Skip to article frontmatterSkip to article content

📘 Where to Learn Hybrid Quantum Physics-Informed Neural Networks (HQPINNs)

This guide is designed to help you master the intersection of quantum computing, deep learning, and physics-informed modeling to build HQPINNs from the ground up.


🧭 Step-by-Step Learning Path

1. 🧮 Mathematical & Physical Foundations

Topics:

Resources:


2. 🧠 Deep Learning & Physics-Informed Neural Networks (PINNs)

Topics:

Resources:


3. ⚛️ Quantum Computing & Machine Learning

Topics:

Resources:


4. 🤖 Learn HQPINNs – The Fusion

Topics:

Key Papers:

Implementations:


5. 🧪 Projects for Practice

Tech Stack:


6. 🌐 Communities and Programs


✅ Final Output Ideas


HybridQuantumPINNs

Awesome Quantum Software

List of Open Quantum Projects by QOSF

QUASI

Top Quantum Computing Projects (LibHunt)


C. Albornoz, G. Alonso, M. Andrenkov, P. Angara, A. Asadi, A. Ballon, S. Bapat, L. Botelho, I. De Vlugt, O. Di Matteo, P. Downing, P. Finlay, A. Fumagalli, A. Gardhouse, J. Geoffrion, N. Girard, A. Hayes, J. Izaac, R. Janik, T. Kalajdzievski, A. Kanwar Singh, A. Khomchenko, N. Killoran, I. Kurečić, O. Landon-Cardinal, A. Martin, D. Nino, A. Otto, C. Pere, J. Pickering, K. Renaud, J. Soni, D. Wakeham, L. Young. PennyLane Codebook. 2024. https://pennylane.ai/codebook