QuEst: Graph Transformer for Quantum Circuit Reliability Estimation

• URL: http://arxiv.org/abs/2210.16724v1
• Date: Sun, 30 Oct 2022 02:35:31 GMT
• Title: QuEst: Graph Transformer for Quantum Circuit Reliability Estimation
• Authors: Hanrui Wang and Pengyu Liu and Jinglei Cheng and Zhiding Liang and Jiaqi Gu and Zirui Li and Yongshan Ding and Weiwen Jiang and Yiyu Shi and Xuehai Qian and David Z. Pan and Frederic T. Chong and Song Han
• Abstract summary: Python library called TorchQuantum can construct, simulate, and train PQC for machine learning tasks. We propose to leverage a graph transformer model to predict noise impact on circuit fidelity. Compared with circuit simulators, the predictor has over 200X speedup for estimating the fidelity.
• Score: 32.89844497610906
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Among different quantum algorithms, PQC for QML show promises on near-term devices. 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. 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. 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 R$^2$ scores for random and algorithm circuits, respectively. Compared with circuit simulators, the predictor has over 200X speedup for estimating the fidelity.

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