quantum optmization

Quantum- and physics-inspired optimization has attracted lots of attention in recent years, as fundamentally novel paradigms towards solving large-scale combinatorial problems. The underlying principle is to encode a given problem into the natural dynamics of physical systems, and to achieve potentially stronger convergence and higher precision through careful external control. Physics-inspired optimization algorithms, such as coherent Ising machines and simulated bifurcation, mimic the dynamical properties of physical systems on conventional hardware – with the goal to inherit certain features, such as natural convergence towards low-energetic states. At Huawei, we investigate a wide range of topics related to quantum- and physics-inspired optimization, from the most fundamental mathematical structures, deep understanding of physical models, to high-performance implementation of novel optimization architectures.