Challenges and opportunities in quantum optimization
Jakub Mareček (Czech Technical University in Prague, Czechia)
Recent advances in quantum computers are demonstrating the ability to solve problems at a scale beyond brute force classical simulation. As such, a widespread interest in quantum algorithms has developed in many areas, with optimization being one of the most pronounced domains. Across computer science and physics, there are a number of different approaches for major classes of optimization problems, such as combinatorial optimization, convex optimization, non-convex optimization, and stochastic extensions. This work draws on multiple approaches to study quantum optimization. Provably exact versus heuristic settings are first explained using computational complexity theory -- highlighting where quantum advantage is possible in each context. Then, the core building blocks for quantum optimization algorithms are outlined to subsequently define prominent problem classes and identify key open questions that, if answered, will advance the field. The effects of scaling relevant problems on noisy quantum devices are also outlined in detail, alongside meaningful benchmarking problems. We underscore the importance of benchmarking by proposing clear metrics to conduct appropriate comparisons with classical optimization techniques. Lastly, we highlight two domains - finance and sustainability - as rich sources of optimization problems that could be used to benchmark, and eventually validate, the potential real-world impact of quantum optimization. The talk is based in part on our survey in Nature Reviews Physics (https://doi.org/10.1038/s42254-024-00770-9), and our earlier papers in Quantum (https://doi.org/10.22331/q-2024-10-10-1495, https://doi.org/10.22331/q-2021-06-17-479). We will also provide a preview of Qiskit Optimization (https://qiskit-community.github.io/qiskit-optimization/).