AWS Quantum Technologies Blog
Author: Martin Schuetz
Martin Schuetz is a Sr. Manager at the Amazon Advanced Solutions Lab, part of the AWS Generative AI Innovation Center - an interdisciplinary team of scientists dedicated to accelerating our customers’ understanding and adoption of advanced technologies. Martin holds a PhD in quantum physics and an M.Sc. in Industrial Engineering. He is a former Fulbright Scholar and Harvard Physics Associate, and worked for several years as an academic researcher with a focus on quantum simulation and quantum optics, at ETH Zurich, the Max Planck Institute for Quantum Optics, and Harvard University. Today, Martin works with customers to help solve some of their hardest problems through scientific innovation, designing and building cutting-edge solutions on AWS.
AWS and JPMorganChase collaborate to advance quantum computing R&D
This post was contributed by Martin Schuetz and Ruben Andrist from the Amazon Advanced Solutions Lab, and Romina Yalovetzky and Atithi Acharya from Global Technology Applied Research at JPMorganChase. Quantum researchers at JPMorganChase and the Amazon Advanced Solutions Lab are working together to explore how quantum technologies may help address complex optimization problems in finance […]
JPMorgan Chase and AWS study the prospects for quantum speedups with near-term Rydberg atom arrays
Want the details on using quantum optimization for finance? New research from Amazon QSL and JPMorgan Chase maps out pathways to quantum advantage. Their post analyzes problem classes inspired by real-world use cases, setting the stage for impactful future experiments.
Exploring industrial use cases in the Airbus-BMW Group Quantum Computing Challenge
Discover how Airbus and BMW Group are harnessing quantum computing to tackle industry challenges. Join the Airbus-BMW Group Quantum Mobility Quest and help shape the future of transportation.
Graph coloring with physics-inspired graph neural networks
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.
Combinatorial Optimization with Physics-Inspired Graph Neural Networks
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 […]
Exploring industrial use cases in the BMW Group Quantum Computing Challenge
Today, the BMW Group launched a global open innovation challenge focused on discovering potential quantum computing solutions for real-world use cases: The BMW Group Quantum Computing Challenge. We are delighted to collaborate with BMW on this challenge, and to invite the quantum community explore new approaches to industrial applications. It’s still early days in quantum […]