Practical Quantum Acceleration for HPC and AI

Overview

Syllabus

• Introduction to Quantum Computing
Overview of Quantum Computing Principles
Quantum Bits (Qubits) vs. Classical Bits
Types of Quantum Computing: Photonic, Superconducting, Trapped Ion, etc.
• Photonic Quantum Computing
Fundamentals of Photonic Quantum Technology
Advantages and Challenges of Photonics in Quantum Computing
Key Players and Current Technologies
• Quantum Acceleration for High-Performance Computing (HPC)
Identifying Compatible HPC Workloads
Quantum Speedups: Theory vs. Practice
Case Studies: Quantum-Enhanced HPC Applications
• Quantum Acceleration for Artificial Intelligence (AI)
Quantum Machine Learning Concepts
Quantum Algorithms for AI: Examples and Benefits
Benchmarks: Comparing Classical and Quantum AI Solutions
• Practical Applications and Use Cases
Quantum Computing in Data Analysis
Real-world Quantum AI Implementations
Emerging Opportunities and Industries
• Leveraging Quantum Computing in Existing Infrastructures
Integration Strategies for Quantum and Classical Systems
Cloud-Based Quantum Computing: Services and Platforms
Tools and Libraries for Quantum Development
• Challenges and Future Directions
Overcoming Current Limitations of Quantum Computing
Future Trends and Predictions for Quantum in HPC and AI
Preparing for Quantum-Proof Algorithms and Security
• Hands-On Workshops and Exercises
Quantum Circuit Design and Simulation
Building and Running Quantum Algorithms on Photonic Platforms
Collaborative Projects: Real-Life Quantum Use Cases
• Conclusion and Course Review
Recap of Key Concepts and Technologies
Q&A Session and Further Resources
Guidance for Continued Learning in Quantum and AI Fields

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Subjects

Computer Science