Quantum variational algorithms have garnered significant interest recently, due to their feasibility of being implemented and tested on noisy intermediate scale quantum (NISQ) devices. We examine the robustness of the...
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Quantum variational algorithms have garnered significant interest recently, due to their feasibility of being implemented and tested on noisy intermediate scale quantum (NISQ) devices. We examine the robustness of the quantum approximate optimization algorithm (QAOA), which can be used to solve certain quantum control problems, state preparation problems, and combinatorial optimization problems. We demonstrate that the error of QAOA simulation can be significantly reduced by robust control optimization techniques, specifically, by sequential convex programming (SCP), to ensure error suppression in situations where the source of the error is known but not necessarily its magnitude. We show that robust optimization improves both the objective landscape of QAOA as well as overall circuit fidelity in the presence of coherent errors and errors in initial state preparation.
Our research addresses the challenging multi-agent safe control problem where agents must reach their goals while avoiding collisions. Avoidance constraints are enforced within a limited sensing field, adding practica...
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ISBN:
(纸本)9798400704864
Our research addresses the challenging multi-agent safe control problem where agents must reach their goals while avoiding collisions. Avoidance constraints are enforced within a limited sensing field, adding practical relevance to the problem. We propose a novel approach based on tractable Control Lyapunov Function (CLF)-based Quadratic Programs (QPs) for individual agents, enabling goal tracking while considering the dynamics of the obstacles in their limited sensing range. Our framework is highly adaptable, accommodating a large number of agents and ensuring scalability. Extensive experiments with differential drive robots illustrate the computational efficiency and scalability of our approach, even in highly occluded environments with large number of robots.
A joint design approach is proposed for the coexistence of MIMO radars and a communication system, for a scenario in which the targets fall in different range bins. Radar transmit precoding and adaptive communication ...
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ISBN:
(纸本)9781479999897
A joint design approach is proposed for the coexistence of MIMO radars and a communication system, for a scenario in which the targets fall in different range bins. Radar transmit precoding and adaptive communication transmission are adopted, and are jointly designed to maximize signal-to-interference-plus-noise ratio (SINR) at the radar receiver subject to the communication system meeting certain rate and power constraints. We start with the design of a system in which knowledge of the target information is used. Such design can be used to benchmark the performance of schemes that do not use target information. Then, we propose a design which does not require target information. In both cases, the optimization problems are nonconvex with respect to the design variables and have high computational complexity. Alternating optimization and sequential convex programming techniques are used to find a local maximum. Based on the analysis of the obtained solution, we propose a reduced dimensionality design, which has reduced complexity without degrading the radar SINR. Simulation results validate the effectiveness of the proposed spectrum sharing framework.
＜正＞In mechanical design problems, the method of moving asymptotes is a popular tool to solve the arising optimization problems. For large problems, however, the method often needs too much storage and computing tim...
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＜正＞In mechanical design problems, the method of moving asymptotes is a popular tool to solve the arising optimization problems. For large problems, however, the method often needs too much storage and computing time in its traditional formulation. Therefore, a new approach to solve the subproblems has been defined which gives the possibility to exploit sparsity in the original problem and does not create dense structure itself. Several subapproaches with different advantages and disadvantages are possible. The decision for the best possibility can be done by the computer. A central point of this paper will be the description of SCPIP, the FORTRAN77 realization of these approaches. Up to now the method has been mainly applied to weight optimization and topology design.
This paper identifies the two most efficient non-linear programming techniques for the solution of optimization problems of plane structural frames under multiple load systems. The problem is one of minimizing the mas...
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