The development of advanced quantum-classical algorithms is among the most prominent strategies in quantum computing. Numerous hybrid solvers have been introduced recently. Many of these methods are created ad hoc to address specific use cases. However, several well-established schemes are frequently utilized to address optimization problems. In this context, D-Wave launched the Hybrid Solver Service in 2020, offering a portfolio of methods designed to accelerate time-to-solution for users aiming to optimize performance and operational processes. Recently, a new technique has been added to this portfolio: the Nonlinear-Program Hybrid Solver. This paper describes this solver and evaluates its performance through a benchmark of 45 instances across three combinatorial optimization problems: the Traveling Salesman Problem, the Knapsack Problem, and the Maximum Cut Problem. To facilitate the use of this relatively unexplored solver, we provide details of the implementation used to solve these three optimization problems.

翻译：先进量子-经典算法的发展是量子计算领域最突出的策略之一。近期已涌现出众多混合求解器。其中许多方法是针对特定用例专门设计的。然而，已有若干成熟方案被广泛应用于解决优化问题。在此背景下，D-Wave于2020年推出混合求解器服务，提供一系列旨在加速用户优化性能与操作流程求解时间的方法。最近，该服务组合新增了一项技术：非线性规划混合求解器。本文描述了该求解器，并通过三个组合优化问题（旅行商问题、背包问题、最大割问题）的45个算例基准测试评估其性能。为促进这一尚未被充分探索的求解器的应用，我们详细阐述了用于解决这三个优化问题的实现方案。