Author
Wei-Hsiang Tseng
Bio: Wei-Hsiang Tseng is an academic researcher from National Taiwan University. The author has contributed to research in topics: Quantum circuit & Electronic circuit. The author has co-authored 1 publications.
Papers
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05 Dec 2021
TL;DR: Wang et al. as mentioned in this paper presented the first work that can automatically perform bridge compression on topological quantum error correction (TQEC) circuits, which can averagely reduce space-time volumes by 83%.
Abstract: The topological quantum error correction (TQEC) scheme is promising for scalable and reliable quantum computing. A TQEC circuit can be modeled by a three-dimensional diagram, and the implementation resource of a TQEC circuit is abstracted to its space-time volume. Implementing a quantum algorithm with a reasonable physical qubit number and reasonable computation time is challenging for large-scale practical problems. Therefore, minimizing the space-time volume of a TQEC circuit becomes a crucial issue. Previous work shows that bridge compression can greatly compress TQEC circuits, but it was performed only manually. It is desirable to develop automated compression techniques for TQEC circuits to achieve low-overhead, large-scale quantum computations. In this paper, we present the first work that can automatically perform bridge compression on TQEC circuits. Compared with the state-of-the-art method, experimental results show that our proposed algorithm can averagely reduce space-time volumes by 83%.
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TL;DR: Wang et al. as mentioned in this paper proposed a bridge compression technique to compact TQEC circuits with modularization, and they also proposed a time-ordering-aware 2.5D placement for compacting TQec circuits and satisfying time-ordered measurement constraints.
Abstract: Topological quantum error correction (TQEC) is promising for scalable fault-tolerant quantum computation. The required resource of a TQEC circuit can be modeled as its space-time volume of a three-dimensional geometric description. Implementing a quantum algorithm with a reasonable physical qubit number and computation time is challenging for large-scale complex problems. Therefore, it is desirable to minimize the space-time volume for large-scale TQEC circuits. Previous work proposed bridge compression, which can significantly compress a TQEC circuit, but it was performed manually. This article presents the first automated tool that can perform bridge compression on a large-scale TQEC circuit. Our proposed algorithm applies the bridge compression technique to compactify TQEC circuits with modularization. Besides, we offer a time-ordering-aware 2.5-D placement for compacting TQEC circuits and satisfying time-ordered measurement constraints. On the other hand, we suggest friend net-aware routing to effectively reduce the required routing resource under topological deformation. Compared with the state-of-the-art work, experimental results show that our proposed algorithm can averagely reduce space-time volumes by 84%.
10 Jul 2022
TL;DR: Wang et al. as discussed by the authors proposed an automated bridging compression algorithm for large-scale topological quantum error correction (TQEC) circuits, which performs initialization/measurement simplification and flipping to improve the compression.
Abstract: Topological quantum error correction (TQEC) using the surface code is among the most promising techniques for fault-tolerant quantum circuits. The required resource of a TQEC circuit can be modeled as a space-time volume of a three-dimensional diagram by describing the defect movement along the time axis. For large-scale complex problems, it is crucial to minimize the space-time volume for a quantum algorithm with a reasonable physical qubit number and computation time. Previous work proposed an automated tool to perform bridge compression on a large-scale TQEC circuit. However, the existing automated bridging compression is only for dual defects and not for primal defects. This paper presents an algorithm to perform bridge compression on primal and dual defects simultaneously. In addition, the automatic compression algorithm performs initialization/measurement simplification and flipping to improve the compression. Compared with the state-of-the-art work, experimental results show that our proposed algorithm can averagely reduce space-time volumes by 47%.