Speaker: Kumal Sharma, Senior Research Scientist, Manager, Quantum Algorithms Team
Title: Toward Quantum Advantage for Ground State Problems
Abstract: This talk will discuss recent progress toward realizing quantum-centric supercomputing, where quantum processors and classical supercomputers jointly push the computational frontiers of many-body physics and chemistry. I will begin by highlighting a series of advances showing how hybrid quantum-classical workflows can already tackle correlated electron problems previously considered intractable for current quantum processors. I will then present our Sample-based Krylov Quantum Diagonalization (SKQD) algorithm — an approach to ground-state approximation that combines theoretically provable convergence with experimental scalability. Using IBM’s Heron processor, we have applied SKQD to strongly correlated impurity models involving up to 85 qubits and 6000 entangling gates, achieving accuracy comparable to density-matrix renormalization group methods. Finally, I will discuss the prospects for demonstrating quantum advantage in ground-state approximation, expectation-value estimation, and peaked-circuit experiments, and how new algorithmic ideas are needed to tolerate the noise present in current and early-FTQC processors.
Bio: Kunal Sharma is a Senior Research Scientist at IBM Quantum Chicago. Previously, he worked at IBM’s T. J. Watson Research Center in Yorktown Heights. Before joining IBM, he was a Hartree Postdoctoral Fellow at the Joint Center for Quantum Information and Computer Science (QuICS) at the University of Maryland. He completed his PhD under Mark Wilde at Louisiana State University, which included a year at Los Alamos National Lab. His research spans quantum algorithms for simulating physical systems, quantum advantage experiments, and quantum machine learning.