Quantum Computing for Engineering Mechanics

Sachin S. Bharadwaj

New York University

Abstract - The past two decades have pointed to a future in which quantum computers may solve certain problems more efficiently than their classical counterparts, owing to substantial gains in memory and computational speed. A more pressing question, however, is whether these advantages can be translated into solutions for practical problems, for instance in engineering mechanics. To begin addressing this question, our work over the last few years has focused on fluid mechanics--a computationally demanding testbed that brings together many of the challenges that arise more broadly across engineering mechanics. In this talk, we begin with a brief pedagogical introduction to the subject, and then discuss the progress we have made so far. A central theme has been the development of end-to-end methods that preserve a net quantum advantage from input to output, while accounting for both the limitations of current and near-term quantum hardware and the fundamental discord between the linearity of quantum mechanics and the nonlinearity of the governing problems. We will highlight several contributions from our work, including efficient quantum algorithms for time-dependent nonlinear differential equations, variational and optimization-based methods, and algorithms to compute physical observables such as energy dissipation and to detect extreme events. These efforts span theory, simulation, and experiments on real quantum devices. We conclude with a forward-looking vision for integrating quantum computing with machine learning through hybrid quantum-classical architectures for future engineering applications.

Speaker Bio -- Dr. Sachin S. Bharadwaj, is presently a postdoctoral associate at the Department of Mechanical and Aerospace Engineering at New York University. He obtained his PhD in 2024, from NYU under the supervision of Prof. Katepalli R. Sreenivasan. Prior to that, he obtained his undergraduate degree in 2019 in Mechanical Engineering and Theoretical Physics & Mathematics from India. He has served as the Chair of the Forum on Graduate Student Affairs (FGSA) at the American Physical Society (APS), during which he has served on various committees and spear-headed important organizational activities, at various levels. His research lies at the intersection of quantum computation and fluid dynamics (QCFD), with a broader focus on integrating quantum computing and machine learning to pave the way for the next-generation, high-performance computational research for engineering applications. Some recognitions include, the APS Five Sigma Physicist Honor, elected full membership of Sigma Xi (Honor Society), as well as being named the 2019 Rhodes Scholarship Finalist (India), jointly by the Rhodes Trust and University of Oxford, UK. He has also been selected as a NATO lecturer (2022 & 2026) at the von Karman Institute for Fluid Dynamics (VKI), Belgium.