YQI Talk - Jacob Watkins & Shouvanik Chakrabarti - JPMorgan

Part 1: Quantum Computing at JPMorganChase (15 mins)

Speaker: Shouvanik Chakrabarti

Abstract: This talk will give an overview of recent research efforts in Quantum Computing at JPMorganChase. We focus on advancements in theory, engineering, and quantum-inspired algorithms building towards quantum enhanced solutions for computational problems in financial engineering, including a recent demonstration of computationally certifiable randomness generated by a trapped-ion quantum processor.

Part 2: On Speedups for Convex Optimization via Quantum Dynamics (40 min)

Speaker: Jacob Watkins

Abstract: I will discuss recent work investigating the potential for quantum speedups in convex optimization through the simulation of quantum dynamics. We establish the first rigorous query complexity bounds simulating the Quantum Hamiltonian Descent (QHD) framework, developing new and improved analyses for quantum simulation of Schrödinger operators using a pseudo- spectral method. While QHD does not offer query complexity improvements over classical methods with access to exact cost function evaluation, we show that the algorithm provides a super-quadratic query advantage, in the high-dimensional regime, over known classical algorithms that tolerate a certain degree of noise in the cost function. Our algorithms also outperform all existing zeroth-order quantum algorithms for noisy (with the same noise tolerance) and stochastic convex optimization in this setting. We attribute the quantum advantage in the noisy setting to the distinct roles of the potential in classical and quantum Hamiltonian dynamics. To the best of our knowledge, these results represent the first rigorous quantum speedups for convex optimization obtained through a dynamical algorithm.