Realizing quantum convolutional neural networks on a superconducting quantum processor to recognize quantum phases

Herrmann, J.; Llima, Sergi Masot; Remm, Ants; Zapletal, Petr; McMahon, Nathan A.; Scarato, Colin; Swiadek, Francois; Andersen, Christian Kraglund; Hellings, Christoph; Krinner, Sebastian; Lacroix, Nathan; Lazar, Stefania; Kerschbaum, M.; Zanuz, Dante Colao; Norris, Graham J.; Hartmann, Michael J.; Wallraff, Andreas; Eichler, Christopher

doi:10.1038/s41467-022-31679-5

Cited by 60 publications

(24 citation statements)

References 45 publications

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“…The Rectified Linear Unit (ReLu) shall be calculated to the reasonable satisfaction as shown in Equation (2), where i = 0:1, ⋯, n and n refers to the number of network layers. For the output x i of layer i, the positive original value is output and the negative value is assigned zero [37]. Compared with other activation functions, there is no need to calculate exp ðx i Þ, and the output value is not centered at zero after activation.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

[Retracted] Artificial Intelligence Technology Driven Environmental Factors Extraction and Analysis Method in Traditional Clothing Handicraft

Tao

Guo

2022

Journal of Environmental and Public Health

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The application of artificial intelligence (AI) technology in the field of clothes can provide a good development mode and system under the social context of AI technology development. AI provides help for the development of intelligent clothing. Intelligent clothing is a high-tech product that integrates intelligent technology and clothing. It combines cutting-edge technologies in electronic information technology, sensor technology, textile science, and material science. In the extraction and analysis of environmental factors in clothing handicraft, AI technology has a considerable application prospect and a certain development potential. In order to improve the accuracy of environmental factors extraction in clothing handicraft, this paper uses convolutional neural network (CNN) to extract and analyze environmental factors in traditional clothing handicraft. We carried out experiments on the extraction of environmental factors in clothing handicrafts with pure color, few patterns, patterns, and complex background. The experimental results show that the CNN has a good effect on the extraction of environmental factors in clothing handicraft under different backgrounds. In addition, the model in this paper has good stability, accuracy, and feature extraction speed, which has high practical value and research significance.

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Section: Convolutional Neural Networkmentioning

confidence: 99%

[Retracted] Artificial Intelligence Technology Driven Environmental Factors Extraction and Analysis Method in Traditional Clothing Handicraft

Tao

Guo

2022

Journal of Environmental and Public Health

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“…With the advent of noisy intermediate-scale quantum (NISQ) devices, it has become an effective new tool for advancing the estimating capabilities in several industries ranging from healthcare to finance [14] [29], image classification [30], and high en-154 ergy physics data analysis [31].…”

Section: Introductionmentioning

confidence: 99%

Quantum federated learning in healthcare: The shift from development to deployment and from models to data

Kais

Bhatia

Alam

2023

Preprint

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Healthcare organizations have a high volume of sensitive data and traditional technologies have limited storage capacity and computational resources. The prospect of sharing healthcare data for machine learning is more arduous due to firm regulations related to patient privacy. The balanced protection of confidentiality, integrity, and availability of healthcare data, has become a major concern beyond classical data security considerations. In recent years, federated learning offers a solution to accelerate distributed machine learning addressing concerns related to data privacy and governance. Currently, the blend of quantum computing and machine learning has experienced significant attention from academic institutions and research communities. Quantum computers have shown the potential to bring huge benefits to the healthcare sector through efficient distributed training across several quantum nodes. The ultimate objective of this work is to develop a quantum federated learning framework (QFL) to tackle the optimization, security, and privacy challenges in the healthcare and clinical industries for medical imaging tasks. In this work, we proposed federated quantum convolutional neural networks (QCNNs) with distributed training across edge devices. To demonstrate the feasibility of the proposed QFL framework, we performed extensive experiments on medical datasets (Pneumonia MNIST, and CT-kidney disease analysis), which are non-independently and non-identically partitioned among the healthcare institutions/clients. The quantum federated global model maintained a high classification testing accuracy and generalizability and outperformed the locally train clients regardless of how unbalanced the medical data is distributed among the clients. The global model achieved the best performance as compared to local clients in terms of the area under curve of the receiver operating characteristic curve (AUCROC) (0.953) and an average of (0.98) on all classes for predicting outcomes on pneumonia and CT-kidney datasets, respectively. Moreover, the client selection mechanism is proposed to reduce the computation overhead at each communication round, which effectively improves the convergence rate. The proposed quantum federated learning framework is validated and assessed via large-scale simulations. Based on our results from numerical simulations, the deployment of distributed and secure quantum machine learning algorithms for enabling scalable and privacy-preserving intelligent healthcare applications would be extremely valuable.

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“…So far, VQAs have been exploited to solve a wide range of problems, including eigenvalue solvers [51][52][53][54][55][56], quantum neural networks [42,57,58], quantum adversarial machine learning [59][60][61][62], quantum approximate optimization algorithms [63], linear equation solvers [64][65][66] and quantum sensing [67][68][69]. Variational Quantum Classification (VQC) algorithms, as typical VQAs, have also been developed to solve classification problems on NISQ computers [39,41,42,60,62,[70][71][72][73][74], with some of them being experimentally demonstrated [17,62,75,76]. Nonetheless, the imperfect nature of NISQ computers restricts the achievable accuracy of VQCs.…”

Section: Introductionmentioning

confidence: 99%

Ensemble-learning error mitigation for variational quantum shallow-circuit classifiers

Li¹,

Huang²,

Hou³

et al. 2023

Preprint

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Classification is one of the main applications of supervised learning. Recent advancement in developing quantum computers has opened a new possibility for machine learning on such machines. However, due to the noisy performance of near-term quantum computers, we desire an approach for solving classification problems with only shallow circuits. Here, we propose two ensemble-learning classification methods, namely bootstrap aggregating and adaptive boosting, which can significantly enhance the performance of variational quantum classifiers for both classical and quantum datasets. The idea is to combine several weak classifiers, each implemented on a shallow noisy quantum circuit, to make a strong one with high accuracy. While both of our protocols substantially outperform errormitigated primitive classifiers, the adaptive boosting shows better performance than the bootstrap aggregating. In addition, its training error decays exponentially with the number of classifiers, leading to a favorable complexity for practical realization. The protocols have been exemplified for classical handwriting digits as well as quantum phase discrimination of a symmetry-protected topological Hamiltonian.

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Realizing quantum convolutional neural networks on a superconducting quantum processor to recognize quantum phases

Cited by 60 publications

References 45 publications

[Retracted] Artificial Intelligence Technology Driven Environmental Factors Extraction and Analysis Method in Traditional Clothing Handicraft

[Retracted] Artificial Intelligence Technology Driven Environmental Factors Extraction and Analysis Method in Traditional Clothing Handicraft

Quantum federated learning in healthcare: The shift from development to deployment and from models to data

Ensemble-learning error mitigation for variational quantum shallow-circuit classifiers

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