Search Results (337)

In this paper, a series of electromagnetically-induced-transparent (EIT) spectra of different Rydberg states, controlled by microwaves, in rubidium (Rb) thermal vapor are presented. The novel evolution regularity for different Rydberg states can be found by experimentally detected transmitted EIT spectra, which can reveal the primary quantum number of different Rydberg states and how to influence microwave control spectroscopy evolution regularity, and which can pave the way in order to address the challenge of selecting Rydberg states for applications in Rydberg microwave field detection. This is helpful for the development of measuring standards of the microwave field in Rydberg states. Full article

A homodyne detector, which is also a common element in current telecommunication, is a core component of continuous-variable quantum key distribution (CV-QKD) since it is considered the simplest setup for the distinguishing of coherent states with minimum error. However, the theoretical security of CV-QKD is based on the assumption that the responses of the homodyne detector are always linear with respect to the input, which is impossible in practice. In the real world, a homodyne detector has a finite linear domain, so the linearity assumption is broken when the input is too large. Regarding this security vulnerability, the eavesdropper Eve can perform the so-called homodyne detector-blinding attack by saturating the homodyne detector and then stealing key information without being detected by the legitimate users. In this paper, we propose a countermeasure for the homodyne detector-blinding attack by using an adjustable optical attenuator with a feedback structure. Specifically, we estimate the suitable attenuation value in the data processing of CV-QKD and feed it back to the adjustable optical attenuator before the detector in real time. Numerical simulation shows that the proposed countermeasure can effectively defend against homodyne detector-blinding attacks and ensure the security of the Gaussian-modulated coherent state protocol with finite-size effect. Full article

The isotopic shift of the bound-electron g factor in highly charged ions (HCI) provides a sensitive probe for testing physics beyond the Standard Model, particularly through interactions mediated by a hypothetical scalar boson. In this study, we analyze the sensitivity of this method within the Higgs portal framework, focusing on the uncertainties introduced by quantum electrodynamics corrections, including finite nuclear size, nuclear recoil, and nuclear polarization effects. All calculations are performed for the ground-state $1s$ configuration of hydrogen-like HCI, where theoretical predictions are most accurate. Using selected isotope pairs (e.g., ${\mathrm{He}}^{4/6}$, ${\mathrm{Ne}}^{20/22}$, ${\mathrm{Ca}}^{40/48}$, ${\mathrm{Sn}}^{120/132}$, ${\mathrm{Th}}^{230/232}$), we demonstrate that the dominant source of uncertainty arises from finite nuclear size corrections, which currently limit the precision of new physics searches. Our results indicate that the sensitivity of this method decreases with increasing atomic number. These findings highlight the necessity of improved nuclear radius measurements and the development of alternative approaches, such as the special differences method, to enable virtually the detection of fifth-force interactions. Full article

In this article, the role of downscaling in boosting the sensitivity of a novel label-free DNA sensor based on sub-10 nm dielectric-modulated transition metal dichalcogenide field-effect transistors (DM-TMD FET) is presented through a quantum simulation approach. The computational method is based on self-consistently solving the quantum transport equation coupled with electrostatics under ballistic transport conditions. The concept of dielectric modulation was employed as a label-free biosensing mechanism for detecting neutral DNA molecules. The computational investigation is exhaustive, encompassing the band profile, charge density, current spectrum, local density of states, drain current, threshold voltage behavior, sensitivity, and subthreshold swing. Four TMD materials were considered as the channel material, namely, MoS₂, MoSe₂, MoTe₂, and WS₂. The investigation of the scaling capability of the proposed label-free gate-all-around DM-TMDFET-based biosensor showed that gate downscaling is a valuable approach not only for producing small biosensors but also for obtaining high biosensing performance. Furthermore, we found that reducing the device size from 12 nm to 9 nm yields only a moderate improvement in sensitivity, whereas a more aggressive downscaling to 6 nm leads to a significant enhancement in sensitivity, primarily due to pronounced short-channel effects. The obtained results have significant technological implications, showing that miniaturization enhances the sensitivity of the proposed nanobiosensor. Full article

Multicolour graphene quantum dots (GQDs), from blue to orange emitting, were successfully synthesized via a one-step hydrothermal method using potassium hydrogen phthalate and o-phenylenediamine as the raw materials. After purification by silica gel column chromatography, four kinds of GQDs with maximum emission wavelengths of 420 nm (blue), 500 nm (green), 540 nm (yellow), and 555 nm (orange) were obtained, and all had a high quantum yield (9.7%, 8.8%, 9.3%, and 10.3%, respectively). The structural characterization revealed that the synthesized GQDs had a regular morphology, with a size of 2–3 nm and a thickness of 1–2 nm. The D-band-to-G-band ratio was less than 0.3, indicating that the GQDs had a high degree of graphitization. In addition, the emission peaks of the GQDs were red-shifted as the particle size increased, confirming that their luminescence was dominated by the quantum confinement effect. By analyzing the surface states and the functional groups of the multicolour GQDs, it was found that the GQDs had a similar elemental composition, which further proved that the emission wavelengths did not depend on the surface element composition, but conformed to the luminescence mechanism regulated by the quantum-limited effect. Furthermore, the four types of GQDs exhibited low cytotoxicity and good stability, suggesting their potential applications in biomarkers and for the synchronous detection of a variety of analytes. Full article

