Field Of Information Processing Research Articles

Mode-selective photon-added two-mode squeezed vacuum state and its entanglement properties

A class of non-Gaussian entangled states is introduced by applying the nonlocal single-photon addition to two-mode squeezed vacuum state and the properties of entanglement are numerically investigated according to linear entropy and Einstein–Podolsky–Rosen (EPR) steering. After the nonlocal single-photon addition operation, Wigner function of the generated state appears some negative region and loses its Gaussian property in phase space. In essence, non-Gaussian entangled states are generated after applying nonlocal single-photon addition. Additionally, by studying the linear entropy and EPR steering, we find that single-photon addition can enhance their entanglement degree and non-Gaussian steering can be witnessed in a large range of squeezing parameter for the second-order quadratures, which may provide a well application in the fields of quantum information processing.

Laterally coupled photonic crystal surface emitting laser arrays

We propose and investigate a novel coherent laser array design based on laterally coupled photonic crystal surface-emitting lasers (PCSELs). As a new type of semiconductor laser technology, PCSELs have field confinement in a planar cavity and laser beam emission in the surface normal direction. By engineering lateral couplings between PCSELs with heterostructure photonic crystal designs, we can achieve coherent operations from an array of PCSELs. In this paper, we demonstrate coherent operation from a passively coupled PCSEL array design. We fabricated PCSEL array devices on a GaAs-based quantum well heterostructure at a target wavelength of 1040 nm. Experimental results show that the 2-by-2 PCSEL arrays have spectral linewidth of 0.14–0.22 nm. Beam combining performance was characterized by self-interference experiments. Similar coherency between the PCSEL array and single PCSEL device was observed. Our compact PCSEL array designs by passive lateral coupling have potential applications in fields of on-chip photonic computing, quantum, and information processing.

Controllable storage and retrieval of temporal vector optical solitons in semiconductor double quantum dots by inter-dot tunneling coupling effect

In this study, we analyzed the storage and retrieval of vector optical fields in semiconductor double quantum dots with interdot tunneling coupling. Our findings indicate that temporal vector optical solitons (TVOS) can be effectively stored and retrieved through this coupling. Furthermore, we conducted numerical simulations to investigate the interaction between TVOS. Our results suggest that the interaction between in-phase solitons is attractive, while the interaction between anti-phase solitons is repulsive. Additionally, we found that TVOS remain stable even after a collision, which is significant for the processing of optical information in solid quantum materials. Overall, our study sheds light on the potential of interdot tunneling coupling for storing and retrieving vector optical fields in semiconductor double quantum dots. Additionally, our findings regarding the interaction between TVOS can inform future research in the field of optical information processing.

Enhanced superluminal pulse propagation via chiral quantum dot system

The control and enhancement of superluminal pulse propagation via a chiral quantum-dots (QDs) system is presented in this article. The proposed system is a double chiral QDs four-level system, that gets excited by pump and probe fields, along with inter-dot tunneling strength. It is observed that the strength of inter-dot tunneling strongly influences the left-circular polarized (LCP) and right-circular polarized (RCP) superluminal pulse propagation. By appropriately modulating this strength, optimal coherence within the system can be achieved, thereby contributing to enhanced superluminal pulse propagation. We have examined the characteristics of RCP and LCP beams and observed the large negative group index and advance time. The observation of a large negative group index and advanced time shows strong evidence for enhanced superluminal pulse propagation in the proposed chiral QDs system. This study has important applications in the field of optical information processing, temporal cloaking, quantum communication, and improving computer chip speed.

Superluminal propagation of surface plasmon polaritons via hybrid chiral quantum dots system

We present a novel methodology for enhancing superluminal surface plasmon polaritons (SPPs) propagations within a hybrid nanostructure configuration consisting of gold (Au) metal and chiral quantum dots (CQDs) medium. The arrangement of CQDs and metal hybrid nanostructures enables the production of SPPs when exposed to incident light. The resonances of SPPs within a hybrid nanostructure are determined through analytical calculations using Maxwell’s equations under specified boundary conditions, while the dynamics of the CQDs system are calculated using the density matrix approach. It is demonstrated that the propagation of SPPs is significantly influenced by both right-circularly polarized (RCP) and left-circularly polarized (LCP) SPPs. Additionally, we investigate the enhancement of superluminal SPPs propagation by varying the electron tunneling strength and the intensity of the control field within the hybrid system. The characteristics of RCP and LCP SPPs have been investigated, indicating a large negative group index and advancement in time. The observation of a large negative group index and advancement in time provides strong evidence for enhanced superluminal SPPs propagation within the proposed hybrid nanostructure. The results have potential applications in the fields of optical information processing, temporal cloaking, quantum communication, and the advancement of computer chip speed.

