Stakeholder perspectives of QC in Chemical Engineering

Who: David Neira, Carnegie Mellon University
Thursday, June 30, 2022 - 2:00pm
7316 Wean Hall and Zoom

Abstract: Quantum computing has been attracting public attention recently. This interest is driven by the advancements in hardware, software, and algorithms required for its successful usage and the promise that it entails the potential acceleration of computational tasks compared to classical computing. This perspective talk presents a short review of quantum computing, how this computational approach solves problems, and three fields that quantum computing can potentially impact the most while relevant to chemical engineering: computational chemistry, optimization, and machine learning. Here we present a series of chemical engineering applications, the developments, potential improvements with respect to classical computing, and challenges that quantum computing faces in each field. The first part of this talk intends to provide a clear picture of the challenges and potential advantages that quantum technology may yield for chemical engineering, together with an invitation for our colleagues to join us in the adoption and development of quantum computing. This part corresponds to the recent invited publication on Perspectives on Quantum Computing for Chemical Engineering at the AIChE Journal 

The second half of this talk will focus on the recent development of a hybrid classical-quantum algorithm for addressing combinatorial problems using quantum resources.

Recent years have seen significant advances in quantum/quantum-inspired technologies capable of approximately searching for the ground state of Ising Hamiltonians. The promise of leveraging quantum technology to accelerate the solution of difficult optimization problems has spurred an increased interest in exploring methods to integrate Ising problems as part of their solution process, with existing approaches ranging from direct transcription to hybrid quantum-classical approaches rooted in existing optimization algorithms.

Due to the heuristic and black-box nature of the underlying Ising solvers, many such approaches have limited optimality guarantees. While some hybrid algorithms may converge to global optima, their underlying classical algorithms typically rely on exhaustive search, making it unclear if such algorithmic scaffolds are primed to take advantage of speed-ups that the Ising solver may offer. In the second half of this presentation, we propose a framework for solving mixed-binary quadratic programs (MBQP) to global optimality using black-box and heuristic Ising solvers. We show the exactness of a convex copositive reformulation of MBQPs, which we propose to solve via a hybrid quantum-classical cutting-plane algorithm. The classical portion of this hybrid framework is guaranteed to be polynomial time, suggesting that when applied to NP-hard problems, the complexity of the solution is shifted onto the subroutine handled by the Ising solver.