Quantum Information Processing最新文献

Graphs are fundamental structures for representing complex, nonlinear structured data across various domains, including social networks and quantum systems. Quantum graph recurrent neural networks (QGRNNs) have been proposed to model quantum dynamics in graph-based quantum systems, but their applicability to classical data remains an open problem. In this paper, we leverage QGRNNs to process classical graph-structured data. In particular, we demonstrate how QGRNN can reconstruct node features in classical datasets. Our results show that QGRNN achieves high feature reconstruction accuracy, resulting in near-perfect classification. Furthermore, we propose an information hiding technique based on our QGRNN, where messages are embedded into a graph and then retrieved under certain conditions. We assess retrieval accuracy for different dictionary sizes and message lengths, showing that QGRNN maintains high retrieval accuracy, with minor degradation as complexity increases. These findings demonstrate the scalability and robustness of QGRNNs for both classical data processing and secure information hiding, paving the way toward quantum-enhanced feature extraction, privacy-preserving computations, and quantum steganography.

This study investigates the feasibility of language translation using quantum natural language processing (QNLP) on noisy intermediate-scale quantum devices. Classical NLP struggles with large-scale computations, but QNLP leverages quantum properties to process linguistic data more efficiently, potentially advancing NLP applications. We propose a hybrid quantum–classical model for neural machine translation, which may outperform classical methods whenever practical quantum computers are available. Using the Shannon entropy, we demonstrate the importance of rotation gate angles in parameterised quantum circuits, enabling communication between circuits for different languages. We combine bag-of-words and compositional structure models, presenting the first proof-of-concept for compositional QNLP, where circuit parameters and structure are key to model interpretability. Focusing on sentences with similar structures, we implement an encoder–decoder model with long short-term memory (LSTM) networks for translation. Experiments on 160 English–Persian sentence pairs achieved the best results with Adam as the optimiser on a two-layer LSTM, minimising loss relative to both stochastic gradient descent and root-mean-square propagation (RMSprop).

Free-space quantum key distribution (QKD) is crucial for establishing secure global quantum communication networks. The realization of such networks will critically depend on airborne platforms, which enable the deployment of mobile nodes and adaptive topology reconfiguration. When analyzing optical transmission in airborne environments, we must account for various factors including diffraction, atmospheric extinction, pointing errors, atmospheric turbulence, and the boundary layer (BL) effects specific to airborne platforms. Unlike previous studies that have primarily focused on atmospheric turbulence, this work investigates the free-space optical (FSO) channel in airborne environments by incorporating BL effects, where we cannot ignore photon deflection caused by the BL. Furthermore, we establish fundamental limits for quantum communication with BL effects and evaluate the achievable secret key rate for continuous-variable quantum key distribution (CV-QKD).

In this paper a method is proposed in which the photonic spin hall shift is enhanced by the surface-plasmon resonance. The enhancement of the PSHS through surface plasmon resonance has been extensively examined in a four-level dielectric medium. By coating the silver metal with a high-refractive-index prism and placing a four level dielectric medium on metal surface, the PSHS can be significantly amplified. This results in a substantial transverse displacement of the horizontal polarization state, attributed to the intensified evanescent field at the interface between the dielectric medium and the silver metal surface. By adjusting the parameters of the driving fields, the photonic spin shift can be controlled to exhibit either positive or negative values. The maximum values of the spin hall shift are in the range of (pm 34.070lambda le Shift_{(L,R)}le pm 34.082lambda ) with probe field detuning ((D_{p})= 5 G) and the minimum value is reported to be ( pm 10 lambda ) against the incidence angle and control field Rabi frequency ((theta _i)= 0.8 rad and (R_1)= same on all values). These findings have promising applications in areas, such as sensors, spintronics, magnetic data storage, and quantum information technologies.

We study the security of a quantum key distribution protocol under the one-sided device-independent (1sDI) setting, which assumes trust in only one party’s measurement device. This approach effectively provides a balance between the experimental viability of device-dependent and the minimal trust assumptions of device-independent (DI-QKD). An analytical lower bound on the asymptotic key rate is derived to provide security against collective attacks, in which the eavesdropper’s information is limited only by the function of observed violation of a linear quantum steering inequality, specifically the three-setting Cavalcanti–Jones–Wiseman–Reid inequality. We provide a closed-form key rate formula by reducing the security analysis to mixtures of Bell-diagonal states by utilizing symmetries of the steering functional. We show that the protocol tolerates higher quantum bit error rates than present DI-QKD protocols by benchmarking its performance under depolarizing noise. Furthermore, we explore the impact of detection inefficiencies and show that, in contrast to DI-QKD, which requires near-perfect detection, secure key generation can be achieved even with lower detection efficiency on the untrusted side. These findings highlight the advantages of 1sDI-QKD as a steering-based alternative for secure quantum communication and provide insights relevant for near-future experimental implementations.

