BQP

Quantum Machine Learning Faculty Development Program

Jay Shah from BQP conducted a full-day Quantum Machine Learning Faculty Development Program at SVKM's NMIMS Mukesh Patel School of Technology Management & Engineering. The session introduced quantum computing fundamentals, quantum neural networks, and quantum-assisted physics-informed machine learning, equipping faculty and researchers with practical frameworks to explore emerging quantum-ready computational models.

Key Takeaways

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Delivered advanced training on quantum machine learning frameworks
Introduced Quantum Neural Networks (QNNs) and Quantum-Assisted PINNs (QA-PINNs)
Explained the intersection of quantum computing and artificial intelligence
Strengthened academic collaboration between industry and university research communities
Enabled faculty to explore quantum-ready research and simulation models

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Jay Shah conducted an intensive full-day academic session exploring the foundations of quantum computing and its applications in artificial intelligence and scientific modeling.

The program introduced participants to the evolving landscape of quantum machine learning, including theoretical concepts and practical computational frameworks. Key topics included Quantum AI architectures, Quantum Support Vector Machines, Quantum Natural Language Processing, and Quantum Generative Adversarial Networks.

Participants were also introduced to Physics-Informed Neural Networks (PINNs) and Quantum-Assisted PINNs (QA-PINNs), highlighting how hybrid classical–quantum approaches can improve modeling accuracy for complex physical systems.

The workshop aimed to bridge theory with real-world application, preparing faculty and researchers to engage with emerging quantum computing technologies and integrate quantum-ready methods into their research programs.

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Quantum Machine Learning Faculty Development Program

Contents

Key Takeaways