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.
翻译:随着近年来在量子计算方面的稳步进展,升级量子处理器的路线图在很大程度上依赖目标的qubit结构。迄今为止,与古典计算早期的早期一样,这些设计由人类专家设计。然而,这些通用结构为定制和优化留出了空间,特别是在针对受欢迎的近期QC应用程序时。在传统计算中,定制结构展示了与普通用途对应方相比的显著性能和能效收益。在本文中,我们提出了一个优化量子结构的框架,特别是通过定制qubit连通性。这是第一项工作:(1)通过将结构优化与最佳编译器相结合,提供性能保障,(2)根据现实的交叉跟踪错误模式评估连接定制的影响,(3)对现实的近期利益电路进行基准,例如量近效优化算法(QAOA)和量子革命神经网络(QCNN)。我们通过优化QAOA电路路段的重六边结构,以及将重电网结构改进到14 % 。