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SCIENCE & ENGINEERING CURRICULUM
A complete, day-by-day self-study curriculum covering graduate-level Quantum Science & Engineering
"I always wanted to continue my education in quantum physics and engineering, but I didn't have the time to sit in a university for years. So I decided to build my own path."
— Imran Ali, Founder of Siiea Innovations, LLC
This repository represents a deeply personal journey. As an entrepreneur running a technology company, I faced a choice that many professionals encounter: abandon my dream of studying quantum science at the highest level, or find another way.
I chose to create my own way.
What you're looking at is a complete, 6-year, 10,000+ hour curriculum covering graduate-level quantum science and engineering. Every day file, every problem set, every computational lab was carefully researched and structured to cover topics comparable to those taught at leading university programs.
This is not a shortcut. It's the same rigorous material—just organized for someone who learns on their own schedule.
| Year | Focus | Days | Hours | Status |
|---|---|---|---|---|
| Year 0 | Mathematical & Physical Foundations | 336 | ~2,500 | ✅ COMPLETE |
| Year 1 | Quantum Mechanics Core | 336 | ~2,500 | ✅ COMPLETE |
| Year 2 | Advanced Quantum Science | 336 | ~2,500 | ✅ COMPLETE |
| Year 3 | Qualifying Exam Preparation | 336 | ~2,500 | ✅ COMPLETE |
| Year 4 | Research Phase I | 336 | ~2,500 | ✅ COMPLETE |
| Year 5 | Research Phase II + Thesis | 336 | ~2,500 | ✅ COMPLETE |
Total: 2,016 days | 72 months | ~10,000+ hours | 100% COMPLETE
Quantum Engineering/
│
├── Year_0_Mathematical_Foundations/ # Calculus → Group Theory
│ ├── Semester_0A: Calculus & DiffEq
│ ├── Semester_0B: Linear Algebra & Complex Analysis
│ ├── Semester_0C: Advanced Foundations
│ └── Semester_0D: Scientific Computing & Symmetry
│
├── Year_1_Quantum_Mechanics_Core/ # Postulates → Algorithms
│ ├── Semester_1A: QM Foundations
│ └── Semester_1B: Quantum Information
│
├── Year_2_Advanced_Quantum_Science/ # Error Correction → Hardware
│ ├── Semester_2A: Quantum Error Correction
│ └── Semester_2B: Fault Tolerance & Hardware
│
├── Year_3_Qualifying_Exam/ # Comprehensive Review
│ ├── Semester_3A: Core Knowledge Review
│ └── Semester_3B: Specialization & Mock Exams
│
├── Year_4_Research_Phase_I/ # First Research Project
│ ├── Semester_4A: Research Foundations
│ └── Semester_4B: Original Research
│
└── Year_5_Research_Phase_II/ # Thesis & Defense
├── Semester_5A: Second Research Project
└── Semester_5B: Thesis Writing & Defense
This curriculum covers topics comparable to those in:
| University | Reference Courses | Coverage |
|---|---|---|
| Harvard | QSE 200, QSE 201, Physics 143a/b | Comprehensive |
| MIT | 8.04, 8.05, 8.06, 8.370x | Comprehensive |
| Caltech | Ph125abc, Ph219 | Substantial |
| Princeton | PHY 521, PHY 522 | Substantial |
| Stanford | PHYSICS 130, 131 | Comprehensive |
Each day follows a consistent 7-hour structure:
| Block | Time | Duration | Activity |
|---|---|---|---|
| Morning | 9:00 AM - 12:30 PM | 3.5 hours | Theory & Derivations |
| Afternoon | 2:00 PM - 4:30 PM | 2.5 hours | Problem Solving |
| Evening | 7:00 PM - 8:00 PM | 1 hour | Computational Lab |
From Day 1 of calculus to Day 2016 of thesis defense, every classical concept is explicitly connected to its quantum mechanical application. You never learn something without understanding why it matters for quantum science.
- Stewart, Calculus (8th ed.)
- Axler, Linear Algebra Done Right (4th ed.)
- Taylor, Classical Mechanics
- Kreyszig, Functional Analysis
- Tinkham, Group Theory and Quantum Mechanics
- Shankar, Principles of Quantum Mechanics (2nd ed.)
- Sakurai, Modern Quantum Mechanics (3rd ed.)
- Nielsen & Chuang, Quantum Computation and Quantum Information
- Preskill, Lecture Notes for Physics 219
- Lidar & Brun, Quantum Error Correction
- Wilde, Quantum Information Theory
- Original research papers in your specialization
Navigate to Day 1:
Year_0_Mathematical_Foundations/
Semester_0A_Calculus_DiffEq/
Month_01_Single_Variable_Calculus/
Week_01_Limits_and_Continuity/
Day_001_Monday.md
If you have a math/physics background, take the diagnostic exam in Month 12. Score 80%+ to skip directly to Year 1.