Higher-dimensional communications in optical fiber enables new possibilities, including increased transmission capacity and hyper-entangled state transfer. However, mode coupling between channels during transmission causes interference between channels and limits detection. In classical optical communications, MIMO (modes in modes out) is a means to deal with this issue; however, it is not possible to utilize this technology in quantum communications due to power limitations. Principal mode transmission is another means to deal with mode coupling and signal interference between channels. Conceptually, this can be used in quantum communications with some limitations. In this study, we numerically simulated this process using the time delay method and show how it can be implemented using two and four higher-dimensional quantum states, such as W or GHZ states. These numerical simulations are very illustrative of how the implementation proceeds. Full article

Motivated by two of the most unexpected discoveries in recent years—the detection of ArH⁺ and ${\mathrm{HeH}}^{+}$ noble gas molecules in the cold, low-pressure regions of the Universe—we investigate [He₂H]⁺ and [Ne₂H]⁺ as potentially detectable species in the interstellar medium, providing new insights into their energetic and spectral properties. These findings are crucial for advancing our understanding of noble gas chemistry in astrophysical environments. To achieve this, we employed a data-driven approach to construct a high-accuracy machine-learning potential energy surface using the reproducing kernel Hilbert space method. Training and testing datasets are generated via high-level CCSD(T)/CBS[56] quantum chemistry computations, followed by a rigorous validation protocol to ensure the reliability of the potential. The ML-PES is then used to compute vibrational states within the MCTDH framework, and assign spectral transitions for the most common isotopologues of these species in the interstellar medium. Our results are compared with previously recorded values, revealing that both cations exhibit a prominent proton-shuttle motion within the infrared spectral range, making them strong candidates for telescopic observation. This study provides a solid computational foundation, based on rigorous, fully quantum treatments, aiming to assist in the identification of these yet unobserved He/Ne hydride cations in astrophysical environments. Full article

One-dimensional (1D) nanomaterials have attracted considerable attention in the fabrication of nano-scale optoelectronic devices owing to their large specific surface areas, high surface-to-volume ratios, and directional electron transport channels. Compared to 1D metal oxide nanostructures, 1D metal sulfides have emerged as promising candidates for high-efficiency photodetectors due to their abundant surface vacancies and trap states, which facilitate oxygen adsorption and dissociation on their surfaces, thereby suppressing intrinsic carrier recombination while achieving enhanced optoelectronic performance. This review focuses on recent advancements in the performance of photodetectors fabricated using 1D binary metal sulfides as primary photosensitive layers, including nanowires, nanorods, nanotubes, and their heterostructures. Initially, the working principles of photodetectors are outlined, along with the key parameters and device types that influence their performance. Subsequently, the synthesis methods, device fabrication, and photoelectric properties of several extensively studied 1D metal sulfides and their composites, such as ZnS, CdS, SnS, Bi₂S₃, Sb₂S₃, WS₂, and SnS₂, are examined. Additionally, the current research status of 1D nanostructures of MoS₂, TiS₃, ReS₂, and In₂S₃, which are predominantly utilized as 2D materials, is explored and summarized. For systematic performance evaluation, standardized metrics encompassing responsivity, detectivity, external quantum efficiency, and response speed are comprehensively tabulated in dedicated sub-sections. The review culminates in proposing targeted research trajectories for advancing photodetection systems employing 1D binary metal sulfides. Full article

In this work, we developed a highly sensitive and reproducible electrochemiluminescent sensor based on a heterostructure of cadmium selenide quantum dots capped with 3-mercaptopropionic acid (MPA) + 3-morpholinoethanesulfonic acid (MES) (QDs CdSe) and carbon nitride nanosheets (g-C₃N₄) for the detection of H₂O₂ in lyophilized serum samples. To enhance the sensor sensitivity, g-C₃N₄ nanosheets were utilized as a platform to immobilize the QDs CdSe. An exhaustive characterization of the heterostructure was conducted, elucidating the interaction mechanism between QDs CdSe and g-C₃N₄. It was revealed that g-C₃N₄ acts as a hole (h⁺) donor, while QDs CdSe act as energy acceptors in a resonance energy transfer process, with the electrochemiluminescence emission originating from the QDs CdSe. The electrochemiluminescence intensity decreases in the presence of H₂O₂ due to the deactivation of the excited states of the QDs CdSe. This electrochemiluminescent sensor demonstrates exceptional performance for detecting H₂O₂ in aqueous systems, achieving a remarkably low limit of detection (LOD) of 1.81 nM, which is more sensitive than most reported sensors to detect H₂O₂. The applicability of the sensor was successfully tested where sub-µM levels of H₂O₂ were accurately quantified. These results highlight the potential of this electrochemiluminescent sensor as a reliable and pre-treatment-free tool for H₂O₂ detection in biochemical studies and human health applications. Full article

