In the future, we will be able to use quantum computers to solve valuable, but hard optimization problems as they arise in different industries and sectors of the economy. In the absence of sufficiently powerful quantum computers (as in 2022), it is possible to use classical optimization techniques, but also optimization methods which have been inspired by quantum physics and quantum effects. These so-called *quantum-inspired optimization* algorithms (QIO) can potentially help solve problems when classical algorithms run out of steam. Importantly, QIO optimization solvers run in the cloud on today’s classical compute infrastructure and therefore don’t require quantum computers. Such QIO techniques are e.g. available in the cloud in Microsoft Azure Quantum. For potential users, various questions will arise: How do these QIO algorithms work in principle? What type of business problems can be solved using such techniques? How to map a business problem into a mathematical description that can serve as input to QIO solvers? Once a mathematically suitable problem specification has been written up, how to work with such QIO solvers in the cloud? Here is an introductory article which addresses a number of these questions and leads the reader through an illustrative worked example to provide a better understanding how QIO methods can be leveraged in practice.

# gk

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