An Automatic Approach for Combinational Problems on a Hybrid Quantum Architecture

Quantum computing has shown great potential and advantages in solving integer factorization and disordered database search. However, it is not easy to solve specific problems with quantum computing device efficiently and widely, because a lot of professional background knowledge is required. In order to solve this problem, we propose an optimization problem’s automatic hybird quantum framework (OpAQ) for solving user-specified problems on a hybrid computing architecture including both quantum and classical computing resources. Such a solver can allow nonprofessionals who are not familiar with quantum physics and quantum computing to use quantum computing device to solve some classically difficult problems easily. Combinatorial optimization problem is one of the most important problems in both academic and industry. In this paper, we mainly focus on these problems and solve them with OpAQ, which is based on quantum approximation optimization algorithm (QAOA). We evaluate the performance of our approach in solving Graph Coloring, Max-cut, Traveling Salesman and Knapsack Problem. The experimental results show that quantum solver can achieve almost the same optimal solutions with the classical.

Optimization design of rockoons based on improved sequential approximation optimization

In this study, a multidisciplinary design optimization framework and detailed procedure based on improved sequential approximation optimization (SAO) and 3-degree-of-freedom trajectory simulation are proposed for conceptual design and parameters optimization of a rockoon (from rocket and balloon) system. A reliable and efficient strategy, which considers the approximation accuracy of the response surfaces in the sampling process, is proposed to reduce the evaluation times of the original model for finding the global optimal solution. Besides, a modified SAO algorithm based on the multistage adaptive sampling strategy is presented, and the obtained tested results verify the fine robustness, high efficiency, reliability, and validity of the proposed SAO algorithm. The objective is to minimize the liftoff gross mass of the launch vehicle which reflects the cost for satellite launching. Constraints are imposed to ensure the orbit injection accuracy and stability of the launch vehicle. Finally, based on the multidisciplinary design framework with modified SAO, the optimal design results in 12 design cases from various payload mass, objective orbit, and ignition altitude are discussed and compared with the generic launch vehicle.