Welcome to Quantum Algorithm!
This is a collection of my learnings and projects on quantum optimization and machine learning. You can start (Optimization) from here and (Machine Learning) from here. But don't worry, I also provide a overview and practical implementation of the classical optimization and machine learning methodoloies.
Real-World Quantum Applications
Quantum Phase Estimation
- Shor's Algorithm:
In this Jupyter notebook, I implemented Shor’s algorithm from scratch using only Qiskit’s basic quantum gates — no built-in libraries. Successfully factor N = 15.
Grover Search
Coming soon!
Quantum Optimization
- Air Traffic Controller Problem:
This notebook applies quantum optimization (VQE) to a realistic aircraft scheduling problem, considering real aircraft types, wake turbulence, and safety constraints.
It demonstrates how quantum algorithms can tackle complex logistics problems — with future potential in operational aviation systems.
Learning Notes
Quantum Optimization and Machine Learning
If you are familiar with classical optimization or machine learning methods, you can start quantum world, please follow any one of these links down below.
Optimization
- Quadratic Unconstrained Binary Optimization (QUBO)
- Adiabatic Quantum Computing & Quantum Annealing (AQQA)
- Quantum Approximate Optimization Algorithm (QAOA)
- Grover Adaptive Search (GAS)
- Variational Quantum Eignesolver (VQE)
Machine Learning
- Quantum Support Vector Machines (QSVM)
- Quantum Neural Networks (QNN)
- Quantum Hybrid Architectures (QHybrid)
- Quantum Generative Adversarial Networks (QGANs)
Classical Optimization and Machine Learning
If you are not familiar with both, you are welcome to start with any one of classical methods below.