Deterministic All-Optical Continuous-Variable Quantum Telecloning.

Quantum telecloning, a pivotal multiuser quantum communication protocol in the realm of quantum information science, facilitates the copy of a quantum state across M distinct locations through teleportation technique. In the continuous-variable regime, the implementation of quantum telecloning necessitates the distribution of multipartite entanglement among the sender and M receiver parties. Following this, the sender carries out optic-electro conversion and transmits information via classical channel to M spatially separated receivers simultaneously. To successfully reconstruct the input state, electro-optic conversion needs to be employed by each receiver. However, due to these conversions, the bandwidth of the optical mode in this process is largely constrained. In this Letter, we present an all-optical version of the 1→2 continuous-variable quantum telecloning scheme, wherein both optic-electro and electro-optic conversions are replaced by optical components. Our scheme allows the two receivers to achieve input state reconstruction solely by utilizing beam splitters, significantly simplifying its complexity. We experimentally demonstrate all-optical 1→2 quantum telecloning of coherent state and achieve the fidelities of 58.6%±1.0% and 58.6%±1.1% for two clones, exceeding the corresponding classical limits (51.9%±0.5% and 51.9%±0.6%). Our results establish a platform for constructing a flexible all-optical multiuser quantum network and promote the field of all-optical quantum information processing.

Creation and manipulation of optical Meron topologies in tightly focused electromagnetic field

The optical topology, which serves as a stable spatial electromagnetic structure, offers a new dimension for applications in the field of optical information processing, transmission, and storage. In recent years, there has been an increasing focus on these spatially structured light fields. By reversing the radiation of orthogonal dipole pairs, we propose an approach to generate Meron topologies within the focused light field while also investigating the evolution of the Meron structure along the longitudinal axis. Through introducing a dipole placed along the z-axis, we achieve precise positioning and fine adjustment of the topological center. The stability of Meron under a high numerical aperture objective lens (NA = 0.95) can be effectively demonstrated.

Implementation and measurement of quantum entanglement using IBM quantum platforms

The use of quantum entanglement has garnered increasing attention among researchers in recent years due to its wide range of applications, not only revolutionizing the field of information processing but also enhancing quantum-safe communications. Identifying the degree of entanglement present in quantum states is a crucial focus, and designing an algorithm capable of feasibly measuring entanglement is imperative. While theoretical calculations hold high regard, the ease of implementing these algorithms in a laboratory setting is essential to gauge their efficiency.In this context, IBM quantum computers stand out as discrete value NISQ (Noisy Intermediate-Scale Quantum) platforms These platforms are based on superconducting qubits, providing an opportunity to test our algorithms without the need for extravagant laboratory equipment. This paper proposes an algorithm designed to measure entanglement in a bipartite system. We will execute the algorithm on IBM’s 127-qubit backends to compare our calculations with real-world results. Furthermore, we aim to address and mitigate errors inherent in these devices by utilizing local mitigation technique available in the IBM Experiments Python package, aiming for more accurate and reliable outcomes.

Amber rainbow ribbon effect in broadband optical metamaterials

Using the trapped rainbow effect to slow down or even stop light has been widely studied. However, high loss and energy leakage severely limited the development of rainbow devices. Here, we observed the negative Goos-Hänchen effect in film samples across the entire visible spectrum. We also discovered an amber rainbow ribbon and an optical black hole due to perfect back reflection in optical waveguides, where little light leaks out. Not only does the amber rainbow ribbon effect show an automatic frequency selection response, as predicted by single frequency theoretical models and confirmed by experiments, it also shows spatial periodic regulation, resulting from broadband omnidirectional visible metamaterials prepared by disordered assembly systems. This broadband light trapping system could play a crucial role in the fields of optical storage and information processing when being used to construct ultra-compact modulators and other tunable devices.

