AI in Quantum Physics: How Machine Learning Solves Scientific Problems

How machine learning methods are applied to quantum physics problems — from wave function optimization to efficient scientific approximations. In this AI Tech Experts Webinar, Marcin Umiński (Data Scientist), explores the connection between quantum physics and machine learning, focusing on practical ways AI can support scientific research. The talk covers: 👉 core concepts of quantum physics (wave functions, energy states, Schrödinger equation) 👉 why exact solutions are computationally expensive and rely on approximations 👉 how machine learning can be applied without losing scientific interpretability 👉 neural networks for variational optimization of wave functions 👉Gaussian processes with Bayesian optimization for efficient exploration 👉delta learning for improving accuracy of approximate methods The key idea: machine learning can accelerate scientific discovery by reducing computational cost while preserving insight into physical systems. If you have questions for Marcin, feel free to comment below. 💭 00:00 AI and quantum physics: Problem framing 02:15 Quantum physics basics and Schrödinger equation 04:34 Computational challenges and approximations 07:44 Applying machine learning to physics problems 12:54 Examples and practical takeaways 🔗 Check out our website: https://deepsense.ai/ 🔗 Linkedin: https://www.linkedin.com/showcase/applied-ai-insider #AIinScience #MachineLearning #QuantumPhysics #ScientificML