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.
This week I had the opportunity to return to the University of Waterloo, where I had been a visiting scholar while I was a PhD student at MIT, to participate in the Undergraduate School on Experimental Quantum Information Processing program (USEQIP), a unique two-week workshop at the university’s Institute for Quantum Computing (IQC). The program […]