended and adapted, establishing a shared experimental baseline consistent with empirical benchmarking methodologies widely adopted in modern artificial intelligence. By embedding photonic quantum models within established machine learning ecosystems, MerLin allows practitioners to leverage existing tooling for ablation studies, cross modality comparisons, and hybrid classical quantum workflows. The framework already implements hardware aware features, allowing tests on available quantum hardware while enabling exploration beyond its current capabilities, positioning MerLin as a future proof co design tool linking algorithms, benchmarks, and hardware.

翻译：识别量子模型在近期量子机器学习（QML）中可能提供实际优势的应用场景，需要超越孤立的算法提案，转向跨模型、数据集与硬件约束的系统性实证探索。本文介绍MerLin——一个开源框架，其设计目标为光子与混合量子机器学习的研究引擎。MerLin将线性光学电路的优化强模拟集成至标准PyTorch与scikit-learn工作流中，支持量子层的端到端可微分训练。该框架围绕系统性基准测试与可复现性构建。作为初步贡献，我们复现了十八项前沿光子与混合量子机器学习工作，涵盖核方法、储备池计算、卷积与循环架构、生成模型以及现代训练范式。这些复现成果以可复用、模块化的实验形式发布，可直接扩展与适配，从而建立了与现代人工智能领域广泛采用的实证基准测试方法相一致的共享实验基线。通过将光子量子模型嵌入成熟的机器学习生态系统，MerLin使研究者能够利用现有工具进行消融研究、跨模态比较及混合经典-量子工作流探索。该框架已实现硬件感知功能，支持在可用量子硬件上进行测试，同时允许探索超越当前硬件能力的方案，使MerLin成为连接算法、基准测试与硬件的未来适应性协同设计工具。