A quantum variational circuit is a quantum machine learning model similar to a neural network.A crafted adversarial example can lead to incorrect results for the model.Using adversarial examples to train the model wil...A quantum variational circuit is a quantum machine learning model similar to a neural network.A crafted adversarial example can lead to incorrect results for the model.Using adversarial examples to train the model will greatly improve its robustness.The existing method is to use automatic differentials or finite difference to obtain a gradient and use it to construct adversarial examples.This paper proposes an innovative method for constructing adversarial examples of quantum variational circuits.In this method,the gradient can be obtained by measuring the expected value of a quantum bit respectively in a series quantum circuit.This method can be used to construct the adversarial examples for a quantum variational circuit classifier.The implementation results prove the effectiveness of the proposed method.Compared with the existing method,our method requires fewer resources and is more efficient.展开更多
Label propagation is an essential semi-supervised learning method based on graphs,which has a broad spectrum of applications in pattern recognition and data mining.This paper proposes a quantum semi-supervised classif...Label propagation is an essential semi-supervised learning method based on graphs,which has a broad spectrum of applications in pattern recognition and data mining.This paper proposes a quantum semi-supervised classifier based on label propagation.Considering the difficulty of graph construction,we develop a variational quantum label propagation(VQLP)method.In this method,a locally parameterized quantum circuit is created to reduce the parameters required in the optimization.Furthermore,we design a quantum semi-supervised binary classifier based on hybrid Bell and Z bases measurement,which has a shallower circuit depth and is more suitable for implementation on near-term quantum devices.We demonstrate the performance of the quantum semi-supervised classifier on the Iris data set,and the simulation results show that the quantum semi-supervised classifier has higher classification accuracy than the swap test classifier.This work opens a new path to quantum machine learning based on graphs.展开更多
Backdoor attacks are emerging security threats to deep neural networks.In these attacks,adversaries manipulate the network by constructing training samples embedded with backdoor triggers.The backdoored model performs...Backdoor attacks are emerging security threats to deep neural networks.In these attacks,adversaries manipulate the network by constructing training samples embedded with backdoor triggers.The backdoored model performs as expected on clean test samples but consistently misclassifies samples containing the backdoor trigger as a specific target label.While quantum neural networks(QNNs)have shown promise in surpassing their classical counterparts in certain machine learning tasks,they are also susceptible to backdoor attacks.However,current attacks on QNNs are constrained by the adversary's understanding of the model structure and specific encoding methods.Given the diversity of encoding methods and model structures in QNNs,the effectiveness of such backdoor attacks remains uncertain.In this paper,we propose an algorithm that leverages dataset-based optimization to initiate backdoor attacks.A malicious adversary can embed backdoor triggers into a QNN model by poisoning only a small portion of the data.The victim QNN maintains high accuracy on clean test samples without the trigger but outputs the target label set by the adversary when predicting samples with the trigger.Furthermore,our proposed attack cannot be easily resisted by existing backdoor detection methods.展开更多
Quantum computing is a game-changing technology for global academia,research centers and industries including computational science,mathematics,finance,pharmaceutical,materials science,chemistry and cryptography.Altho...Quantum computing is a game-changing technology for global academia,research centers and industries including computational science,mathematics,finance,pharmaceutical,materials science,chemistry and cryptography.Although it has seen a major boost in the last decade,we are still a long way from reaching the maturity of a full-fledged quantum computer.That said,we will be in the noisy-intermediate scale quantum(NISQ)era for a long time,working on dozens or even thousands of qubits quantum computing systems.An outstanding challenge,then,is to come up with an application that can reliably carry out a nontrivial task of interest on the near-term quantum devices with non-negligible quantum noise.To address this challenge,several near-term quantum computing techniques,including variational quantum algorithms,error mitigation,quantum circuit compilation and benchmarking protocols,have been proposed to characterize and mitigate errors,and to implement algorithms with a certain resistance to noise,so as to enhance the capabilities of near-term quantum devices and explore the boundaries of their ability to realize useful applications.Besides,the development of near-term quantum devices is inseparable from the efficient classical sim-ulation,which plays a vital role in quantum algorithm design and verification,error-tolerant verification and other applications.This review will provide a thorough introduction of these near-term quantum computing techniques,report on their progress,and finally discuss the future prospect of these techniques,which we hope will motivate researchers to undertake additional studies in this field.展开更多
