QuEst: Graph Transformer for Quantum Circuit Reliability Estimation

Hanrui Wang, Pengyu Liu, Jinglei Cheng, Zhiding Liang, Jiaqi Gu, Zirui Li, Yongshan Ding, Weiwen Jiang, Yiyu Shi, Xuehai Qian, David Z Pan, Frederic T Chong, Song Han
MIT, Tsinghua Univ., Purdue Univ., Univ. of Notre Dame, Univ. of Taxes at Austin, Rutgers Univ., Yale Univ., George Mason Univ., Univ. of Chicago
(* indicates equal contribution)

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Abstract

Quantum Computing has attracted much research attention because of its potential to achieve fundamental speed and efficiency improvements in various domains. Among different quantum algorithms, Parameterized Quantum Circuits (PQC) for Quantum Machine Learning (QML) show promises to realize quantum advantages on the current Noisy Intermediate-Scale Quantum (NISQ) Machines. Therefore, to facilitate the QML and PQC research, a recent python library called TorchQuantum has been released. It can construct, simulate, and train PQC for machine learning tasks with high speed and convenient debugging supports. Besides quantum for ML, we want to raise the community's attention on the reversed direction: ML for quantum. Specifically, the TorchQuantum library also supports using data-driven ML models to solve problems in quantum system research, such as predicting the impact of quantum noise on circuit fidelity and improving the quantum circuit compilation efficiency. This paper presents a case study of the ML for quantum part in TorchQuantum. Since estimating the noise impact on circuit reliability is an essential step toward understanding and mitigating noise, we propose to leverage classical ML to predict noise impact on circuit fidelity. Inspired by the natural graph representation of quantum circuits, we propose to leverage a graph transformer model to predict the noisy circuit fidelity. We firstly collect a large dataset with a variety of quantum circuits and obtain their fidelity on noisy simulators and real machines. Then we embed each circuit into a graph with gate and noise properties as node features, and adopt a graph transformer to predict the fidelity. We can avoid exponential classical simulation cost and efficiently estimate fidelity with polynomial complexity. Evaluated on 5 thousand random and algorithm circuits, the graph transformer predictor can provide accurate fidelity estimation with RMSE error 0.04 and outperform a simple neural network-based model by 0.02 on average. It can achieve 0.99 and 0.95 R2 scores for random and algorithm circuits, respectively. Compared with circuit simulators, the predictor has over 200× speedup for estimating the fidelity. The datasets and predictors can be accessed in the TorchQuantum library.

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Citation

@inproceedings{10.1145/3508352.3561118,
author = {Wang, Hanrui and Liang, Zhiding and Gu, Jiaqi and Li, Zirui and Ding, Yongshan and Jiang, Weiwen and Shi, Yiyu and Pan, David Z. and Chong, Frederic T. and Han, Song},
title = {QuEst: Graph Transformer for Quantum Circuit Reliability Estimation (TorchQuantum Case Study for Robust Quantum Circuits)},
year = {2022},
isbn = {9781450392174},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3508352.3561118},
doi = {10.1145/3508352.3561118},
booktitle = {Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design},
articleno = {136},
numpages = {9},
location = {San Diego, California},
series = {ICCAD '22}
}

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Acknowledgment

We thank National Science Foundation, MIT-IBM Watson AI Lab, and Qualcomm Innovation Fellowship for supporting this research. This work is funded in part by EPiQC, an NSF Expedition in Computing, under grants CCF-1730082 1730449; in part by STAQ under grant NSF Phy-1818914; in part by DOE grants DE-SC0020289 and DE-SC0020331; and in part by NSF OMA-2016136 and the Q-NEXT DOE NQI Center. We acknowledge the use of IBM Quantum services for this work.

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