Reproduced Papers

MerLin enables researchers to reproduce and build upon published quantum machine learning research. This section provides implementations of key papers in the quantum ML field, complete with working code, analysis, and extensions.

Overview

Each reproduction may include:

  • Original paper implementation - Faithful recreation of the paper’s methodology

  • Reproduction status - Indicating whether the reproduction is partial or complete

  • Jupyter notebooks - Interactive exploration of results and concepts

  • Full code - Available on GitHub for easy access and modification

  • Performance analysis - Comparison with paper results

  • Extension opportunities - Ideas for building upon the work

Note

All reproductions are implemented using MerLin’s high-level API, making them accessible to ML practitioners without deep quantum expertise.

Available Reproductions

Paper Title

Authors

Year

Status

Description/Category

Fock State Expressivity

Gan et al.

2021

Complete

Foundational work on photonic circuit architectures

Quantum Optical Reservoir Computing Powered by Boson Sampling

Sakurai et al.

2025

In Progress

Boson sampling for quantum reservoir computing

Quantum Large Language Model Fine-Tuning

Jern et al.

2025

In Progress

Fine Tuning a LLM with a photonic neural network

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/reproductions/ directory

Template Structure:

paper_reproduction/
├── README.md             # Paper overview and results
├── implementation.py     # Core implementation
├── notebook.ipynb        # Interactive exploration showing the key concepts, not necessarily the full implementation
├── data/                 # Datasets and preprocessing
├── results/              # Figures and analysis
└── tests/                # Validation tests

Template Summary Page: this document

Recognition

Contributors to reproductions are recognized in:

  • Paper reproduction documentation

  • MerLin project contributors list

  • Academic citations in MerLin publications

Upcoming Reproductions

Near-term (Q2 2025):

Currently accepting proposals

Medium-term (Q3-Q4 2025):

Community voting in progress

Community Requested:

Vote on upcoming reproductions in our paper requests discussions.

Have a paper you’d like to see reproduced? Start a discussion and let us know!