Exploring the potential for quantum advantage in mathematical optimization

This week, the IBM Quantum Blog digs into perhaps one of the most misunderstood and controversial domains in quantum algorithms research: quantum optimization.

The blog details key takeaways from a white paper recently published by the Quantum Optimization Working Group in Nature Reviews Physics. Their paper provides an overview of existing quantum optimization methods and explores the potential for quantum advantage in a variety of optimization problem settings. The blog also details promising research from IBM®, Los Alamos National Lab, and the University of Basel, which shows how applying Conditional Value at Risk (CVaR) to samples returned from a quantum computer may be useful for various optimization tasks.

Blog: Exploring the potential for quantum advantage in mathematical optimization

Learn more about incorporating CVaR into your optimization workflows in the Advanced Techniques for QAOA tutorial on IBM Quantum Learning.