Quantum Circuit Architecture Search on a Superconducting Processor

Variational quantum algorithms (VQAs) have shown strong evidence to gain provable computational advantages in diverse fields such as finance, machine learning, and chemistry. However, the heuristic ansatz exploited in modern VQAs is incapable of balancing the trade-off between expressivity and trainability, which may lead to degraded performance when executed on noisy intermediate-scale quantum (NISQ) machines. To address this issue, here, we demonstrate the first proof-of-principle experiment of applying an efficient automatic ansatz design technique, i.e., quantum architecture search (QAS), to enhance VQAs on an 8-qubit superconducting quantum processor. In particular, we apply QAS to tailor the hardware-efficient ansatz toward classification tasks. Compared with heuristic ansätze, the ansatz designed by QAS improves the test accuracy from 31% to 98%. We further explain this superior performance by visualizing the loss landscape and analyzing effective parameters of all ansätze. Our work provides concrete guidance for developing variable ansätze to tackle various large-scale quantum learning problems with advantages.

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Publication Details

Journal
Entropy
Published
2024-11-26
DOI
https://doi.org/10.3390/e26121025
Citations
2
Primary Topic
Quantum Computing Algorithms and Architecture
Type
article
Field-Weighted Citation Impact
0.69

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article

Quantum Circuit Architecture Search on a Superconducting Processor

Jing-Ning Zhang, Xingyao Wu, Yuxuan Du, Peng Zhao et al.
2 citations
Entropy
Quantum Computing Algorithms and Architecture
0.69
article

Quantum Circuit Architecture Search on a Superconducting Processor

Jing-Ning Zhang, Xingyao Wu, Yuxuan Du, Peng Zhao, Dong E. Liu, Mengjun Hu, Ruixia Wang, Haifeng Yu, Huikai Xu, Kehuan Linghu, Dacheng Tao, Min-Hsiu Hsieh, Zhiyuan Li, Teng Ma, Xuegang Li, Yirong Jin, Yang Qian
article en
2 citations

Abstract

Variational quantum algorithms (VQAs) have shown strong evidence to gain provable computational advantages in diverse fields such as finance, machine learning, and chemistry. However, the heuristic ansatz exploited in modern VQAs is incapable of balancing the trade-off between expressivity and trainability, which may lead to degraded performance when executed on noisy intermediate-scale quantum (NISQ) machines. To address this issue, here, we demonstrate the first proof-of-principle experiment of applying an efficient automatic ansatz design technique, i.e., quantum architecture search (QAS), to enhance VQAs on an 8-qubit superconducting quantum processor. In particular, we apply QAS to tailor the hardware-efficient ansatz toward classification tasks. Compared with heuristic ansätze, the ansatz designed by QAS improves the test accuracy from 31% to 98%. We further explain this superior performance by visualizing the loss landscape and analyzing effective parameters of all ansätze. Our work provides concrete guidance for developing variable ansätze to tackle various large-scale quantum learning problems with advantages.

EntropyVol. 26(12)
University of Technology Sydney (AU), The University of Sydney (AU), Centre for Quantum Computation and Communication Technology (AU), Jingdong (China) (CN), Beijing Academy of Quantum Information Sciences (CN), Tsinghua University (CN)
National Science Foundation, National Natural Science Foundation of China
Industry, innovation and infrastructure
Openalex Percentile: Top 23%
Quantum Computing Algorithms and Architecture
0.69
DOIOpen Access PDFOpenAlex
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