Statistical Methods for Quantum State Verification and Fidelity Estimation

Yu, Xiao-Dong; Shang, Jiangwei; Gühne, Otfried

doi:10.1002/qute.202100126

Cited by 26 publications

(6 citation statements)

References 136 publications

(281 reference statements)

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“…Variational quantum algorithms have been adopted by recent works to perform fidelity estimation [5,6,42]. Machine learning-based and statistical methods are also proposed to estimate the fidelity [31,58,60]. In addition, "classical shadow" is proposed for more efficient tomography [18], which can also benefit fidelity estimation.…”

Section: Fidelity Estimation and Predictionmentioning

confidence: 99%

QuEst: Graph Transformer for Quantum Circuit Reliability Estimation

Wang¹,

Liu²,

Cheng³

et al. 2022

Preprint

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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 networkbased 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 200× speedup for Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).

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Section: Fidelity Estimation and Predictionmentioning

confidence: 99%

QuEst: Graph Transformer for Quantum Circuit Reliability Estimation

Wang¹,

Liu²,

Cheng³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Variational quantum algorithms have been adopted by recent works to perform fidelity estimation [5,6,41]. Machine learning-based and statistical methods are also proposed to estimate the fidelity [30,57,59]. In addition, "classical shadow" is proposed for more efficient tomography [17], which can also benefit fidelity estimation.…”

Section: Fidelity Estimation and Predictionmentioning

confidence: 99%

TorchQuantum Case Study for Robust Quantum Circuits

Wang

Liang

Gu³

et al. 2022

Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design

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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. I nspired b y t he n atural g raph 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 c an 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 networkbased 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 200× speedup for estimating the fidelity. The datasets and predictors can be accessed in the TorchQuantum library.

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“…In the last few years, certain types of entangled quantum states and processes have been verified efficiently or even optimally using local measurements only; [ 17–42 ] see Ref. [43] for a recent review.…”

Section: Introductionmentioning

confidence: 99%

Efficient Verification of Arbitrary Entangled States with Homogeneous Local Measurements

Liu

Shang

et al. 2023

Adv Quantum Tech

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Quantum state verification (QSV) is the task of relying on local measurements only to verify that a given quantum device does produce the desired target state. Up to now, certain types of entangled states can be verified efficiently or even optimally by QSV. However, given an arbitrary entangled state, how to design its verification protocol remains an open problem. This study presents a systematic strategy to tackle this problem by considering the locality of what it initiates as the choice‐independent measurement protocols, whose operators can be directly achieved when they are homogeneous. Taking several typical entangled states as examples, this study shows the explicit procedures of the protocol design using standard Pauli projections, demonstrating the superiority of the method for attaining better QSV strategies. Moreover, the framework can be naturally extended to other tasks such as the construction of entanglement witnesses, and even parameter estimation.

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Statistical Methods for Quantum State Verification and Fidelity Estimation

Cited by 26 publications

References 136 publications

QuEst: Graph Transformer for Quantum Circuit Reliability Estimation

QuEst: Graph Transformer for Quantum Circuit Reliability Estimation

TorchQuantum Case Study for Robust Quantum Circuits

Efficient Verification of Arbitrary Entangled States with Homogeneous Local Measurements

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