Introduction to Quantum Deep Learning

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

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Subjects

Conference Talks