Diabetic Retinopathy(DR)is a common complication of diabetes mellitus that causes lesions on the retina that affect vision.Late detection of DR can lead to irreversible blindness.The manual diagnosis process of DR ret...Diabetic Retinopathy(DR)is a common complication of diabetes mellitus that causes lesions on the retina that affect vision.Late detection of DR can lead to irreversible blindness.The manual diagnosis process of DR retina fundus images by ophthalmologists is time consuming and costly.While,Classical Transfer learning models are extensively used for computer aided detection of DR;however,their maintenance costs limits detection performance rate.Therefore,Quantum Transfer learning is a better option to address this problem in an optimized manner.The significance of Hybrid quantum transfer learning approach includes that it performs heuristically.Thus,our proposed methodology aims to detect DR using a hybrid quantum transfer learning approach.To build our model we extract the APTOS 2019 Blindness Detection dataset from Kaggle and used inception-V3 pre-trained classical neural network for feature extraction and Variational Quantum classifier for stratification and trained our model on Penny Lane default device,IBM Qiskit BasicAer device and Google Cirq Simulator device.Both models are built based on PyTorch machine learning library.We bring about a contrast performance rate between classical and quantum models.Our proposed model achieves an accuracy of 93%–96%on the quantum hybrid model and 85%accuracy rate on the classical model.So,quantum computing can harness quantum machine learning to do work with power and efficiency that is not possible for classical computers.展开更多
基金Project supported by the National Natural Science Foundation of China(Grant Nos.62076042 and 62102049)the Natural Science Foundation of Sichuan Province(Grant No.2022NSFSC0535)+2 种基金the Key Research and Development Project of Sichuan Province(Grant Nos.2021YFSY0012 and 2021YFG0332)the Key Research and Development Project of Chengdu(Grant No.2021-YF05-02424-GX)the Innovation Team of Quantum Security Communication of Sichuan Province(Grant No.17TD0009).
文摘A quantum variational circuit is a quantum machine learning model similar to a neural network.A crafted adversarial example can lead to incorrect results for the model.Using adversarial examples to train the model will greatly improve its robustness.The existing method is to use automatic differentials or finite difference to obtain a gradient and use it to construct adversarial examples.This paper proposes an innovative method for constructing adversarial examples of quantum variational circuits.In this method,the gradient can be obtained by measuring the expected value of a quantum bit respectively in a series quantum circuit.This method can be used to construct the adversarial examples for a quantum variational circuit classifier.The implementation results prove the effectiveness of the proposed method.Compared with the existing method,our method requires fewer resources and is more efficient.
基金Project supported by the Open Fund of Advanced Cryptography and System Security Key Laboratory of Sichuan Province(Grant No.SKLACSS-202108)the National Natural Science Foundation of China(Grant No.U162271070)Scientific Research Fund of Zaozhuang University(Grant No.102061901).
文摘Label propagation is an essential semi-supervised learning method based on graphs,which has a broad spectrum of applications in pattern recognition and data mining.This paper proposes a quantum semi-supervised classifier based on label propagation.Considering the difficulty of graph construction,we develop a variational quantum label propagation(VQLP)method.In this method,a locally parameterized quantum circuit is created to reduce the parameters required in the optimization.Furthermore,we design a quantum semi-supervised binary classifier based on hybrid Bell and Z bases measurement,which has a shallower circuit depth and is more suitable for implementation on near-term quantum devices.We demonstrate the performance of the quantum semi-supervised classifier on the Iris data set,and the simulation results show that the quantum semi-supervised classifier has higher classification accuracy than the swap test classifier.This work opens a new path to quantum machine learning based on graphs.
