Hundreds of Quantum Applications

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.

Maintaining steady connections on a mobile network entails a lot of behind-the-scenes effort. Work done by Japanese telecommunications company NTT DOCOMO demonstrated that quantum computing can help mitigate potential performance issues on mobile networks during periods of heavy use. This is now an in-production application for NTT DOCOMO.

Supreeth Mysore Venkatesh, Antonio Macaluso, Marlon Nuske, Matthias Klusch, Andreas Dengel 

Deutsches Forschungszentrum für Künstliche

https://arxiv.org/abs/2408.06007

The increasing number of Low Earth Orbit (LEO) satellites, driven by lower manufacturing and launch costs, is proving invaluable for Earth observation missions and low-latency internet connectivity. However, as the number of satellites increases, the number of communication links to maintain also rises, making the management of this vast network increasingly challenging and highlighting the need for clustering satellites into efficient groups as a promising solution. This paper formulates the clustering of LEO satellites as a coalition structure generation (CSG) problem and leverages quantum annealing to solve it. We represent the satellite network as a graph and obtain the optimal partitions using a hybrid quantum-classical algorithm called GCS-Q. The algorithm follows a top-down approach by iteratively splitting the graph at each step using a quadratic unconstrained binary optimization (QUBO) formulation. To evaluate our approach, we utilize real-world three-line element set (TLE/3LE) data for Starlink satellites from Celestrak. Our experiments, conducted using the D-Wave Advantage annealer and the state-of-the-art solver Gurobi, demonstrate that the quantum annealer significantly outperforms classical methods in terms of runtime while maintaining the solution quality. The performance achieved with quantum annealers surpasses the capabilities of classical computers, highlighting the transformative potential of quantum computing in optimizing the management of large-scale satellite networks.

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.

Supply Chain/ Logistics SavantX with videoQuantum Computing has shown how it can make a difference in optimizing logistics. SavantX developed the HONE (Hyper Optimized Nodal Efficiency) Engine, which uses proprietary data science and quantum computing to present real-time optimized solutions for use at the second largest container terminal at the Port of Los Angeles, Pier 300. SavantX leverages the D-Wave quantum computer repeatedly in transportation applications in real-time. Hear about lessons learned in applying quantum solutions to a real-world application and how quantum compares to the classical approach.

Having the ability to efficiently detect the location where a leak occurs in a network is high on the bucket-list of any water distribution company worldwide. The determination of “how many” pressure-sensors to put “where” in the network is known to be an NP-hard problem. delaware in Belgium joined forces with De Watergroep (Belgium) and KWR (The Netherlands) and developed a tractable approach to determine “how many” and “where”. The QUBO-formulation of this problem allows for a fast determination of the optimal number and location of pressure-sensors via D-Wave’s quantum solver, which now becomes feasible in days instead of centuries. This webinar explains the steps taken and the outcome reached.

The Port of Los Angeles is known as America's busiest port. In a time where we are facing global supply chain challenges, it's essential that the Port operates to its maximum capacity with the highest level of efficiency possible. In conjunction with Fenix Marine Services, SavantX developed and deployed new technology using AI and D-Wave’s quantum computer to optimize terminal operations. The results of their work doubled cargo handling equipment productivity and produced more predictable cargo flows. Watch this webinar to learn more.

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%​.

Dixit, V.V., Niu, C. Quantum computing for transport network design problems. Sci Rep13, 12267 (2023).

Transport network design problem (TNDP) is a well-studied problem for planning and operations of transportation systems. They are widely used to determine links for capacity enhancement, link closures to schedule maintenance, identify new road or transit links and more generally network enhancements under resource constraints. As changes in network capacities result in a redistribution of demand on the network, resulting in changes in the congestion patterns, TNDP is generally modelled as a bi-level problem, which is known to be NP-hard. Meta-heuristic methods, such as Tabu Search Method are relied upon to solve these problems, which have been demonstrated to achieve near optimality in reasonable time. The advent of quantum computing has afforded an opportunity to solve these problems faster. We formulate the TNDP problem as a bi-level problem, with the upper level formulated as a Quadratic Unconstrained Binary Optimization (QUBO) problem that is solved using quantum annealing on a D-Wave quantum computer. We compare the results with Tabu Search. We find that quantum annealing provides significant computational benefit. The proposed solution has implications for networks across different contexts including communications, traffic, industrial operations, electricity, water, broader supply chains and epidemiology.

Satellite image acquisition scheduling is a problem that is omnipresent in the earth observation field; its goal is to find the optimal subset of images to be taken during a given orbit pass under a set of constraints. This problem, which can be modeled via combinatorial optimization, has been dealt with many times by the artificial intelligence and operations research communities. However, despite its inherent interest, it has been scarcely studied through the quantum computing paradigm. Taking this situation as motivation, we present in this paper two QUBO formulations for the problem, using different approaches to handle the non-trivial constraints. We compare the formulations experimentally over 20 problem instances using three quantum annealers currently available from D-Wave, as well as one of its hybrid solvers. Fourteen of the tested instances have been obtained from the well-known SPOT5 benchmark, while the remaining six have been generated ad-hoc for this study. Our results show that the formulation and the ancilla handling technique is crucial to solve the problem successfully. Finally, we also provide practical guidelines on the size limits of problem instances that can be realistically solved on current quantum computers.

A few minutes spent browsing the web is enough to expose most internet users to lots of advertisements, most of which have been targeted to some extent based on the viewer’s preferences and interests. But even if this has become one of the predominant form of marketing in the 21st century, the global market value for TV advertising exceeded $225B in 2022.