Category: Amazon Quantum Solutions Lab

BMW sponsors PhD students to research novel approaches to computational challenges. Today we’ll show you how they bridge the gap between academia and industry, to solve some of the hardest problems in industry using Bayesian protocols for quantum optimization problems.

In this post we show how physics-inspired graph neural networks can be used to solve the notoriously hard graph-coloring problem, at scale. This can help in an huge number of familiar resource-allocation problems from sports to rental cars.

Introduction The problem of robot motion planning is pervasive across many industry verticals, including (for example) automotive, manufacturing, and logistics. In the automotive industry, robotic path optimization problems can be found across the value chain in body shops, paint shops, assembly, and logistics, among others [1]. Typically, hundreds of robots operate in a single plant […]

Suppose you are trying to solve a problem of interest (e.g., portfolio optimization or machine learning), and you are given some classical data, neatly arranged into a matrix A. Your plan is to solve this problem using a quantum algorithm to process the data. For example, you may be trying to solve a linear system […]

Customers looking to solve their hardest computational problems often wonder about the production-readiness of quantum computing. They want to know when a full-scale, fault-tolerant quantum computer will be available, and what the obstacles are to achieving this ambitious goal. Current generation quantum computers are not fault-tolerant and have limited utility, but customers are experimenting with […]

Post Event Update We had a great time in Munich with the BMW Group at the Quantum Computing for Automotive Challenges event on July 20-21. Dr. Helmut Katzgraber, Sr. Practice Manager for the AWS Quantum Solutions Lab, reported: “To date, the application scope of quantum computers for real-world problems remains narrow. This is why identifying […]

Over the last decade, governments and technology companies have invested heavily in research and development of quantum computers that have the potential to revolutionize science and technology. While there is still a long way ahead, these investments have already transformed quantum computers: They have evolved from delicate laboratory systems accessible to only a few research […]

Combinatorial optimization problems, such as the traveling salesman problem where we are looking for an optimal path with a discrete number of variables, are pervasive across science and industry. Practical (and yet notoriously challenging) applications can be found in virtually every industry, such as transportation and logistics, telecommunications, and finance. For example, optimization algorithms help […]

As of 11/17/2022, D-Wave is no longer available on Amazon Braket and has transitioned to the AWS Marketplace. Therefore, information on this page may be outdated. Learn more. Many customers are facing the challenge of efficiently extracting information hidden within complex network structures. For example, a healthcare insurance company needs to identify fraudulent claims through […]

Combinatorial Optimization is one of the most popular fields in applied optimization, and it has various practical applications in almost every industry, including both private and public sectors. Examples include supply chain optimization, workforce and production planning, manufacturing layout design, facility planning, vehicle scheduling and routing, financial engineering, capital budgeting, retail seasonal planning, telecommunication network […]