This repository presents a structured computational exploration of fundamental concepts in Quantum Computing.
The project focuses on translating theoretical quantum mechanical principles into executable simulations using Python-based scientific tools. Emphasis is placed on mathematical rigor, state representation, and circuit-level modeling to develop a strong foundational understanding suitable for academic and research contexts.
Quantum Computing represents a paradigm shift from classical computation by leveraging superposition, entanglement, and probabilistic measurement.
This project aims to:
- Model qubit systems using linear algebra
- Implement quantum gates through matrix representations
- Simulate multi-qubit interactions
- Analyze measurement probabilities
- Bridge theoretical constructs with computational execution
The notebook serves as an academic reference for structured experimentation.
The implementation is grounded in:
- Complex vector spaces
- Dirac notation and state vectors
- Unitary matrix transformations
- Tensor products for multi-qubit systems
- Probabilistic measurement postulates
All transformations preserve normalization constraints consistent with quantum mechanical principles.
- Python 3.x
- Jupyter Notebook
- NumPy (linear algebra operations)
- Optional: Qiskit / Cirq (circuit simulation frameworks)
Quantum_iisc/
│
├── Quantum_iisc.ipynb # Primary computational notebook
└── README.md # Academic documentation
Quantum-Computing/
│
├── codes/
│ └── quantum_computing_iit_tirupati.py
├── Quantum_iisc.ipynb
└── README.md
The notebook is organized sequentially to reflect theoretical progression.
- Qubit State Representation
- Superposition and Amplitude Analysis
- Quantum Gate Construction
- Multi-Qubit Tensor Product Systems
- Entanglement Modeling
- Measurement and Probability Distribution
- Circuit Simulation Workflows
Each section integrates theoretical explanation with executable code.
The simulations demonstrate:
- State evolution under unitary operations
- Interference patterns in superposed systems
- Entanglement correlations
- Statistical distributions from repeated measurement
Results are verified against expected theoretical outcomes.
This repository is suitable for:
- Undergraduate and postgraduate coursework
- Introductory quantum computing laboratories
- Self-guided foundational research preparation
- Conceptual reinforcement prior to algorithm-level studies
It emphasizes conceptual clarity over framework abstraction.
Potential extensions include:
- Implementation of canonical algorithms (Deutsch–Jozsa, Grover’s, Shor’s preliminary concepts)
- Noise modeling and decoherence simulation
- Integration with real quantum hardware backends
- Comparative analysis of simulator performance
🔹 Launch the notebook
jupyter notebook🔹 Clone the Repository
git clone https://github.com/hemant467/Quantum-Computing.git
cd Quantum-Computing🔹 Navigate to the Code Directory
cd "📟 Codes 📜"🔹 Install dependencies
pip install numpy matplotlib jupyter🔹 Run the Python File
python quantum_computing_iit_tirupati.pyThis project provides a mathematically grounded, implementation-focused study of quantum computational principles. It is designed to cultivate rigorous intuition and prepare learners for advanced quantum algorithm development and research exploration.
- Nielsen, M. A., & Chuang, I. L. Quantum Computation and Quantum Information.
- Preskill, J. Lecture Notes on Quantum Computation.
- Dirac, P. A. M. The Principles of Quantum Mechanics.
Hemant Katta
Independent Research Enthusiast – Quantum Computing & Advanced Computational Systems
“Scientific progress begins with disciplined curiosity.”
