Show Notes
Visit The Coherence Times podcast page to get more AI content → https://www.ibm.com/think/podcasts/the-coherence-times
Aritificial intelligence has transformed the way researchers explore some of the world’s biggest problems, but what happens when machine learning meets quantum computing? The topic of quantum machine learning has generated enormous interest in recent years, but fundamental questions remain: What specifically counts as QML? Where might quantum computers deliver computational advantages over classical AI? What would a convincing demonstration of that advantage actually look like?
In this episode of The Coherence Times, we sit down with IBM Research’s Kunal Sharma and EPFL’s Zoe Holmes to explore the rapidly evolving field of quantum machine learning. Together, they discuss the complex relationship between quantum computing and AI, how AI can help improve quantum algorithms, and what it will take for quantum machine learning to move from theory to practical applications.
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.
Want to learn more about quantum computing and quantum machine learning? Head to the Learning page on IBM Quantum Platform (https://quantum.cloud.ibm.com/). New users can create a free account and get started today.
More from The Coherence Times
Some of the most important problems in science and engineering can be expressed using differential equations, which describe how systems change over time. From electrical circuits to fluid dynamics, plasma physics, and other complex natural phenomena, solving these equations often requires enormous computational resources. Researchers are increasingly exploring whether quantum computers can offer new ways of tackling these mathematical challenges.
One of the biggest misconceptions about quantum computing is that it will eventually replace classical computing, but that’s not the case. *All* quantum computing relies on classical computing, even just for loading data and reading outputs, and many of the most promising quantum use cases involve quantum processing units (QPUs) working alongside the CPUs and GPUs behind powerful classical supercomputers. However, figuring out how these different computing paradigms fit together is easier said than done.
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?
Visit The Coherence Times podcast page to get more quantum content → https://www.ibm.com/think/podcasts/the-coherence-times
Visit The Coherence Times podcast page to get more AI content → https://www.ibm.com/think/podcasts/the-coherence-times