Reproduced Papers

MerLin provides reproducible implementations of published quantum machine learning papers. Each card links to a dedicated reproduction page with paper metadata, implementation details, code access, and results.

Research Impact

MerLin’s reproductions span published quantum machine learning research; the figures below summarize how many papers have been reproduced and how widely those papers have been cited.

18Papers reproduced
1,097Total citations

Citation data: OpenAlex, as of 2026-08-28.

Available Reproductions

The reproductions are organized by topic. Each card opens the corresponding paper-reproduction page.

Kernel Methods

Photonic Quantum-Enhanced Kernels

Photonic Quantum-Enhanced Kernels

Kernel-based classification on a photonic processor with indistinguishable-versus-distinguishable photon comparisons.

Yin et al.2025CompleteKernel methods
Nearest Centroid Quantum Classification

Nearest Centroid Quantum Classification

Amplitude-encoding nearest-centroid classification with quantum inner-product estimation.

Johri et al.2020CompleteImage TBD

For a Better Understanding of Photonic QML Theory

Fock State-Enhanced Expressivity

Fock State-Enhanced Expressivity

Expressivity study of photonic variational models with increasing photon counts.

Gan et al.2022Complete
HQNN: Myth or Reality?

HQNN: Myth or Reality?

Controlled benchmark of hybrid quantum neural networks versus classical baselines across feature complexity.

Kashif et al.2025Complete
Experimental Data Re-uploading

Experimental Data Re-uploading

Resource-efficient photonic data re-uploading model reproduced on multiple datasets.

Mauser et al.2025Complete
Limitations of Amplitude Encoding

Limitations of Amplitude Encoding

Theoretical and experimental study of amplitude-encoding concentration effects and their impact on quantum classification.

Wang et al.2025Complete
Neural Quantum Embedding

Neural Quantum Embedding

Training the quantum embedding with a classical model to optimize its performance.

Hur et al.2024Complete

Computer Vision

Quantum Optical Reservoir Computing

Quantum Optical Reservoir Computing

Boson-sampling reservoirs for image recognition and scalable photonic reservoir analysis.

Sakurai et al.2025In Progress
Photonic QCNN with State Injection

Photonic QCNN with State Injection

Photonic quantum convolutional architecture with adaptive state injection for vision tasks.

Monbroussou et al.2025Complete
QCNN for Classical Data Classification

QCNN for Classical Data Classification

Quantum pseudo-convolution workflow for PCA-compressed classical image classification.

Hur et al.2022Complete

Sequential Tasks

Quantum Recurrent Neural Networks for Sequential Learning

Quantum Recurrent Neural Networks for Sequential Learning

QRNN for NISQ devices with photonic implementation and evaluation on sequential tasks.

Li et al.2023CompletePennylaneSequential
Quantum Long Short-Term Memory

Quantum Long Short-Term Memory

Quantum LSTM architecture with photonic implementation for sequence and temporal modeling tasks.

Yen-Chi et al.2020CompleteSequential

Advanced Training Paradigms

Quantum LLM Fine-Tuning

Quantum LLM Fine-Tuning

Photonic quantum heads for sentence-embedding classification with baseline-aware evaluation.

Kim et al.2025Complete
Quantum Self-Supervised Learning

Quantum Self-Supervised Learning

SimCLR-style qSSL reproduction with MerLin photonic, Qiskit, and classical backends.

Jaderberg et al.2022Complete
Quantum Adversarial Machine Learning

Quantum Adversarial Machine Learning

Adversarial attacks and defenses for hybrid quantum-classical photonic classifiers.

Lu et al.2020CompleteImage TBD
Photonic Quantum GAN

Photonic Quantum GAN

Photonic QGAN for classical image generation with a MerLin QuantumLayer patch generator.

Sedrakyan and Salavrakos2025In Progress
Hybrid Quantum PINN

Hybrid Quantum PINN

Physics-informed high-speed-flow benchmarks with classical, PennyLane, and MerLin photonic branches.

Leong et al.2025CompletePINNCFD
Quantum Transfer Learning

Quantum Transfer Learning

Hybrid transfer learning with frozen classical feature extractors and trainable quantum heads.

