Inside quantum optimization research

Optimization is often cited as one of quantum computing’s most promising applications. Businesses and researchers are constantly searching for better routes, schedules, allocations, and ways to allocate limited resources, meaning that a computational advantage for quantum optimization methods could deliver enormous value. Yet despite years of research, fundamental questions remain: Which optimization problems are best suited to quantum computers? And how will we know when quantum methods have truly surpassed their classical counterparts? In this episode of the Coherence Times, we’re joined by IBM’s Stefan Wörner and Stephanie Riedmüller of the Zuse Institute to explore the promise and limitations of quantum optimization. Together, they discuss the search for new quantum algorithms, the importance of benchmarking and community-driven research, and what it will take for quantum computers to deliver practical advantages for real-world optimization problems. Visit The Coherence Times podcast page to get more AI content → https://www.ibm.com/think/podcasts/the-coherence-times The opinions expressed in this podcast are solely those of the participants and do not necessarily reflect the views of IBM or any other organization or entity.