IQUIST Seminar: "Learning Ground State Observables from Quantum Experiments," Kunal Sharma, Senior Research Scientist, IBM

"Learning Ground State Observables from Quantum Experiments"

Abstract: Quantum machine learning can be viewed not only as a search for speedups in classical machine tasks, but also as a way to learn from quantum data generated by quantum processors. In this talk, I will discuss this perspective through recent work on learning ground-state observables from quantum experiments. We use quantum data from approximate ground states of two-dimensional Heisenberg XXZ model, constructed using samples from IBM Heron quantum processors and classical high-performance computing, to train neural networks that predict observables across Hamiltonian parameter space. The results show accurate generalization to unseen parameters, suggesting a path toward using quantum computers as data generators for machine learning in many-body physics.  

Bio: Kunal Sharma is a Senior Research Scientist and Team Manager at IBM Research in Chicago, where he leads work on quantum algorithms, quantum advantage, and quantum machine learning. Prior to joining IBM, he was a Hartree Postdoctoral Fellow at the University of Maryland. He also serves as an Editor for Quantum journal.