Overview
Explore quantum computing's impact on deep learning, including hybrid quantum-classical models, variational circuits, and applications in reinforcement learning and NLP.
Syllabus
-
- Introduction to Quantum Computing
-- Basics of quantum mechanics
-- Quantum bits (qubits) and their properties
-- Quantum gates and circuits
-- Quantum superposition and entanglement
- Fundamentals of Deep Learning
-- Overview of neural networks
-- Training and optimization
-- Common architectures: CNNs, RNNs, and Transformers
- Quantum Computing for Deep Learning
-- Quantum vs classical computation
-- Quantum supremacy and its implications
-- Introduction to quantum algorithms
- Hybrid Quantum-Classical Models
-- Concept and scope of hybrid models
-- Techniques for integrating quantum and classical models
-- Examples of hybrid architectures
- Variational Quantum Circuits
-- Introduction to variational methods
-- Designing variational quantum circuits
-- Applications in machine learning
- Quantum Reinforcement Learning
-- Basics of reinforcement learning
-- Quantum approaches to reinforcement learning
-- Case studies and examples
- Quantum Natural Language Processing (NLP)
-- Introduction to NLP and its challenges
-- Quantum-enhanced NLP models
-- Real-world applications and case studies
- Tools and Platforms for Quantum Deep Learning
-- Overview of quantum computing platforms (e.g., IBM Q, Google Cirq)
-- Software libraries and frameworks
-- Setting up a quantum deep learning environment
- Future Directions and Challenges
-- Current limitations of quantum deep learning
-- Potential breakthroughs and ongoing research
-- Ethical considerations and implications
- Project and Evaluation
-- Hands-on project using quantum deep learning concepts
-- Guidelines for project selection and execution
-- Assessment criteria and feedback process
Taught by
Tags
On-Demand
