A quantum identification system based on the transformation of polarization of a mesoscopic coherent state is proposed. Physically, an initial polarization state which carries the identity information is transformed i...A quantum identification system based on the transformation of polarization of a mesoscopic coherent state is proposed. Physically, an initial polarization state which carries the identity information is transformed into an arbitrary elliptical polarization state, To verify the identity of a communicator, a reverse procedure is performed by the receiver, For simply describing the transformation procedure, the analytical methods of Poincaré sphere and quaternion are adopted. Since quantum noise provides such a measurement uncertainty for the eavesdropping that the identity information cannot be retrieved from the elliptical polarization state, the proposed scheme is secure.展开更多
Quantum Machine Learning(QML)techniques have been recently attracting massive interest.However reported applications usually employ synthetic or well-known datasets.One of these techniques based on using a hybrid appr...Quantum Machine Learning(QML)techniques have been recently attracting massive interest.However reported applications usually employ synthetic or well-known datasets.One of these techniques based on using a hybrid approach combining quantum and classic devices is the Variational Quantum Classifier(VQC),which development seems promising.Albeit being largely studied,VQC implementations for“real-world”datasets are still challenging on Noisy Intermediate Scale Quantum devices(NISQ).In this paper we propose a preprocessing pipeline based on Stokes parameters for data mapping.This pipeline enhances the prediction rates when applying VQC techniques,improving the feasibility of solving classification problems using NISQ devices.By including feature selection techniques and geometrical transformations,enhanced quantum state preparation is achieved.Also,a representation based on the Stokes parameters in the PoincaréSphere is possible for visualizing the data.Our results show that by using the proposed techniques we improve the classification score for the incidence of acute comorbid diseases in Type 2 Diabetes Mellitus patients.We used the implemented version of VQC available on IBM’s framework Qiskit,and obtained with two and three qubits an accuracy of 70%and 72%respectively.展开更多
基金Project supported by the National Natural Science Foundation of China （Grant No 60472018）.
文摘A quantum identification system based on the transformation of polarization of a mesoscopic coherent state is proposed. Physically, an initial polarization state which carries the identity information is transformed into an arbitrary elliptical polarization state, To verify the identity of a communicator, a reverse procedure is performed by the receiver, For simply describing the transformation procedure, the analytical methods of Poincaré sphere and quaternion are adopted. Since quantum noise provides such a measurement uncertainty for the eavesdropping that the identity information cannot be retrieved from the elliptical polarization state, the proposed scheme is secure.
基金funded by eVIDA Research group IT-905-16 from Basque Government.
文摘Quantum Machine Learning(QML)techniques have been recently attracting massive interest.However reported applications usually employ synthetic or well-known datasets.One of these techniques based on using a hybrid approach combining quantum and classic devices is the Variational Quantum Classifier(VQC),which development seems promising.Albeit being largely studied,VQC implementations for“real-world”datasets are still challenging on Noisy Intermediate Scale Quantum devices(NISQ).In this paper we propose a preprocessing pipeline based on Stokes parameters for data mapping.This pipeline enhances the prediction rates when applying VQC techniques,improving the feasibility of solving classification problems using NISQ devices.By including feature selection techniques and geometrical transformations,enhanced quantum state preparation is achieved.Also,a representation based on the Stokes parameters in the PoincaréSphere is possible for visualizing the data.Our results show that by using the proposed techniques we improve the classification score for the incidence of acute comorbid diseases in Type 2 Diabetes Mellitus patients.We used the implemented version of VQC available on IBM’s framework Qiskit,and obtained with two and three qubits an accuracy of 70%and 72%respectively.
