Impact of quantum noise on the training of quantum Generative Adversarial Networks

Borras, K.; Chang, Su Yeon; Funcke, Lena; Grossi, Michele; Hartung, Tobias; Jansen, Karl; Kruecker, D.; Kühn, Stefan; Rehm, Florian; Tüysüz, Cenk; Vallecorsa, S.

doi:10.1088/1742-6596/2438/1/012093

Cited by 7 publications

(4 citation statements)

References 5 publications

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“…In the current NISQ era, relatively high hardware error levels are one of the primary limitations to effectively employing algorithms on real quantum devices. Similar as in the classical case, QML models appear to be noise resilient to some degree of hardware errors [37][38][39][40]. In the following, the robustness of the QAG model to simulated noise is tested in inference and training.…”

Section: Quantum Noise Studymentioning

confidence: 95%

Precise image generation on current noisy quantum computing devices

Rehm,

Vallecorsa,

Borras

et al. 2023

Quantum Sci. Technol.

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The Quantum Angle Generator (QAG) is a new full Quantum Machine Learning model designed to generate accurate images on current Noise Intermediate Scale (NISQ) Quantum devices. Variational quantum circuits form the core of the QAG model, and various circuit architectures are evaluated. In combination with the so-called MERA-upsampling architecture, the QAG model achieves excellent results, which are analyzed and evaluated in detail. To our knowledge, this is the first time that a quantum model has achieved such accurate results. To explore the robustness of the model to noise, an extensive quantum noise study is performed. In this paper, it is demonstrated that the model trained on a physical quantum device learns the noise characteristics of the hardware and generates outstanding results. It is verified that even a quantum hardware machine calibration change during training of up to 8% can be well tolerated. For demonstration, the model is employed in indispensable simulations in high energy physics required to measure particle energies and, ultimately, to discover unknown particles at the Large Hadron Collider at CERN.

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Section: Quantum Noise Studymentioning

confidence: 95%

Precise image generation on current noisy quantum computing devices

Rehm,

Vallecorsa,

Borras

et al. 2023

Quantum Sci. Technol.

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“…[15,20] This method involves measuring the conditional probability at each ground state and creating a response matrix for estimating the expected value of the true observables, which can mitigate readout errors in QGAN. [21] However, the process of measuring the bias for each ground state requires a significant consumption of measurement shots, making it impractical for scenarios with limited measurement budgets and medium to large-scale quantum circuits. [15,16,22] To address the above problems, we use an efficient and economical method called bit-flip averaging (BFA).…”

Section: Introductionmentioning

confidence: 99%

“…In this simulation, we do not consider readout errors caused by phase flips because they have no effect on the statistical results of the quantum circuit. [21,23] For the error mitigation process, we follow two main steps. Firstly, we analyze the influence of noise on the quantum circuit and compute the response matrix using the BFA model.…”

Section: Introductionmentioning

confidence: 99%

Quantum generative adversarial networks based on a readout error mitigation method with fault tolerant mechanism

Zhao 赵,

Ma 马,

Cheng 程

et al. 2024

Chinese Phys. B

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Readout errors caused by measurement noise are significant source of errors in quantum circuits, which severely affects the output results and is an urgent problem to be solved in noisy-intermediate scale quantum (NISQ) computing. In this paper, we use the bit-flip averaging (BFA) method to mitigate frequent readout errors in quantum generative adversarial networks (QGAN) for image generation, which simplifies the response matrix structure by averaging the qubits for each random bit-flip in advance, successfully solving problems with high cost of measurement for traditional error mitigation methods. Our experiments were simulated in Qiskit, using the handwritten digit image recognition dataset, under the BFA-based method, the Kullback-Leibler(KL) divergence of the generated images converges to 0.04, 0.05, and 0.1 for readout error probabilities of p=0.01, p=0.05, and p=0.1, respectively. Additionally, by evaluating the fidelity of the quantum states representing the images, we observe average fidelity values of 0.97, 0.96, and 0.95 for the three readout error probabilities, respectively. These results demonstrate the robustness of the model in mitigating readout errors and provide a highly fault tolerant mechanism for image generation models.

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“…Recently, this new technology has been applied to various HEP problems (Guan et al, 2021). Namely, in event reconstruction (Das et al, 2019;Shapoval and Calafiura, 2019;Bapst et al, 2020;Tüysüz et al, 2020;Wei et al, 2020;Zlokapa et al, 2021a;Funcke et al, 2022), classification tasks (Mott et al, 2017;Belis et al, 2021;Blance and Spannowsky, 2021;Terashi et al, 2021;Wu et al, 2021;Zlokapa et al, 2021b;Araz and Spannowsky, 2022;Chen et al, 2022;Gianelle et al, 2022), data generation (Chang et al, 2021a,b;Delgado and Hamilton, 2022;Borras et al, 2023;Rehm et al, 2023), and anomaly detection problems (Ngairangbam et al, 2022;Alvi et al, 2023;Schuhmacher et al, 2023;Woźniak et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Fitting a collider in a quantum computer: tackling the challenges of quantum machine learning for big datasets

Peixoto,

Castro,

Crispim Romão

et al. 2023

Front. Artif. Intell.

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Current quantum systems have significant limitations affecting the processing of large datasets with high dimensionality, typical of high energy physics. In the present paper, feature and data prototype selection techniques were studied to tackle this challenge. A grid search was performed and quantum machine learning models were trained and benchmarked against classical shallow machine learning methods, trained both in the reduced and the complete datasets. The performance of the quantum algorithms was found to be comparable to the classical ones, even when using large datasets. Sequential Backward Selection and Principal Component Analysis techniques were used for feature's selection and while the former can produce the better quantum machine learning models in specific cases, it is more unstable. Additionally, we show that such variability in the results is caused by the use of discrete variables, highlighting the suitability of Principal Component analysis transformed data for quantum machine learning applications in the high energy physics context.

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Impact of quantum noise on the training of quantum Generative Adversarial Networks

Cited by 7 publications

References 5 publications

Precise image generation on current noisy quantum computing devices

Precise image generation on current noisy quantum computing devices

Quantum generative adversarial networks based on a readout error mitigation method with fault tolerant mechanism

Fitting a collider in a quantum computer: tackling the challenges of quantum machine learning for big datasets

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