Physics-inspired AI: Beyond Quantum Principles

The intersection of artificial intelligence and physics has long been a fertile ground for new paradigms, from optimization methods inspired by thermodynamics to algorithms rooted in quantum mechanics. In this workshop, we move beyond the hype of "quantum advantage" and critically examine which principles from quantum physics genuinely inform AI - and which do not. Participants will explore concepts such as superposition, entanglement, and measurement from a computational perspective, while also clarifying their limitations in the context of machine learning. Through hands-on demonstrations with Qiskit and PennyLane, we will implement physics-inspired models, compare classical versus quantum-inspired approaches, and discuss where these methods hold promise for future AI systems. By bridging rigorous theory with practical coding exercises, this workshop provides participants with both a conceptual foundation and applied skills for navigating the emerging field of physics-inspired AI.