Quantum error-correcting codes have always been important technologies to ensure the reliability and security of quantum communication. In this paper, we construct some optimal quantum subsystem codes using generalized Reed–Solomon codes over finite fields with odd characteristics. These new construction methods enrich the construction ideas of quantum subsystem codes, and many optimal quantum subsystem codes can be derived.

We derive the Fock and position-space representations of squeezed vacuum states and their photon-added extensions associated with the para-Bose oscillator algebra of order (mathcal{P}=2lambda +1), which constitutes a parity deformation of the standard harmonic oscillator algebra recovered at (mathcal{P}=1). For orders greater than one, we establish the resolution of the identity for both squeezed vacuum states and arbitrary m-photon-added squeezed vacuum states over the unit disk by constructing appropriate positive-definite measures, which depend on (lambda ) and on the pair ((lambda ,m)), respectively. For odd values of the deformation order, we obtain the Wigner function of the squeezed vacuum states in phase space in the position representation and show that the emergence of negative regions, absent in the harmonic oscillator case, serves as a clear signature of nonclassicality for (mathcal{P}>1). We further analyze the individual roles of the parameters (lambda ) and m in enhancing or suppressing nonclassical features, including quadrature squeezing, sub-Poissonian photon statistics, photon antibunching, and entanglement in their corresponding quasi-Bell states. Optical tomograms of the m-photon–added squeezed vacuum states are constructed for even values of (lambda ) by solving a real eigenvalue equation for the annihilation operator. Finally, a schematic analysis is presented to elucidate how the parameters (lambda ) and m govern the structure of the resulting optical tomograms.

We investigate the process of quantum teleportation in an expanding universe modeled by Friedmann–Robertson–Walker spacetime, focusing on two cosmologically relevant scenarios: a power-law expansion and the de Sitter universe. Adopting a field-theoretical approach, we analyze the quantum correlations between two comoving observers who share an entangled mode of a scalar field. Using the Bogoliubov transformation, we compute the teleportation fidelity and examine its dependence on the expansion rate, initial entanglement, and the mode frequency. Our findings indicate that spacetime curvature and the underlying cosmological background significantly affect the efficiency of quantum teleportation, particularly through mode mixing and vacuum structure. We also compare our results with the flat Minkowski case to highlight the role of cosmic expansion in degrading or preserving quantum information.

This survey presents a comprehensive analysis of the quantum Fourier transform (QFT), a fundamental tool in quantum computing, in comparison to its classical counterpart, the fast Fourier transform (FFT). The study begins with an introduction to the classical Fourier transform, which is widely used in different disciplines such as signal and image processing. The article introduces the QFT as a quantum version of the Fourier transform, detailing how it leverages quantum parallelism and superposition to reduce the time complexity of the operation, and highlighting its crucial role in quantum algorithms like Shor’s algorithm for integer factorization. The analysis also addresses the mathematical foundations of the QFT, its implementation in quantum circuits, and the key advantages and challenges associated with its use, such as measurement precision and quantum decoherence. Finally, it concludes with an exploration of the current and potential applications of QFT in quantum computing.

The integration of symmetry into quantum models within geometric quantum machine learning has attracted increasing research attention. In this work, we introduce a symmetry-constrained quantum convolutional neural network framework tailored to few-shot learning in the noisy intermediate-scale quantum (NISQ) setting. By unifying equivariant data embeddings with symmetry-constrained quantum gate sets, our approach compresses the hypothesis space into group-invariant subspaces, enforcing geometric inductive biases that mitigate overfitting. Theoretically, we specialize existing generalization results for parameterized quantum circuits to our symmetry-constrained QCNN architecture and show that, under (T in mathcal {O}(log n)) and (M = textrm{poly}(n)), the resulting bounds exhibit polylogarithmic scaling in the system size. This perspective complements more fine-grained, architecture-specific error decompositions by providing an alternative view that highlights how symmetry and parameter sharing compress the QCNN hypothesis space and influence the scaling behavior of existing theoretical results. To address NISQ hardware constraints, we implement a brick-layer circuit architecture with frequency collision avoidance, ensuring nearest neighbor connectivity and practical feasibility. Numerical simulations on binary MNIST and Fashion-MNIST tasks with additive Gaussian input noise ((sigma = 0.2)) applied to the classical data and a noiseless statevector backend for all quantum models indicate that, for small-to-moderate training sets, the symmetric QCNN can reduce the generalization error by up to (64%) relative to a generic QCNN and by up to (74%) relative to a standard VQC under this input noise model. On the noisy MNIST task, the proposed framework attains up to (93.9%) test accuracy with a stabilized loss around 0.28 in our simulations, while maintaining competitive performance against classical baselines such as SVMs and random forests, suggesting that symmetry-driven dimensionality reduction can improve generalization and robustness to input perturbations in quantum learning. Overall, our work presents a QCNN framework that combines symmetry preservation, generalization analysis based on existing theory, and NISQ-compatible architectural design within a single coherent model, and illustrates how these ingredients can be jointly exploited in geometric quantum machine learning.