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7-9 FEBRUARY 2025
TP-1 TURING HALL (8TH FLOOR)
Hands-on mentorship from quantum computing professionals
Connect with like-minded quantum enthusiasts and industry leaders
Work on cutting-edge quantum computing challenges
Win exciting prizes and exclusive swag from sponsors
Collaborative learning through peer discussions, guided sessions, and shared problem-solving
The SRM Quantum Computing Club is a dynamic student organization at SRM University, united by a passion for quantum computing. Through engaging workshops and interactive sessions, we explore quantum mechanics, algorithms, and practical applications. Hands-on learning with cutting-edge platforms like Qiskit and Cirq enhances our problem-solving skills and understanding. Guest lectures and industry interactions keep us updated with the latest advancements. Our inclusive environment welcomes students from all disciplines, fostering lasting friendships and professional connections.
(Quantum ML)
Fuse quantum computing with machine learning to design next-generation intelligent systems. Explore quantum neural networks, variational quantum algorithms, and hybrid quantum-classical models that push the limits of AI performance, optimization, and data representation.
& Cryptography
Engineer the future of secure communication using quantum principles. Explore quantum key distribution (QKD), entanglement-based protocols, and post-quantum security models to design robust, tamper-resistant communication systems for real-world deployment.
& Error Mitigation
Focus on making quantum algorithms practical and scalable on noisy intermediate-scale quantum (NISQ) devices. Explore quantum optimization algorithms (VQE, QAOA), noise modeling, error mitigation techniques, and hardware-aware circuit optimization to improve performance under real-world constraints.
₹15,000
₹10,000
₹5,000
(RECOMMENDED)
Design and simulate a framework to quantify and preserve quantum correlations in two-qubit, multi-qubit, or qubit–qudit systems subjected to amplitude damping and other typical noise channels. Simulate a prepare-and-measure QKD protocol and analyse how channel noise and losses affect key generation rate and security.
Apply QAOA to solve graph partitioning problems, exploring its effectiveness in dividing networks into balanced subsets while minimizing edge cuts.
Develop a hybrid quantum-classical framework that combines quantum computing with classical neural networks to enhance image recognition capabilities.
Implement QAOA to optimize solutions for the classic traveling salesman problem, demonstrating quantum advantage in combinatorial optimization.
Develop a QGNN-based hybrid quantum–classical framework for optimizing job scheduling and production routing in manufacturing systems under multiple operational constraints.






1/5
Originality and creativity of the solution
Quality of code and quantum algorithms used
Real-world applicability and potential impact
Clarity and effectiveness of project demonstration