Handwritten digit recognition using quantum convolution neural network

<span lang="EN-US">The recognition of handwritten digits holds a significant place in the field of information processing. Recognizing such characters accurately from images is a complex task because of the vast differences in people's writing styles. Furthermore, the presence of various image artifacts such as blurring, intensity variations, and noise adds to the complexity of this process. The existing algorithm, convolution neural network (CNN) is one of the prominent algorithms in deep learning to handle the above problems. But there is a difficulty in handling input data that differs significantly from the training data, leading to decreased accuracy and performance. In this work, a method is proposed to overcome the aforementioned limitations by incorporating a quantum convolutional neural network algorithm (QCNN). QCNN is capable of performing more complex operations than classical CNNs. It can achieve higher levels of accuracy than classical CNNs, especially when working with noisy or incomplete data. It has the potential to scale more efficiently and effectively than classical CNNs, making them better suited for large-scale applications. The effectiveness of the proposed model is demonstrated on the modified national institute of standards and technology (MNIST) dataset and achieved an average accuracy of 91.08%.</span>

Investigating entangled photons to quantify quantum correlations in dual optomechanical cavities.

Quantum correlations that go beyond quantum entanglement have a significant role in the fields of quantum information processing and quantum computation. In essence, when we describe a quantum system using pure states, quantum entanglement and quantum correlation are essentially indistinguishable. However, this equivalence breaks down when dealing with general mixed states. To contribute to understanding this distinction, we have considered a straightforward model for both generating and measuring these quantum correlations. We focus on two macroscopic mechanical resonators situated in separate Fabry–Perot cavities and coupled through the photon hopping process. Our model allows us to conduct a comprehensive examination and quantification of quantum correlations that extend beyond entanglement between these mechanical modes. Our approach begins with the determination of the global covariance matrix, serving as the foundation for calculating two key metrics: the Entropy of Formation and the Gaussian quantum Discord. These metrics enable us to quantify the extent of quantum entanglement and quantum correlations, respectively. Our analysis, based on these quantifiers, reveals that quantum discord serves as a more appropriate metric for characterizing the quantum correlations between the mechanical modes in an optomechanical quantum system, especially in the presence of robust photon hopping.

Port-based entanglement teleportation via noisy resource states

Port-based teleportation (PBT) represents a variation of the standard quantum teleportation and is currently being employed and explored within the field of quantum information processing owing to its various applications. In this study, we focus on PBT protocol when the resource state is disrupted by local Pauli noises. Here, we fully characterise the channel of the noisy PBT protocol using Krauss representation. Especially, by exploiting the application of PBT for entanglement distribution necessary in realizing quantum networks, we investigate entanglement transmission through this protocol for each qubit considering noisy resource states, denoted as port-based entanglement teleportation (PBET). Finally, we derive upper and lower bounds for the teleported entanglement as a function of the initial entanglement and the noises. Our study demonstrates that quantum entanglement can be efficiently distributed by protocols utilizing large-sized resource states in the presence of noise and is expected to serve as a reliable guide for developing optimized PBET protocols. To obtain these results, we address that the order of entanglement of two qubit states is preserved through the local Pauli channel, and identify the boundaries of entanglement loss through this teleportation channel.

Tourism and Hospitality Forecasting With Big Data: A Systematic Review of the Literature

Empirical research has shown that incorporating big data into tourism and hospitality forecasting significantly improves prediction accuracy. This study presents a comprehensive review of big data forecasting in the tourism and hospitality industry, critically evaluating existing research and identifying five key research questions and trends that require further attention. These include the lack of theoretical foundation, the rise of high-frequency forecasting research, less attention to unstructured data, the necessity of dynamic data analysis in forecasting, and the construction of a tourism and hospitality demand information system based on cloud computing. Importantly, this study constructs a theoretical framework by combining relevant theories from psychology, communication, information processing, and other fields. Five types of big data used for tourism and hospitality forecasting are identified: web-based volume data, social media statistics, textual data, photo data, and video data. Additionally, more recent tactics such as mixed data sampling and machine learning methods are discussed.

