Complex optimization problems exist across every industry, such as vehicle routing, supply chain management, portfolio optimization, power grid management, and many others. Optimization algorithms are also at the core of many machine learning methods. These real-world problems are very valuable to solve in order to reduce costs, accelerate processes, or reduce risk.
Quantum computers might be able help solve complex optimization problems, from combinatorial optimization to partial differential equations. Quantum computers can naturally represent random distributions as quantum states, and therefore have the potential to provide better solutions than today’s classical optimization algorithms.