Deep quantum neural networks on a superconducting processor

Pan, Xiaoxuan; Lu, Zhide; Wang, Wei‐Ting; Hua, Z.; Xu, Yan; Li, Weikang; Cai, Wenli; Li, Xuegang; Wang, Haiyan; Song, Yipu; Zou, Chang‐Ling; Deng, Dong-Ling; Sun, Luyan

doi:10.1038/s41467-023-39785-8

Cited by 10 publications

(5 citation statements)

References 47 publications

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“…In this paper, four commonly used metrics such as accuracy, precision, recall and F1-score of binary classification task species are selected for model evaluation. The specific formulas for the four metrics are shown in equations ( 5)- (8). Among them, TP indicates that positive samples are correctly classified as positive samples, FP indicates that negative samples are incorrectly classified as positive samples, FN…”

Section: Datacon Datasetsmentioning

confidence: 99%

“…To enhance the VQC model, L1Loss, L2Loss and CrossEntropyLoss are selected for loss function, COYBYLA, P_BFGS, SPSA, etc are selected for optimizers. To enhance the QKNN model, the set of integers [3,4,5,6,7,8,9] is selected for k-value, and the set of fractions (0,1) is selected for the weight parameter w.…”

Section: The Process Of Model Enhancement Realizationmentioning

confidence: 99%

“…Quantum computing is a new computing model with parallel computing capability and information carrying capacity that is difficult to reach by classical computing technology, which can effectively improve the speed of computation and realize the exponential acceleration of computation, and is expected to be a feasible solution to the arithmetic bottleneck of classical computing [6,7]. Advances in hardware accelerate quantum computing into reality [8,9]. The combination of quantum computing and machine learning has achieved a series of results, such as quantum support vector machine (QSVM), quantum K-nearest neighbors, quantum neural network, quantum reinforcement learning, quantum ensemble learning, and quantum cluster algorithms [10][11][12][13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

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A hybrid quantum ensemble learning model for malicious code detection

Xiong,

Ding,

Fei

et al. 2024

Quantum Sci. Technol.

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Quantum computing as a new computing model with parallel computing capability and high information carrying capacity, has attracted a lot of attention from researchers. Ensemble learning is an effective strategy often used in machine learning to improve the performance of weak classifiers. Currently, the classification performance of quantum classifiers is not satisfactory enough due to factors such as the depth of quantum circuit, quantum noise, and quantum coding method, etc. For this reason, this paper combined the ensemble learning idea and quantum classifiers to design a novel hybrid quantum machine learning model. Firstly, we run the Stacking method in classical machine learning to realize the dimensionality reduction of high-latitude data while ensuring the validity of data features. Secondly, we used the Bagging method and Bayesian hyperparameter optimization method applied to Quantum Support Vector Machine (QSVM), Quantum K Nearest Neighbors (QKNN), Variational Quantum Classifier (VQC). Thirdly, the voting method is used to ensemble the predict results of QSVM, QKNN, VQC as the final result. We applied the hybrid quantum ensemble machine learning model to malicious code detection. The experimental results show that the classification precision (accuracy, F1-score) of this model has been improved to 98.9% (94.5%, 94.24%). Combined with the acceleration of quantum computing and the higher precision rate, it can effectively deal with the growing trend of malicious codes, which is of great significance to cyberspace security.

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Section: Datacon Datasetsmentioning

confidence: 99%

Section: The Process Of Model Enhancement Realizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A hybrid quantum ensemble learning model for malicious code detection

Xiong,

Ding,

Fei

et al. 2024

Quantum Sci. Technol.

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“…Figure 4 shows the link node topology. Link nodes play an essential role in quantum communication networks, including quantum state transmission, quantum entanglement distribution, quantum channel establishment, quantum relaying, and error correction [40]. They must maintain the coherence and accuracy of the quantum states and ensure the reliable transmission of entangled quantum states.…”

Section: Blocking Probabilities and Thresholdsmentioning

confidence: 99%

A Dynamic-Routing Algorithm Based on a Virtual Quantum Key Distribution Network

Miao

2023

Applied Sciences

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Quantum key distribution (QKD) is an encrypted communication technique based on the principles of quantum mechanics that ensures communication security by exploiting the properties of quantum states. Currently, the transmission efficiency of the QKD system is low. Trusted relay technology is used to solve this problem and achieve long-distance transmission. However, trusted relaying alone cannot decrypt the issues of poor link stability and the low utilization of key resources. To further optimize the system performance, we propose a dynamic routing algorithm. One of the improvement schemes includes the following: firstly, an adjustable-size quantum key pool (QKP) is designed, which can dynamically adjust the size of the refreshing pool according to the actual demand. Secondly, the utilization of key resources is improved by using the residual quantum key model to dynamically obtain the remaining key amount in the QKP and set the key amount threshold. We calculate the link-blocking probability and track the blocking intensity and blocking entry by combining the Poisson process, thus realizing the evaluation of the link stability. Finally, the number of remaining keys in the QKP and the link-blocking probability combine with the random wandering model as the basis of the route selection for the QKD dynamic routing algorithm to achieve efficient key path selection. We validated the algorithm by comparing it with other algorithms on the Mininet simulation platform, and the algorithm proved to have a better performance in terms of congestion avoidance, delay reduction, and improved QKD efficiency. This scheme provides a novel and efficient way to solve the problems in existing QKD systems. It effectively improves the transmission efficiency and strengthens the system’s security by dynamically obtaining the critical volume, accurately evaluating the link state, and selecting the optimal critical path.

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“…The interplay between quantum computing and machine learning gives rise to a research frontier of quantum machine learning [1,2]. The attempting quantum computing characteristics hold the intriguing potential to trigger a revolution in traditional machine learning study [3][4][5][6][7][8][9][10]. Along this direction, a series of careful investigations have been conducted and various kinds of quantum classifiers have been introduced in [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Universal adversarial perturbations for multiple classification tasks with quantum classifiers

Qiu

2023

Mach. Learn.: Sci. Technol.

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Quantum adversarial machine learning is an emerging field that studies the vulnerability of quantum learning systems against adversarial perturbations and develops possible defense strategies. Quantum universal adversarial perturbations are small perturbations, which can make different input samples into adversarial examples that may deceive a given quantum classifier. This is a field that was rarely looked into but worthwhile investigating because universal perturbations might simplify malicious attacks to a large extent, causing unexpected devastation to quantum machine learning models. In this paper, we take a step forward and explore the quantum universal perturbations in the context of heterogeneous classification tasks. In particular, we find that quantum classifiers that achieve almost state-of-the-art accuracy on two different classification tasks can be both conclusively deceived by one carefully-crafted universal perturbation. This result is explicitly demonstrated with well-designed quantum continual learning models with elastic weight consolidation method to avoid catastrophic forgetting, as well as real-life heterogeneous datasets from hand-written digits and medical MRI images. Our results provide a simple and efficient way to generate universal perturbations on heterogeneous classification tasks and thus would provide valuable guidance for future quantum learning technologies.

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Deep quantum neural networks on a superconducting processor

Cited by 10 publications

References 47 publications

A hybrid quantum ensemble learning model for malicious code detection

A hybrid quantum ensemble learning model for malicious code detection

A Dynamic-Routing Algorithm Based on a Virtual Quantum Key Distribution Network

Universal adversarial perturbations for multiple classification tasks with quantum classifiers

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