Hundreds of Quantum Applications

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.

In this talk we present a short overview of recent quantum applications in VW. Using the Quantum Shuttle project as motivation, we show how to construct live quantum optimization services for production applications. Specifically, we showcase a new application—the multi-car paint shop problem—and provide a live demo of this optimization system with real-world data.

Keeping GE’s robust R&D program running presents significant logistical challenges, especially when it comes to asset sustainment. Using D-Wave’s quantum computing system, the company set out to improve resource allocations for machine repairs and minimize resource contention when multiple repairs are requested at once.

As part of their work supporting Innovate UK, British multinational defense, security, and aerospace company BAE Systems undertook an assessment of how to optimize planning and scheduling of operations, distribution, and telecommunications across dozens of major industries using quantum computing.

When BMW began looking for ways to optimize their production lines, they turned to D-Wave. Using quantum algorithms, the company streamlined the complexity and requirements for programming robots used in PVC application and joint sealing.

In addition to creating smarter, safer, more environmentally friendly automotive components, DENSO envisions incorporating quantum computing into everything from production and robotics to scheduling and logistics for product delivery. Work is already underway to make that vision a reality. DENSO recently completed proof-of-concept work using D-Wave's quantum platform, aimed at optimizing control of Automated Guided Vehicles (AGVs) on their factory floors.

In 2019, DENSO and Tohoku University announced the creation of an algorithm designed to improve the ability of a D-Wave system to handle complicated problems. The algorithm partitions an original, large problem into a group of subproblems. The D-Wave system then iteratively optimizes each subproblem to eventually solve the original, larger one. DENSO plans to use the algorithm to address everything from public transportation to the coordination of robotic systems in factories.

Given a set of jobs and a finite number of machines, this demo show how to schedule jobs on those machines such that all the jobs are completed at the earliest possible time.

On the Volkswagen factory floor, the production process creates a series of constantly changing conditions. Delays, shifting wait times, and irregular requests all impact business objectives and outcomes. In order to find optimization solutions, Volkswagen is developing a hybrid algorithm that relies on both classical and quantum approaches to problem solving.

VW is leading the way in the application of quantum computing in the automotive industry. In addition to using D-Wave's 2000Q to optimize factory production, the company has tested a wide range of applications, including the optimization of traffic flow via GPS, the development of neural networks and machine learning for self-driving cars, discovery of new materials for manufacturing, and catalyzing improvements in automotive design.

This is a heuristic approach on how to optimally schedule jobs using a quantum computer. Given a set of jobs and a finite number of machines, how should we schedule the jobs on those machines such that all the jobs are completed at the earliest possible time?

Toyota CRDL developed a hybrid quantum-classical algorithm for model predictive control (MPC). Unlike the usual application scenario for MPC algorithms the research team used the D-Wave system for dynamic control problems in which solutions must be obtained at high speed, specifically stabilization of a spring-mass-damper system, such as the ones in our cars that absorb shock on uneven roads; and dynamic audio quantization for radios and GPS devices. In both cases, the D-Wave method exhibited better performance than two classical approaches.