This capstone project focuses on binary operations, logic gates, and computational models to build a foundational system for logic-based computation. Participants will apply programming concepts, Boolean algebra, and circuit design to develop a working computational prototype.

Design and deploy a fully serverless web application using AWS Lambda and DynamoDB. Gain hands-on experience with event-driven architecture, API integration, and cloud scalability.

Design and implement a cloud security framework to detect threats and respond to incidents in real-time. Gain hands-on experience with security monitoring, automation, and compliance in cloud environments.

This capstone project focuses on building a fully interactive text-based RPG using programming fundamentals. Participants will design gameplay mechanics, implement a turn-based system, and integrate procedural content generation while following best practices in software engineering.

Develop a quantum circuit visualization tool.
Implement quantum gates and state evolution.
Simulate quantum operations interactively.
Integrate with quantum frameworks (Qiskit/Cirq).
Analyze quantum state changes and superposition.
Deploy a functional quantum gate simulator.

Design, implement, and optimize a multi-cloud architecture using AWS, Azure, and GCP. Gain hands-on experience in building a resilient, scalable, and cost-effective cloud deployment strategy.

This capstone explores QAOA for solving the MaxCut problem. Participants will implement QAOA, optimize graph partitioning, and compare quantum vs. classical performance using real-world datasets and quantum computing frameworks.

This capstone explores QKNN for classification, leveraging quantum computing to enhance distance-based learning. Participants will implement and analyze QKNN using quantum frameworks and real-world datasets.

Understand quantum optimization fundamentals.
Implement QAOA and quantum annealing algorithms.
Compare classical vs. quantum portfolio strategies.
Work with financial datasets for asset allocation.
Optimize risk-return trade-offs using quantum techniques.
Evaluate performance metrics and real-world applicability.

This capstone explores QSVM for quantum-enhanced classification. Participants will implement QSVM, compare classical vs. quantum SVM, and analyze performance on real-world datasets using quantum computing frameworks.