# Python environment (Year 0+)
pip install numpy scipy matplotlib sympy jupyter
# Quantum computing (Year 1+)
pip install qiskit qiskit-aer qiskit-ibm-runtime qutip pennylane
# Apple Silicon optimization (optional — for Mac Studio/MacBook Pro)
pip install mlxThe curriculum is being expanded with runnable Jupyter notebooks for every computational lab — turning static markdown into live, executable science.
| Component | Description | Status |
|---|---|---|
| Year 0 Notebooks | ~96 notebooks: linear algebra, ODE solvers, classical mechanics, group theory | Planned |
| Year 1 Notebooks | ~96 notebooks: wave functions, Bloch sphere, Qiskit circuits, entanglement | Planned |
| Year 2 Notebooks | ~96 notebooks: error correction, surface codes, fault tolerance, noise models | Planned |
| MLX Labs | Apple Silicon-optimized quantum simulation and quantum ML experiments | Planned |
This project is being developed on an Apple Mac Studio (M-series, 512GB unified memory), enabling quantum simulations at scales most personal machines can't reach:
| Qubits | State Vector Size | Feasibility (512GB) |
|---|---|---|
| 20 | 16 MB | Trivial |
| 25 | 512 MB | Easy |
| 30 | 16 GB | Comfortable |
| 33 | 128 GB | Feasible |
| 35 | 512 GB | At the limit |
The MLX Labs will include:
- Quantum neural network decoders — train ML models for error correction
- Variational quantum eigensolvers — hybrid classical-quantum optimization
- Quantum kernel methods — quantum-enhanced machine learning
- Large-scale state vector simulation — push beyond 30 qubits locally
notebooks/
├── Year_0_Foundations/
│ ├── Week_01_Limits.ipynb
│ ├── Week_02_Derivatives.ipynb
│ └── ...
├── Year_1_Quantum/
│ ├── Week_49_Hilbert_Space.ipynb
│ ├── Week_50_Measurement.ipynb
│ └── ...
├── Year_2_Advanced/
│ └── ...
└── MLX_Labs/
├── 01_quantum_neural_decoder.ipynb
├── 02_variational_quantum_eigensolver.ipynb
├── 03_quantum_kernel_methods.ipynb
└── 04_large_scale_simulation.ipynb
This curriculum emphasizes deep understanding over rapid completion:
- Derivations, Not Just Formulas — Know why equations work
- Quantum Connections Throughout — Every classical concept linked to QM
- Modern Applications — Current research and future directions
- Computational Literacy — Python/Qiskit implementations throughout
- Research Preparation — Building toward original contributions
A PhD takes 5-6 years for a reason. This curriculum respects that timeline. You're not racing to a certificate—you're building genuine expertise that will last a lifetime.
- Working professionals who can't attend traditional PhD programs
- Self-learners who want world-class quantum education
- Career changers entering quantum computing/science
- Students supplementing their formal education
- Curious minds who want to truly understand quantum mechanics
- Those seeking quick credentials or certificates
- Anyone looking for shortcuts to quantum understanding
- People who need external accountability to learn
- Those expecting a 6-month bootcamp experience
- Year 0: Mathematical and Physical Foundations (336 days) ✅
- Year 1: Quantum Mechanics Core (336 days) ✅
- Year 2: Advanced Quantum Science (336 days) ✅
- Year 3: Qualifying Exam Preparation (336 days) ✅
- Year 4: Research Phase I (336 days) ✅
- Year 5: Research Phase II + Thesis (336 days) ✅
CURRICULUM 100% COMPLETE
This work is licensed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0).
You may:
- Use this for personal study
- Share it with attribution
- Adapt and build upon the material under the same license
- Reference it in your own work
You may not:
- Use it commercially
- Redistribute without crediting the author
See LICENSE for full terms.
Imran Ali is the founder of Siiea Innovations, LLC, a technology company focused on innovation and education. This curriculum was created as a personal project to prove that world-class education is achievable outside traditional institutions—if you're willing to put in the work.
This curriculum draws from the open educational resources of:
- MIT OpenCourseWare
- IBM Quantum Learning
- John Preskill's Caltech Lecture Notes
- The broader physics and quantum computing community
Special thanks to every educator who makes knowledge freely available.
- MIT OpenCourseWare — 18.01, 18.02, 18.06, 8.04, 8.05, 8.06
- IBM Quantum Learning — Qiskit Textbook
- Preskill's Caltech Notes
- MIT OCW — Full course lectures
- 3Blue1Brown — Visual mathematics
- David Tong's Cambridge lectures
- Python + NumPy/SciPy/SymPy
- Qiskit (IBM Quantum)
- QuTiP (Quantum Toolbox in Python)
- Jupyter Notebooks
After extensive research, no other open-source project offers anything close to this scope. Here's how the landscape breaks down:
| Project | Type | Scope | Original Content |
|---|---|---|---|
| This Curriculum | Day-by-day curriculum | 6 years, 2,016 days | Yes — all lessons, problems, labs |
| OSSU Computer Science | Course link list | CS degree (no quantum) | No — links only |
| Microsoft QuantumKatas | Coding exercises | Single semester, Q# only | Yes — but archived |
| Qiskit Textbook | Jupyter notebooks | Quantum algorithms only | Yes — vendor-locked to IBM |
| awesome-quantum-computing | Link aggregation | Broad but shallow | No — directory only |
| NVIDIA CUDA-Q Academic | GPU quantum labs | Narrow HPC focus | Yes — vendor-specific |
What no one else does:
- Day-by-day structure across 2,016 days
- Full mathematical foundations from calculus (Year 0)
- 6-year PhD-equivalent scope in a single repository
- Integrated research training with thesis preparation (Years 4-5)
- Deep error correction and fault tolerance coverage (Year 2-3)
- Qualifying exam simulation (Year 3)
- Framework-agnostic Python — not locked to any vendor
- Self-contained — all theory, derivations, and code in the repository itself
This is a personal educational project. If you find errors or have suggestions, please open an issue. Pull requests are welcome for typo fixes and corrections.
Begin your quantum journey today.
"The underlying physical laws necessary for the mathematical theory of a large part of physics and the whole of chemistry are thus completely known."
— Paul Dirac (1929)
Created by Imran Ali | Siiea Innovations, LLC
February 2026
