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eybmits/README.md

Markus Baumann

I'm Markus Baumann, a researcher at LMU Munich / QAR-Lab working on mathematical and stochastic models for time-series forecasting, Quantum Reservoir Computing, and Quantum Machine Learning.

I am interested in how excellent science can be done in practice: reproducible experiments, clear documentation, visible negative results, and claims that stay tied to the artifacts and baselines behind them.

I also explore how AI can improve this workflow by helping document, verify, and refine research processes without replacing scientific judgment.

LMU Munich QAR-Lab Time Series Forecasting Quantum Reservoir Computing Quantum Machine Learning Open Source

Canonical links

Current research focus

  • Mathematical and stochastic models for temporal data, uncertainty, and dynamical systems.
  • Time-series forecasting and evaluation under stochastic and nonlinear dynamics.
  • Quantum Reservoir Computing for temporal processing and forecasting tasks.
  • Quantum Machine Learning and mathematical models of quantum information processing.
  • Statistical evaluation, uncertainty quantification, and reproducible computational research.
  • How modern research workflows can be accelerated with AI while keeping verification and reproducibility central.

๐Ÿ”ฌ Open Research

Project Focus Repository
๐Ÿงญ QRC Operating Band Reproducibility package for "Where a Quantum Reservoir Works: A Transferable Operating Band". qrc-operating-band
๐Ÿชž VQML Symmetry Reproducibility package for symmetry-aware variational quantum machine learning experiments. vqml-symmetry
๐Ÿงฌ IQP Generative Modeling Reproducible result packages and figure pipelines for parity-structured quantum generative modeling. iqp-generative-modeling
๐Ÿงฎ Hamiltonian Implicit Differentiation Implicit-differentiation experiments for bilevel Max-Cut optimization. hamiltonian-implicit-differentiation
๐Ÿ“š Statistically Learnable QRC Archival research project on statistically learnable Quantum Reservoir Computing. qrc-statistical-learning

๐Ÿงฉ Projects

Project Focus Repository
๐ŸŽง VibeTracker Mood-adaptive music validation dashboard and experiment app. vibe-tracker
๐ŸŒฑ Longevity Logbook Terminal-first training logbook with editable strength programming. longevity-logbook
โฑ๏ธ 100x100 Pomodoro Minimal Pomodoro tracker for repeated focused work sessions. pomodoro-100x100
๐Ÿง  Cognitive Decision Tree Animated decision-tree MVP for low-friction cognitive-state self-assessment. cognitive-decision-tree

๐Ÿ› ๏ธ Documentation and tools

Project Focus Repository
๐Ÿ  Research Website Public website and research identity hub. research-website
๐ŸŒ GitHub Pages site Live deployment target for the public research website. eybmits.github.io
๐ŸŽ“ QRC Learning Platform Learning site for Quantum Reservoir Computing concepts and flashcards. qrc-learning-platform
๐Ÿ” Spaced-Repetition Tutor Spaced-repetition and LLM tutoring loops for learning workflows. spaced-repetition-tutor
โœ๏ธ macOS Writing Assistant Sentence-level German and English correction with fast undo. writing-assistant-macos

Popular repositories Loading

  1. vibe-shuffle vibe-shuffle Public

    Mood-adaptive music validation dashboard for the Vibe Shuffle experiment.

    TeX 1

  2. iqp-generative-modeling iqp-generative-modeling Public

    Reproducible IQP quantum generative modeling figures, result packages, and checksums.

    Python

  3. hamiltonian-implicit-differentiation hamiltonian-implicit-differentiation Public

    Implicit-differentiation experiments for parametrized Hamiltonians and bilevel Max-Cut optimization.

    Python

  4. qrc-learning-platform qrc-learning-platform Public

    Interactive Quantum Reservoir Computing learning platform with spaced-repetition flashcards.

    TypeScript

  5. research-website research-website Public

    Source for Markus Baumann's public research website and identity hub.

    Astro

  6. eybmits.github.io eybmits.github.io Public

    Live personal website for Markus Baumann, researcher in quantum reservoir computing and quantum machine learning.

    HTML