论文标题
学习学习量子涡轮检测
Learning to Learn Quantum Turbo Detection
论文作者
论文摘要
本文研究了采用各种量子电路（VQC）的涡轮接收器。 VQC使用量子近似优化算法（QAOA）的ANSATZ配置。我们提出了一个“学习学习”（L2L）框架以优化涡轮VQC解码器，以便生成高保真软性决策输出。除了展示提出的算法的计算复杂性外，我们还表明，L2L VQC Turbo解码器可以在多输入多输出系统中实现接近最佳最大可能性性能的出色性能。
This paper investigates a turbo receiver employing a variational quantum circuit (VQC). The VQC is configured with an ansatz of the quantum approximate optimization algorithm (QAOA). We propose a 'learning to learn' (L2L) framework to optimize the turbo VQC decoder such that high fidelity soft-decision output is generated. Besides demonstrating the proposed algorithm's computational complexity, we show that the L2L VQC turbo decoder can achieve an excellent performance close to the optimal maximum-likelihood performance in a multiple-input multiple-output system.
