论文标题
特定域特异性量子体系结构优化
Domain-Specific Quantum Architecture Optimization
论文作者
论文摘要
近年来，随着量子计算的稳定进展，升级量子处理器的路线图严重依赖于目标量子架构。到目前为止，这些设计与古典计算的幼年相似，是由人类专家制作的。但是，这些通用体系结构为定制和优化留出了空间，尤其是在针对流行的近期QC应用程序时。在经典计算中，定制的体系结构表现出与通用物相比的显着性能和能源效率的提高。在本文中，我们提出了一个用于优化量子体系结构的框架，特别是通过自定义量子架连接。 It is the first work that (1) provides performance guarantees by integrating architecture optimization with an optimal compiler, (2) evaluates the impact of connectivity customization under a realistic crosstalk error model, and (3) benchmarks on realistic circuits of near-term interest, such as the quantum approximate optimization algorithm (QAOA) and quantum convolutional neural network (QCNN).我们通过优化QAOA电路的重甲状腺大架构，并在网格架构上提高了14％的改善，从而证明了仿真的忠诚度提高了59％。对于QCNN电路，体系结构优化可在重甲状腺大架构上提高11％的忠诚度，而网格体系结构则提高了605％。
With the steady progress in quantum computing over recent years, roadmaps for upscaling quantum processors have relied heavily on the targeted qubit architectures. So far, similarly to the early age of classical computing, these designs have been crafted by human experts. These general-purpose architectures, however, leave room for customization and optimization, especially when targeting popular near-term QC applications. In classical computing, customized architectures have demonstrated significant performance and energy efficiency gains over general-purpose counterparts. In this paper, we present a framework for optimizing quantum architectures, specifically through customizing qubit connectivity. It is the first work that (1) provides performance guarantees by integrating architecture optimization with an optimal compiler, (2) evaluates the impact of connectivity customization under a realistic crosstalk error model, and (3) benchmarks on realistic circuits of near-term interest, such as the quantum approximate optimization algorithm (QAOA) and quantum convolutional neural network (QCNN). We demonstrate up to 59% fidelity improvement in simulation by optimizing the heavy-hexagon architecture for QAOA circuits, and up to 14% improvement on the grid architecture. For the QCNN circuit, architecture optimization improves fidelity by 11% on the heavy-hexagon architecture and 605% on the grid architecture.