基金supported by the National Natural Science Foundation of China(Grant No.62076042)the National Key Research and Development Plan of China,Key Project of Cyberspace Security Governance(Grant No.2022YFB3103103)the Key Research and Development Project of Sichuan Province(Grant Nos.2022YFS0571,2021YFSY0012,2021YFG0332,and 2020YFG0307)。
文摘Backdoor attacks are emerging security threats to deep neural networks.In these attacks,adversaries manipulate the network by constructing training samples embedded with backdoor triggers.The backdoored model performs as expected on clean test samples but consistently misclassifies samples containing the backdoor trigger as a specific target label.While quantum neural networks(QNNs)have shown promise in surpassing their classical counterparts in certain machine learning tasks,they are also susceptible to backdoor attacks.However,current attacks on QNNs are constrained by the adversary's understanding of the model structure and specific encoding methods.Given the diversity of encoding methods and model structures in QNNs,the effectiveness of such backdoor attacks remains uncertain.In this paper,we propose an algorithm that leverages dataset-based optimization to initiate backdoor attacks.A malicious adversary can embed backdoor triggers into a QNN model by poisoning only a small portion of the data.The victim QNN maintains high accuracy on clean test samples without the trigger but outputs the target label set by the adversary when predicting samples with the trigger.Furthermore,our proposed attack cannot be easily resisted by existing backdoor detection methods.
基金support from the Youth Talent Lifting Project(Grant No.2020-JCJQ-QT-030)the National Natural Science Foundation of China(Grant Nos.11905294,and 12274464)+7 种基金the China Postdoctoral Science Foundation,and the Open Research Fund from State Key Laboratory of High Performance Computing of China(Grant No.201901-01)support from the National Natural Science Foundation of China(Grant Nos.11805279,12074117,61833010,and 12061131011)support from the Strategic Priority Research Program of Chinese Academy of Sciences(Grant No.XDB28000000)the National Natural Science Foundation of China(Grant Nos.61832003,61872334,and 61801459)the National Natural Science Foundation of China(Grant No.12005015)the National Natural Science Foundation of China(Grant Nos.11974205,and 11774197)the National Key Research and Development Program of China(Grant No.2017YFA0303700)the Key Research and Development Program of Guangdong Province(Grant No.2018B030325002).
文摘Quantum computing is a game-changing technology for global academia,research centers and industries including computational science,mathematics,finance,pharmaceutical,materials science,chemistry and cryptography.Although it has seen a major boost in the last decade,we are still a long way from reaching the maturity of a full-fledged quantum computer.That said,we will be in the noisy-intermediate scale quantum(NISQ)era for a long time,working on dozens or even thousands of qubits quantum computing systems.An outstanding challenge,then,is to come up with an application that can reliably carry out a nontrivial task of interest on the near-term quantum devices with non-negligible quantum noise.To address this challenge,several near-term quantum computing techniques,including variational quantum algorithms,error mitigation,quantum circuit compilation and benchmarking protocols,have been proposed to characterize and mitigate errors,and to implement algorithms with a certain resistance to noise,so as to enhance the capabilities of near-term quantum devices and explore the boundaries of their ability to realize useful applications.Besides,the development of near-term quantum devices is inseparable from the efficient classical sim-ulation,which plays a vital role in quantum algorithm design and verification,error-tolerant verification and other applications.This review will provide a thorough introduction of these near-term quantum computing techniques,report on their progress,and finally discuss the future prospect of these techniques,which we hope will motivate researchers to undertake additional studies in this field.
文摘Diabetic Retinopathy(DR)is a common complication of diabetes mellitus that causes lesions on the retina that affect vision.Late detection of DR can lead to irreversible blindness.The manual diagnosis process of DR retina fundus images by ophthalmologists is time consuming and costly.While,Classical Transfer learning models are extensively used for computer aided detection of DR;however,their maintenance costs limits detection performance rate.Therefore,Quantum Transfer learning is a better option to address this problem in an optimized manner.The significance of Hybrid quantum transfer learning approach includes that it performs heuristically.Thus,our proposed methodology aims to detect DR using a hybrid quantum transfer learning approach.To build our model we extract the APTOS 2019 Blindness Detection dataset from Kaggle and used inception-V3 pre-trained classical neural network for feature extraction and Variational Quantum classifier for stratification and trained our model on Penny Lane default device,IBM Qiskit BasicAer device and Google Cirq Simulator device.Both models are built based on PyTorch machine learning library.We bring about a contrast performance rate between classical and quantum models.Our proposed model achieves an accuracy of 93%–96%on the quantum hybrid model and 85%accuracy rate on the classical model.So,quantum computing can harness quantum machine learning to do work with power and efficiency that is not possible for classical computers.