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 forward-looking co-design tool linking algorithms, benchmarks, and hardware.

翻译：摘要：为在近期量子机器学习（QML）中识别量子模型可能提供实际优势的场景，需超越孤立的算法提案，转向涵盖模型、数据集及硬件约束的系统化与实证探索。本文提出MerLin——一个面向光子与混合量子机器学习的开源发现引擎框架。该框架将线性光路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训练。MerLin围绕系统化基准测试与可复现性设计。作为初始贡献，我们复现了涵盖核方法、蓄水池计算、卷积与循环架构、生成模型及现代训练范式的十八项前沿光子与混合量子机器学习工作。这些复现以可复用、模块化的实验形式发布，可直接扩展与适配，从而建立与当代人工智能领域广泛采用的实证基准测试方法论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，MerLin使实践者能够利用现有工具进行消融研究、跨模态对比及经典-量子混合工作流。该框架已实现硬件感知特性，既支持在现有量子硬件上进行测试，又允许探索超越当前硬件能力的功能，使其成为连接算法、基准测试与硬件的前瞻性协同设计工具。