Hundreds of Quantum Applications

Pattison Food Group has over 100 stores that offer e-commerce delivery. 3-4 people are dedicated to manually creating driver schedules each week. Pattison worked with D-Wave to create these schedules automatically, taking scheduling rules (seniority, preferences/history, and policies) into account. As a result, weekly manual efforts for initial scheduling creation was reduced by ~80%​.

Employing D-Wave's annealing quantum computing solutions as accelerators in the speed and quality of training our generative AI-driven, drug discovery (DD) analysis systems, we constructed a hybrid quantum and classical computer system, aiming to extend the applicable range of molecular-generation AI systems in the DD field (i.e., AI-driven Drug Design). The goal of our project is to pioneer a new process for discovering 'first-in-class' pharmaceutical small compounds, which is performed with supports by D-Wave's team of professional services experts. In my talk, we report substantial aspects of our projects, based on the above-mentioned strategy and present progress.

Abhishek Awasthi∗, Nico Kraus†, Florian Krellner‡, David Zambrano†

*BASF Digital Solutions GmbH, Ludwigshafen am Rhein, Germany

†Aqarios GmbH, Munich, Germany

‡SAP SE, Walldorf, Germany

This work is a benchmark study for quantum- classical computing method with a real-world optimization problem from industry. The problem involves scheduling and balancing jobs on different machines, with a non-linear objective function. We first present the motivation and the problem description, along with different modeling techniques for classical and quantum computing. The modeling for classical solvers has been done as a mixed-integer convex program, while for the quantum-classical solver we model the problem as a binary quadratic program, which is best suited to the D-Wave Leap’s Hybrid Solver. This ensures that all the solvers we use are fetched with dedicated and most suitable model(s). Henceforth, we carry out benchmarking and comparisons between classical and quantum-classical methods, on problem sizes ranging till approximately 150, 000 variables. We utilize an industry grade classical solver and compare its results with D-Wave Leap’s Hybrid Solver. The results we obtain from D-Wave are highly competitive and sometimes offer speedups, compared to the classical solver.