track.01
Quantum Machine Learning
Exploring how quantum models outperform classical models.
R&D
Contributing to the Quantum Ecosystem.
Our R&D team works on open-source quantum software, technical prototypes, and research-driven projects that contribute to the quantum ecosystem.
> initializing research workspace
> loading open-source projects
> mapping quantum education tools
> contributors welcome
Research
Research tracks
track.02
Quantum Development Tooling
Building tools for quantum circuit research and development.
track.03
Quantum Chemistry
Investigating quantum algorithms for chemical applications.
Open source
Open-source projects
Quantum-Learn
KUQCI/quantum-learn
A production-ready quantum machine learning library with backend-agnostic APIs for Pennylane and Qiskit, featuring hybrid estimators, variational quantum classifiers, and Hugging Face Hub integration for model sharing.
- Area
- Quantum Machine Learning
- Difficulty
- Intermediate-Advanced
PythonQiskitPennylaneVariational AlgorithmsHybrid MLClassification/Regression
Contribution needs
- -Mature and production-tested library. Open to community contributions for new quantum algorithms, backend optimizations, kernel methods, and experimental features.
QML-Molecular-Atomization-Energy
KUQCI/QML-Molecular-Atomization-Energy
A research-oriented project exploring quantum machine learning approaches for molecular atomization energy prediction using QM7 and variational quantum circuits.
- Area
- Quantum Machine Learning
- Difficulty
- Intermediate-Advanced
PythonQiskitPennyLaneQuantum Machine LearningMachine LearningQuantum ComputingQM7
Contribution needs
- -Project is in testing phase. Contributions welcome once testing is complete.
Quantum-Circuit-Visualizer
KUQCI/Quantum-Circuit-Visualizer
A simplified tool for visualizing and analyzing quantum circuits.
- Area
- Quantum Circuit Analysis
- Difficulty
- Beginner
PythonQiskitQuantum CircuitsVisualization
Contribution needs
- -Building out the initial prototype. Contributions welcome once core features are stabilized.
Introduction-to-Practical-Quantum-Computing
KUQCI/Introduction-to-Practical-Quantum-Computing
A light and intuitive introduction to quantum computing with a focus on applying it through software.
- Area
- Quantum Education
- Difficulty
- Beginner
PythonQiskitQuantum SoftwareLearning
Contribution needs
- -No contributions are required for now while the project scope is being planned.
Contribution
How to get involved
|0> Join QCI -> Explore -> Scope -> Build -> Share
step 1
Join QCI and share your interests.
step 2
Explore the project areas and choose a direction.
step 3
Align with the team on a small, well-scoped task.
step 4
Build a prototype, note, or software contribution.
step 5
Share the result for feedback, publication, or a pull request.