Efficient noiseless linear amplification protocol for single-photon state using imperfect auxiliary photon source

Noiseless linear amplification (NLA) is a crucial method to solve the photon transmission loss problem. However, most NLA protocols require an ideal auxiliary single-photon source, which is unavailable under current experimental condition. Meanwhile, their heralded amplification performance is relatively low. For enhancing the feasibility and amplification performance of the NLA, in this paper, we propose an efficient NLA protocol with a practical imperfect auxiliary single-photon source. We introduce the local-quadrature squeezing operation into the NLA protocol, which can effectively increase its amplification factor. This NLA protocol only uses some common linear-optical elements, the practical imperfect auxiliary single-photon source, and imperfect single-photon detectors, so that it is easy to implement under the existing experimental condition. It may have important applications in the future quantum information processing field.

Magneto-optical epitaxial bismuth-substituted yttrium iron garnet thin films on a diamagnetic substrate for low temperature applications

The uprising interest in the integration of the magneto-optical iron garnets into the field of quantum information processing imposes requirements for the damping parameter and optical properties at low temperatures. Typically, high quality iron garnet films are mostly grown epitaxially on the paramagnetic substrates that have rare-earth elements in its composition, which leads to an increase of their magnetic damping at temperatures below 100 K. Here, we epitaxially grew magneto-optical ferromagnetic garnet films on a diamagnetic yttrium scandium gallium garnet substrate to demonstrate narrow ferromagnetic resonance (FMR) linewidth at temperature range from 300 K to 4 K. The maximum Faraday angle measured at wavelength of 660 nm is ΘF = 0.34 ⋅103 deg/cm at 65 K. The FMR linewidth of the iron garnet film gets slightly larger as temperature decreases, but still remains at a level of 35 Oe at 4 GHz even at 4 K. The investigated iron garnet thin films grown on diamagnetic substrates can be successfully implemented in low-temperature technological applications that require low damping and high magneto-optical response.

Purification for Hybrid Entanglement between Discrete‐ and Continuous‐Variable States

AbstractHybrid entangled states (HES) have attracted significant attention and been utilized in various quantum information processing applications. However, like many other entangled states, maximally entangled HES may degrade to mixed states due to environmental noise and operational imperfections. In this paper, a hybrid entanglement purification protocol (HEPP) for the HES, which consists of photon‐number state and coherent state is proposed. This HEPP is designed to effectively purify a bit‐flip error occurring in any qubit of the HES. Furthermore, HEPP is extended to a general condition, say, the multi‐party scenario, and integrates the generation of HES into the HEPP. Moreover, if different initial mixed states are chosen, then the residual entanglement can be utilized to distill high‐quality entanglement. The HEPP has important applications in the future quantum information processing field.

Pattern recognition study of musical rhythm perception based on multimodal deep learning

Abstract Rhythm perception is becoming more and more important in the field of music information processing and music understanding. The study first adopts signal processing methods to extract musical features, then uses feature fusion techniques to integrate features of different modalities into a single feature vector. Based on this model, the study identifies the rhythmic activation function of music and combines it with the hidden Markov model to infer the rhythm of the music. One of the key points of the study is to perform rhythm recognition on music containing drums, to explore the recognition effect. One of the focuses of the study is to recognize the rhythm of music containing drums to explore the recognition effect.In addition, the study also analyzes the Softmax output values of the music and compares the recognition effect of different models. The results show that the rhythm recognition of music using the multimodal deep learning method performs the best in terms of the F-Measure value, the Cemgil value, the Goto value, and the P-score value, with the respective 65.65%, 66.76%, 36.75%, and 36.75%. 66.76%, 36.75%, and 75.68%.Especially in the drum music recognition, the position of each drum music is accurately recognized, proving the model’s effectiveness in this paper. The research provides a new feasible method for the recognition and understanding of music rhythms and a valuable reference for the research in this field.

