Today, at TechCrunch Disrupt in San Francisco, I showed a simple machine learning demo, which I ran live on a real quantum computer in New York, through the cloud. Sure, that problem could just as easily have been solved using a classical algorithm on your laptop. But wouldn’t that have been a lot less exciting?
IBM quantum computer
I continue to be amazed by how much progress has been made in a short time; just a few years ago, the very thought of this would have been just a dream. Today, it’s still early days for quantum computing. But systems are getting better and better – and relatively soon, we’ll be in uncharted territory, where we can no longer simulate what the systems are doing. From there, it’s just a matter of time until we start solving at least certain kinds of problems better than we can now using today’s classical systems.
Getting to a future where quantum computers break new ground will require the collective talent and contributions of many brilliant people. If you are excited about this too, then get involved. Whether or not you know it, you have something important to contribute.
Convex optimization problems, which involve the minimization of a convex function over a convex set, can be approximated in theory to any fixed precision in polynomial time. However, practical algorithms are known only for special cases. An important question is whether it is possible to develop algorithms for a broader subset of convex optimization problems that are efficient in both theory and practice.
IBM researchers have partnered with scientists from MIT, Northeastern University, Boston University and University of Minnesota to publish two papers on novel attacks and defenses for graph neural networks and on a new robust training algorithm called hierarchical random switching at IJCAI 2019.
We report new research results relevant to AI planning in our paper, "Depth-First Memory-Limited AND/OR Search and Unsolvability in Cyclic Search Spaces," presented at the International Joint Conference on Artificial Intelligence, IJCAI-19.