51茶馆

Abstract: Despite rapid progress in the performance of State-of-the-Art AI models for quantum, most such methods remain blackboxes: lacking guarantees of robust and reliable predictions that meet uncertainty quantification benchmarks essential in scientific domains. To address this gap, I will summarize a few directions that improve robustness, mechanistic interpretability, and uncertainty quantification of complex learning and sample generation abilities. I will present the simplest model capable of in-context learning, an ability that underpins LLM success, especially for quantum attributes. The emergence of in-context learning ability is studied for the simplest class of tasks: linear regression. The model performance is exactly derived in the joint asymptotic limit of a large number of samples, token dimensions, sample length, and task diversity: exhibiting neural scaling laws and a phase transition from memorization-to-generalization. These results are supported by experiments on standard (full) architectures. Time permitting, I will also introduce a framework hinging on 鈥淩enormalization Group鈥�, a cornerstone of theoretical physics, that systematically coarsegrains data features irrelevant to learning, while capturing nontrivial perturbations to model predictions within scientific uncertainty bounds. Altogether, these works advance AI reliability in a first-principles manner, while bridging AI with fundamental physics.

About the speaker:聽Dr. Anindita Maiti is a Postdoctoral Fellow at the Perimeter Institute for Theoretical Physics, cross-affiliated with Prof. Roger Melko鈥檚 group Perimeter Institute Quantum Intelligence Lab (PIQuIL) since September 2023. Previously, Anindita held a short postdoctoral appointment in physics for ML foundations, mentored by Prof. Cengiz Pehlevan, at Harvard Applied Math (May鈥揂ugust 2023). She earned her PhD in theoretical high-energy physics (string theory and particle theory division) at Northeastern University and the NSF AI Institute for Artificial Intelligence and Fundamental Interactions (IAIFI) in May 2023, under the supervision of Prof. James Halverson. Anindita received her Integrated Bachelors and Masters in Engineering Physics at IIT Bombay in Aug 2017, guided by Prof. Urjit Yajnik. Anindita鈥檚 research lies at the intersection of AI/ML, quantum, and statistical physics. In short, she works on Physics of Learning and ML for Quantum. Broadly, Anindita uses theoretical physics concepts: such as path integrals, renormalization group flow, computational statistics, and random matrix theory, to develop a physics-informed theoretical foundation for ML and to guide the principled design of AI systems. Anindita applies this framework to construct interpretable and trustworthy AI- and ML-based simulation strategies for quantum field theory and quantum many-body physics.