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 …
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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. …
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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 …
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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 …
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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 …
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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, …
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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 …
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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) …
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