The Method Of Logical Networks For Modeling Adaptive Knowledge Testing Systems

The subject of the research is the development of mathematical and algorithmic support of the intellectual toolkit for the analysis of sets of test tasks and the modeling of the process of interpreting the quality of sets of test tasks, which allows for objective and comprehensive continuous control of the knowledge of subjects of training, subject to the implementation of the concept of virtual distributed training (retraining).
 The purpose of the research is to improve the effectiveness of monitoring the knowledge of subjects of education in the distance form of education through the use of adaptive computer testing methods based on models of logical networks and the algebra of finite predicates.
 The following tasks are solved in the article: the formation of a testing model in a distributed virtual learning environment and a model of validity assessment based on the content of sets of test tasks.
 The following methods are used: methods of logical networks and algebraic programming based on the algebra of finite predicates and predicate operations, intellectual analysis of information.
 The following results were obtained: the principles of intellectual analysis were formulated in the development of a model of a universal logical network and its application to actual tasks of artificial intelligence in the field of informal information processing, namely, in the construction of knowledge testing systems for distributed virtual learning
 Conclusions. Algorithms for optimal multi-stage adaptive testing of knowledge as part of distributed virtual learning models and methods for analyzing the success of training subjects have been improved. The use of conjunctive decomposition with binary predicates achieves the goal of the research, because in this way any multi-place predicate can be represented by a logical network simulating the process of knowledge testing, the model of the subject of learning is described.

Concatenation-based pre-trained convolutional neural networks using attention mechanism for environmental sound classification

Environmental sound classification (ESC) is gaining popularity in the field of information processing due to its significance in non-speech audio categorization. ESC faces challenges in categorizing ambient sounds, which lack a clear structure, unlike speech or music sounds. Deep learning (DL) techniques are widely used in ESC to extract relevant information from ambient sounds, in which feature extraction is very crucial and directly impacts the classification performance. However, feature extraction can be computationally expensive, especially when employing complex non-linear techniques, due to the significant amount of computing resources required during the training process of DL models. Moreover, environmental sounds can exhibit significant variability in terms of their temporal and spectral characteristics, which can pose challenges to training DL models effectively. In order to overcome these limitations, this research proposes an efficient method for extracting meaningful features from audio files and improving DL techniques' performance using spectrogram images generated from sound environmental datasets. The proposed approach uses convolutional neural networks (CNNs) with attention mechanisms and appropriate data augmentation methods. Unlike pre-trained models that use a single vector for feature extraction, the proposed approach uses a new concatenation-based CNN model with attention mechanisms, which can more effectively capture intricate relationships between input data features. This approach allows for the extraction of features from different sectors of the feature space, resulting in a more precise classification of complex and diverse datasets. Additionally, the proposed approach leverages the parallel extraction feature technique to extract features from multiple CNN models, which improves classification performance. Furthermore, attention modules are used to focus on the most relevant features of the input data. Comparative experiments are also conducted for the proposed approach and some existing state-of-the-art methods, and the results show that the former has better classification performance.

Research Progress on the Application of Topological Phase Transition Materials in the Field of Memristor and Neuromorphic Computing.

Topological phase transition materials have strong coupling between their charge, spin orbitals, and lattice structure, which makes them have good electrical and magnetic properties, leading to promising applications in the fields of memristive devices. The smaller Gibbs free energy difference between the topological phases, the stable oxygen vacancy ordered structure, and the reversible topological phase transition promote the memristive effect, which is more conducive to its application in information storage, information processing, information calculation, and other related fields. In particular, extracting the current resistance or conductance of the two-terminal memristor to convert to the weight of the synapse in the neural network can simulate the behavior of biological synapses in their structure and function. In addition, in order to improve the performance of memristors and better apply them to neuromorphic computing, methods such as ion doping, electrode selection, interface modulation, and preparation process control have been demonstrated in memristors based on topological phase transition materials. At present, it is considered an effective method to obtain a unique resistive switching behavior by improving the process of preparing functional layers, regulating the crystal phase of topological phase transition materials, and constructing interface barrier-dependent devices. In this review, we systematically expound the resistance switching mechanism, resistance switching performance regulation, and neuromorphic computing of topological phase transition memristors, and provide some suggestions for the challenges faced by the development of the next generation of non-volatile memory and brain-like neuromorphic devices based on topological phase transition materials.