Zhen Wang / 汪震

Zhen Wang / 汪震

A master major in AI at USTC.

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portfolio

publications

QCIR: Pattern Matching Based Universal Quantum Circuit Rewriting Framework

International Conference on Computer-Aided Design (ICCAD), 2022

Due to multiple limitations of quantum computers in the NISQ era, quantum compilation efforts are required to efficiently execute quantum algorithms on NISQ devices. Program rewriting based on pattern matching can improve the generalization ability of compiler optimization. However, it has rarely been explored for quantum circuit optimization, further considering physical features of target devices. In this paper, we propose a pattern-matching based quantum circuit optimization framework QCIR with a novel pattern description format, enabling the user-configured cost model and two categories of patterns, i.e., generic patterns and folding patterns. To get better compilation latency, we propose a DAG representation of quantum circuit called QCIR-DAG, and QVF algorithm for subcircuit matching. We implement continuous single-qubit optimization pass constructed by QCIR, achieving 10\% and 20\% optimization rate for benchmarks from Qiskit and ScaffCC, respectively.The practicality of QCIR is demonstrated by execution time and experimental results on the quantum simulator and quantum devices.

Recommended citation:
Mingyu Chen, Yu Zhang, Yongshang Li, Zhen Wang, Jun Li, and Xiangyang Li, QCIR: Pattern Matching Based Universal Quantum Circuit Rewriting Framework, in Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design, San Diego California: ACM, Oct. 2022.
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Accelerating Data Generation for Neural Operators via Krylov Subspace Recycling

International Conference on Learning Representations (ICLR), 2023

Learning neural operators for solving partial differential equations (PDEs) has attracted great attention due to its high inference efficiency. However, training such operators requires generating a substantial amount of labeled data, i.e., PDE problems together with their solutions. The data generation process is exceptionally time-consuming, as it involves solving numerous systems of linear equations to obtain numerical solutions to the PDEs. Many existing methods solve these systems independently without considering their inherent similarities, resulting in extremely redundant computations. To tackle this problem, we propose a novel method, namely Sorting Krylov Recycling (SKR), to boost the efficiency of solving these systems, thus significantly accelerating data generation for neural operators training. To the best of our knowledge, SKR is the first attempt to address the time-consuming nature of data generation for learning neural operators. The working horse of SKR is Krylov subspace recycling, a powerful technique for solving a series of interrelated systems by leveraging their inherent similarities. Specifically, SKR employs a sorting algorithm to arrange these systems in a sequence, where adjacent systems exhibit high similarities. Then it equips a solver with Krylov subspace recycling to solve the systems sequentially instead of independently, thus effectively enhancing the solving efficiency. Both theoretical analysis and extensive experiments demonstrate that SKR can significantly accelerate neural operator data generation, achieving a remarkable speedup of up to 13.9 times.

Recommended citation:
Hong Wang, Zhongkai Hao, Jie Wang, Zijie Geng, Zhen Wang, Bin Li, Feng Wu, Accelerating Data Generation for Neural Operators via Krylov Subspace Recycling, presented at the The Twelfth International Conference on Learning Representations, Oct. 2023.
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talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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