Title: Electron Pairing and Fractionalization in the Age of AI and Quantum Computing
Abstract: Electrons in solids can do two remarkable things: they can pair up and flow without resistance, and they can collectively behave as fractionally charged particles. Predicting these phenomena remains a major challenge because the physics spans enormous spatial scales and exponentially large quantum state spaces. I will show how my group uses AI to understand electron pairing in realistic materials: machine-learned Hamiltonians reproduce moiré electronic structures with meV accuracy for systems of up to millions of atoms, enabling studies of superconductivity from weak to strong coupling in twisted semiconductors.
To understand fractionalization, we turn to quantum processors. Constant-depth circuits prepare 18 fractional quantum Hall states on up to 156 superconducting qubits; remarkably, some of the most exotic states are also the easiest to prepare. Together, these efforts show how AI can extend quantum-materials modeling to realistic scales, while quantum processors open access to highly entangled states beyond classical reach.