Next-Gen Computing: Quantum Algorithms and Machine Learning

Date: 24-06-2026 to 11-07-2026 (Except Sunday) from 10:00 AM to 12:00 PM.

Course Content

Fundamentals of Quantum Computing

Introduction to classical vs quantum computing – Historical development of quantum computing – Basic postulates of quantum mechanics – Qubits and quantum states – Dirac notation – Bloch sphere representation – Superposition – Quantum measurement – Multi-qubit systems – Tensor products – Quantum entanglement – Bell states – Quantum information concepts.

Quantum Gates and Quantum Circuits

Single qubit gates: Pauli-X, Pauli-Y, Pauli-Z, Hadamard, Phase gates – Rotation gates – Two-qubit gates: Controlled-NOT, SWAP gate – Universal quantum gate sets – Quantum circuit model – Quantum parallelism – No-cloning theorem – Introduction to quantum programming using Qiskit – Building basic quantum circuits and executing them on simulators.

Quantum Algorithms and Machine Learning

Quantum algorithmic principles – Quantum complexity advantage – Deutsch–Jozsa Algorithm – Grover’s Algorithm – Shor’s Algorithm - Variational Quantum Algorithms – Hybrid quantum-classical optimization. Quantum Machine Learning – Quantum data encoding techniques – Quantum feature maps – Parameterized quantum circuits – Variational quantum classifiers – Quantum kernel methods – Quantum Support Vector Machine – Quantum neural networks.

Co-ordinators

Dr. Sudhakar Ilango S Professor & Dean / SCOPE

Dr. Kuppusamy P Professor / SCOPE