Advanced quantum technologies最新文献

Due to the increasing complexity of radar signals and the limitations of traditional machine learning techniques in handling noise and high-dimensional data, this paper proposes a quantum-classical hybrid convolutional neural network (QCHCNN) model for radar signal classification. In our approach, radar signals are first transformed into time-frequency amplitude spectrum images, effectively capturing their essential features. These images are then processed through a quantum convolution layer, leveraging the advantages of quantum computing to enhance feature extraction. The output is subsequently fed into a classical convolutional neural network, which further refines the data and produces accurate classification results. Our experiments show that QCHCNN outperforms in radar signal multi-classification, demonstrating stability across datasets, noise resistance, and robustness to quantum noise. It improves accuracy by 5.65% over classical convolutional neural network, reaching 96.07%. Finally, this study highlights the potential of quantum-classical hybrid methods to advance radar signal multi-classification and machine learning applications.

Recently, magnomechanical systems have emerged as promising platforms for quantum technologies, exploiting magnon–photon–phonon interactions to store high-fidelity quantum information. In this paper, we propose a novel scheme to entangle two spatially separated ferrimagnetic YIG crystals by injecting a laser field into an optomagnonic ring cavity. The proposed optomagnomechanical configuration utilizes the coupling between magnetostriction-induced mechanical displacements and the optical cavity via radiation pressure. Magnons, collective spin excitations in macroscopic ferrimagnets, are directly driven by an electromagnetic field. We demonstrate the generation of a robust macroscopic entangled state via exciting the optical cavity with a red-detuned laser field and the YIG crystals with blue-detuned microwave fields. Our analysis reveals that magnon entanglement vanishes for identical magnomechanical couplings and remains robust against thermal fluctuations. Moreover, the quantum steering can be tuned directionally by the asymmetry of the magnomechanical coupling, allowing for a selective control of the optomagnetic quantum correlations. The entangled magnon modes in two ferrimagnetic crystals represent genuine macroscopic quantum states with potential applications in the study of macroscopic quantum mechanics and quantum information processing based on magnonics. Our model is based on experimentally accessible parameters, providing a feasible route to quantum technologies.

We investigate how a homogeneous, isotropic dielectric modifies the resonance interaction energy of two atoms in a maximally entangled state undergoing synchronized motion. Our analytical and numerical results reveal that the magnitude, sign, and spatial profile of the interaction are governed by a subtle interplay among four key control knobs: the atoms' common velocity, the medium's refractive index, the interatomic separation, and the orientation of their electric dipole moments. By precisely tuning these parameters, one may not only amplify or suppress the interaction strength but also trigger a complete inversion of its character, converting attraction into repulsion or vice versa. The most dramatic departure from conventional behavior can occur at the Cherenkov threshold, $��v�=�c�/�n�$. In the short-distance regime, the resonance energy scales as $�L_0^{�-�1�}$, abandoning the familiar $�L_0^{�-�3�}$ scaling characteristic of subluminal or superluminal motion and of free-space configurations. The slower power-law decay endows the interaction with an extended effective range, while its amplitude is universally diminished relative to the off-threshold cases. Our findings establish that the synergistic interplay between a dielectric medium and uniform relativistic motion provides a tunable mechanism to control resonance interactions in entangled atomic pairs.

The maximum work that can be extracted from a quantum battery is bounded by the ergotropy of the system, which is determined by the spectral properties of the Hamiltonian. In this paper, we employ the formalism of quantum walks to investigate how the topology of the battery and the chirality of the Hamiltonian influence its performance as an energy storage unit. We analyze architectures of battery cells based on ring, complete, and wheel graph structures and analyze their behavior in the presence of noise. Our results show that these structures exhibit distinct ergotropy scaling, with the interplay between chirality and topology providing a tunable mechanism to optimize work extraction and enhance robustness against decoherence. In particular, chirality enhances ergotropy for complete quantum cells, without altering the linear scaling with size, whereas in ring cells, it bridges the performance gap between configurations with odd and even number of units. Additionally, chirality may be exploited to force degeneracies in the Hamiltonian, a condition that can spare the ergotropy to vanish in the presence of pure dephasing. We conclude that topology and chirality are key resources for improving ergotropy, offering guidelines to optimize quantum energy devices and protocols.

We investigate spatially separable edge states in a non-Hermitian system, whose localization is effectively controllable via gain and loss. Spatial separation occurs when the accumulation direction of bulk states induced by the non-Hermitian skin effect opposes the localization direction of edge states controlled by the band topology. To distinguish distinct localization behaviors, we introduce an indicator that combines the spectral winding number with the degree of separation between edge states. In particular, the system exhibits six distinct phases, three spatially separated and three spatially overlapped. Moreover, we propose an electric circuit scheme to implement the theoretical models. Our work demonstrates the tunability and universality of spatially separable edge and corner states, offering a foundation for potential devices with controllable directional localization.

A quantum K-nearest neighbors(QKNN) algorithm is proposed to offer superior performance compared to the classical KNN(CKNN) approach, improving classification accuracy, scalability, and robustness. Our approach optimizes Hadamard and rotation gates for quantum data encoding and efficiently embeds classical data into quantum states. Entangled gates, such as IsingXY and CNOT, enhance feature extraction and classification by enabling complex feature interactions. A new quantum distance metric based on swap test results is used to calculate similarity measures between quantum states. This algorithm offers superior accuracy and computational efficiency compared to traditional Euclidean distance metrics. We used three benchmark datasets to evaluate the suggested QKNN method. The results demonstrated that it outperformed the other two methods, classical KNN (CKNN) and quantum neural networks (QNN), as well as the more recent QKNN research. The proposed QKNN algorithm achieves prediction accuracies of 98.25%, 100%, and 99.27% for the three datasets, whereas the QNN achieves prediction accuracies of 97.17%, 83.33%, and 86.18%, respectively. Moreover, quantum noise challenges are addressed by integrating a Shor code-based error mitigation strategy, which ensures stability of the algorithm and resilience to noisy quantum environments. The results demonstrate the scalability, efficiency, and robustness of the proposed QKNN algorithm.

The experimental detection of quantum entanglement is of great importance in quantum information processing. We present two separability criteria based on the generalized realignment moments. By incorporating additional parameters, these criteria prove to be more flexible and stronger than some of the existing ones. Detailed examples are given to demonstrate their availability and feasibility for entanglement detection.