Practical Quantum Acceleration for HPC and AI

Overview

Discover how photonic quantum computing can accelerate HPC and AI workloads through practical applications and advantages.

Syllabus

- Introduction to Quantum Computing -- Overview of quantum computing principles -- Difference between classical and quantum computing -- Quantum bits (qubits) and basic operations - Photonic Quantum Computing -- Explanation of photonic quantum technology -- Advantages of photonic quantum computing over other types -- Key components: photons, waveguides, and detectors - High-Performance Computing (HPC) Fundamentals -- Basics of HPC architectures -- Typical HPC workloads and applications -- Challenges in scaling and performance - AI Workloads and Algorithms -- Overview of AI models and frameworks -- Key AI algorithms: machine learning, deep learning, and neural networks -- Computational demands of AI workloads - Quantum Computing for HPC -- Intersection of quantum computing and HPC -- Practical examples of quantum-accelerated HPC -- Quantum algorithms relevant to HPC - Quantum Computing for AI -- Quantum-enhanced machine learning techniques -- Exploring quantum neural networks -- Case studies of AI acceleration using quantum computing - Practical Applications of Photonic Quantum Computing -- Real-world case studies: Accelerating scientific computing and big data -- Innovations in industry sectors like finance, healthcare, and logistics -- Photonic quantum computing in cloud infrastructure - Tools and Platforms for Quantum Computing -- Overview of existing quantum computing platforms -- Introduction to software tools and SDKs for quantum programming -- Lab exercises: Building and testing quantum circuits - Current Limitations and Future Prospects -- Challenges in quantum hardware and error correction -- Emerging trends and future research directions -- The roadmap for the integration of quantum technologies in HPC and AI - Course Wrap-up and Final Project -- Summary of key concepts and learnings -- Final project: Design a quantum-accelerated solution for a specific HPC or AI challenge -- Presentation and peer review of projects

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