由量子近似优化算法增强的压缩感知
Compressed sensing enhanced by quantum approximate optimization algorithm
AI总结：
本文提出将 QAOA 作为量子子程序嵌入匹配追踪，利用结构化约束把检测优化归约为 Ising 模型，以增强大规模压缩感知；模拟显示其优于参考经典方法。
摘要：
我们提出了一个使用量子子程序处理一系列大规模压缩感知问题的框架。我们应用量子近似优化算法（QAOA）来支持稀疏信号重建算法——匹配追踪（matching pursuit）中的检测。当问题规模较大且约束由非结构化模式给出时，该算法所需的约束优化难以处理。我们的框架利用专门设计的、易于操控的结构化约束，并将优化问题归约为 Ising 模型的求解，而该模型可使用 Ising 求解器找到。在本研究中，我们在量子计算机模拟器上测试了 QAOA 用于此目的的性能。我们观察到，我们的方法可以优于参考的经典方法。我们的结果探索了一条将量子计算机应用于压缩感知领域的有前景路径。
英文摘要：
We present a framework to deal with a range of large scale compressive sensing problems using a quantum subroutine. We apply a quantum approximate optimization algorithm (QAOA) to support detection in a sparse signal reconstruction algorithm: matching pursuit. The constrained optimization required in this algorithm is difficult to handle when the size of the problem is large and constraints are given by unstructured patterns. Our framework utilizes specially designed structured constraints that are easy to manipulate and reduce the optimization problem to the solution of an Ising model which can be found using Ising solvers. In this research, we test the performance of QAOA for this purpose on a simulator of quantum computer. We observe that our method can outperform reference classical methods. Our results explore a promising path of applying quantum computers in the compressive sensing field.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
