Towards Efficient Superconducting Quantum Processor Architecture Design

Tian Yang, Weilong Wang, Bo Zhao +4 more

Joseph Suttle, Neereja Sundaresan, Srikanth J. Srinivasan +10 more

Gushu Li, Yufei Ding, Yuan Xie

More computational resources (i.e., more physical qubits and qubit connections) on a superconducting quantum processor not only improve the performance but also result in more complex chip architecture with lower yield rate. Optimizing both of them simultaneously is a difficult problem due to their intrinsic trade-off. Inspired by the application-specific design principle, this paper proposes an automatic design flow to generate simplified superconducting quantum processor architecture with negligible performance loss for different quantum programs. Our architecture-design-oriented profiling method identifies program components and patterns critical to both the performance and the yield rate. A follow-up hardware design flow decomposes the complicated design procedure into three subroutines, each of which focuses on different hardware components and cooperates with corresponding profiling results and physical constraints. Experimental results show that our design methodology could outperform IBM's general-purpose design schemes with better Pareto-optimal results.

Gushu Li, Yufei Ding, Yuan Xie

More computational resources (i.e., more physical qubits and qubit connections) on a superconducting quantum processor not only improve the performance but also result in more complex chip architecture with lower yield rate. Optimizing both of them simultaneously is a difficult problem due to their intrinsic trade-off. Inspired by the application-specific design principle, this paper proposes an automatic design flow to generate simplified superconducting quantum processor architecture with negligible performance loss for different quantum programs. Our architecture-design-oriented profiling method identifies program components and patterns critical to both the performance and the yield rate. A follow-up hardware design flow decomposes the complicated design procedure into three subroutines, each of which focuses on different hardware components and cooperates with corresponding profiling results and physical constraints. Experimental results show that our design methodology could outperform IBM's general-purpose design schemes with better Pareto-optimal results.,0

Wei Hu, Jiawei Pi, Weiye Xia +2 more

Tian Yang, Chen Liang, Weilong Wang +5 more

Routing design is an important aspect in aiding the completion of the Quantum Processing Unit (QPU) layout design for large-scale superconducting quantum processors. One of the research focuses is how to generate reliable routing schemes within a short time. In this study, we propose a superconducting quantum processor auto-routing method for supporting scalable architecture, which is mainly implemented through the bidirectional A star algorithm, the backtracking algorithm, and the greedy strategy. By using this method, the number of crossovers and corners can be reduced while efficiently completing the processor routing. To verify the effectiveness of our method, we selected 5 types of qubit numbers for processor routing experiments. The experimental results show that compared to the improved A star algorithm of Qiskit Metal, our method reduces the average execution time by at least 43.61% and 41.68% in serial and parallel, respectively. Compared with four other routing algorithms, our method has a minimum average reduction of 10.63% and 1.21% in the number of crossovers and corners, respectively. In addition, our method supports the processor routing design of planar and flip-chip architectures, and can automatically process both airbridge and insulation types of crossovers. Therefore, our method can provide efficient and reliable automated routing design to assist the development of large-scale superconducting quantum processors.