Mari et al.2020CompleteTransfer learning
Quantum Relational Knowledge Distillation

Quantum Relational Knowledge Distillation

Knowledge distillation (KD) is a widely adopted technique for compressing large models into smaller, more efficient student models

Liu et al.2025CompleteKnowledge Distillation

Distributed Training

Distributed Quantum Neural Networks

Distributed Quantum Neural Networks

Distributed photonic parameter generation for CNN compression and MNIST-classification experiments.

Chen et al.2025Complete

Future-proofing

Quantum Memristor Neuromorphic Computing

Quantum Memristor Neuromorphic Computing

Photonic quantum-memristor reservoir model with reproduced nonlinear-task improvements.

Selimovic et al.2025Partial

Reproductions by Citations

The papers reproduced in MerLin, ordered by citation count. Each row links to the reproduction page; the arrow opens the original paper.

PaperAuthorsVenueYearCitations
Transfer Learning in Hybrid Classical-Quantum Neural Networks ↗Mari et al.Quantum2020412
Quantum Convolutional Neural Network for Classical Data Classification ↗Hur et al.Quantum Machine Intelligence2022378
Quantum Adversarial Machine Learning ↗Lu et al.Physical Review Research2020141
Quantum Self-Supervised Learning ↗Jaderberg et al.Quantum Science and Technology202239
Fock State-enhanced Expressivity of Quantum Machine Learning Models ↗Gan et al.EPJ Quantum Technology202231
Experimental Quantum-enhanced Kernel-based Machine Learning on a Photonic Processor ↗Yin et al.Nature Photonics202525
Computational Advantage in Hybrid Quantum Neural Networks: Myth or Reality? ↗Kashif et al.ACM/IEEE DAC202518
Neural Quantum Embedding: Pushing the Limits of Quantum Supervised Learning ↗Hur et al.Physical Review A202418
Quantum Optical Reservoir Computing Powered by Boson Sampling ↗Sakurai et al.Optica Quantum202512
Photonic Quantum Generative Adversarial Networks for Classical Data ↗Sedrakyan and SalavrakosOptica Quantum20248
Nearest Centroid Classification on a Trapped Ion Quantum Computer ↗Johri et al.npj Quantum Information20217
Hybrid Quantum Physics-informed Neural Network: Towards Efficient Learning of High-speed Flows ↗Leong et al.Computers & Fluids20254
Quantum Large Language Model Fine-Tuning ↗Kim et al.IEEE QCE20254
Limitations of Amplitude Encoding on Quantum Classification ↗Wang et al.arXiv preprint20250
Distributed Quantum Neural Networks on Distributed Photonic Quantum Computing ↗Chen et al.arXiv preprint20250
Experimental Neuromorphic Computing Based on Quantum Memristor ↗Selimovic et al.arXiv preprint20250
Photonic Quantum Convolutional Neural Networks with Adaptive State Injection ↗Monbroussou et al.arXiv preprint20250
Experimental Data Re-uploading with Provable Enhanced Learning Capabilities ↗Mauser et al.arXiv preprint2025—

Citation data: OpenAlex, as of 2026-08-28.

Contributing Reproductions

We welcome contributions of additional paper reproductions.

Requirements:

  • High-impact quantum ML papers (>50 citations preferred)

  • Photonic/optical quantum computing focus

  • Implementable with current MerLin features

  • Clear experimental validation

Submission Process:

  1. Propose the paper in our GitHub Discussions

  2. Implement using MerLin following our guidelines

  3. Validate results against original paper

  4. Document in Jupyter notebook format

  5. Submit via pull request a complete reproduction folder and a summary page in docs/source/reproduced_papers/reproductions/ directory

Mandatory Structure for a Reproduction:

papers/NAME/            # Non-ambiguous acronym or fullname of the reproduced paper
├── .gitignore          # specific .gitignore rules for clean repository
├── notebook.ipynb      # Interactive exploration of key concepts
├── README.md           # Paper overview and results overview
├── requirements.txt    # additional requirements for the scripts
├── configs/            # defaults + CLI/runtime descriptors consumed by the repo root runner
├── lib/                # code used by the shared runner and notebooks - as an integrated library (import shared data helpers from papers/shared/<paper>/)
├── models/             # Trained models
├── results/            # Selected generated figures, tables, or outputs from trained models
├── tests/              # Validation tests
└── utils/              # additional commandline utilities for visualization, launch of multiple trainings, etc...

Template Summary Page: this document

Recognition

Contributors to reproductions are recognized in:

  • Paper reproduction documentation

  • MerLin project contributors list

  • Academic citations in MerLin publications

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