Damage to the photosynthetic apparatus during leaf dehydration is an indicator of the maintenance of leaf vitality and the resilience of tree seedlings to severe drought. Genipa americana is a tree widely distributed in the neotropical region but with great ecological and sociocultural importance in the south of the state of Bahia, Brazil, where its fruits are harvested from subspontaneous trees. This study aimed to compare the feasibility of the maximum quantum efficiency of photosystem II (Fv/Fm) and performance indexes derived from the JIP test, i.e., performance index on absorption basis (PI_abs) and total performance index (PI_total), for screening G. americana seedlings from different mother plants for leaf damage caused by dehydration. From leaf dehydration curves, we calculated the values of relative water content (RWC) in which Fv/Fm, PI_abs, and PI_total reach a loss of 10% and 50% in relation to the values of fully hydrated leaves. PI_total was the only parameter that revealed consistent significant differences between progenies for RWC at 50% of percentage loss. Significant differences were observed among progenies for leaf traits; however, no correlation was detected between these traits and chlorophyll fluorescence parameters. Monitoring the PI_total values during leaf dehydration is a useful tool for screening G. americana progenies in relation to their capacity to maintain leaf vitality under occasional severe droughts. Full article

Quantum computing gives direct access to the study of the real-time dynamics of quantum many-body systems. In principle, it is possible to directly calculate non-equal-time correlation functions, from which one can detect interesting phenomena, such as the presence of quantum scars or dynamical quantum phase transitions. In practice, these calculations are strongly affected by noise, due to the complexity of the required quantum circuits. As a testbed for the evaluation of the real-time evolution of observables and correlations, the dynamics of the ${\mathbb{Z}}_n$ Schwinger model in a one-dimensional lattice is considered. To control the computational cost, we adopt a quantum–classical strategy that reduces the dimensionality of the system by restricting the dynamics to the Dirac vacuum sector and optimizes the embedding into a qubit model by minimizing the number of three-qubit gates. The time evolution of particle-density operators in a non-equilibrium quench protocol is both simulated in a bare noisy condition and implemented on a physical IBM quantum device. In either case, the convergence towards a maximally mixed state is targeted by means of different error mitigation techniques. The evaluation of the particle-density correlation shows a well-performing post-processing error mitigation for properly chosen coupling regimes. Full article

In this study, a parameterized quantum circuit (PQC) is applied for anomaly detection, a crucial process to identify unusual patterns or outliers in data. PQC is a quantum circuit with trainable parameters linked to quantum gates, which are iteratively optimized by classical optimizers to ensure that the circuit’s output fulfills its objectives. This is analogous to the way of using trainable parameters, such as weights adjusted in classical machine learning and neural network models. We used the amplitude−embedding mechanism with classical data into quantum states of qubits. These states are fed into PQC, which contains strongly entangled layers, and the circuit is trained to determine whether an anomaly exists. As anomaly detection datasets are often imbalanced, resampling techniques, such as random oversampling, the synthetic minority oversampling technique (SMOTE), random undersampling, and Tomek-Link undersampling, are applied to reduce the imbalance. The proposed PQC and various resampling techniques were compared using the public Musk dataset for anomaly detection. Their combination was also compared with the combination of the classical autoencoder and the classical isolation forest model in terms of the F1 score. By analyzing the comparison results, the advantages and disadvantages of PQC for future research studies were determined. Full article

This paper presents a multiparty quantum private comparison (MQPC) protocol that facilitates multiple users to compare the equality of their private inputs while preserving the confidentiality of each input through the principles of quantum mechanics. In our approach, users initially convert their secret integers into binary representations, which are then encoded into single photons that act as carriers of the information. These encoded single-photon states undergo encryption via rotational operations, effectively obscuring the original inputs before transmission to a semi-honest third party (TP). The TP decrypts the quantum states and conducts Z-basis measurements to derive the comparison results. To enhance security, the protocol incorporates decoy photons, enabling participants to detect potential eavesdropping on the quantum channel. Importantly, even if the TP or other participants attempt to glean insights into each other’s inputs, the encryption via rotational operations ensures that private information remains inaccessible. This protocol demonstrates significant advancements in practicality compared to existing MQPC frameworks that rely on complex quantum technologies, such as entanglement swapping and multi-particle entanglement. By leveraging the simplicity of single photons, rotation operations, and Z-basis measurements, our protocol is more accessible for implementation. Full article

Vector beams (VBs) with longitudinally varying polarization states provide a new dimension for light field manipulation, and promote the advancements of related areas such as optical metrology, longitudinal depth detection, and classical and quantum communications. In this study, we propose a half-wave plate dielectric metasurface based on a spatial partitioning method, realizing the longitudinal manipulation of the polarization states of higher-order Poincaré (HOP) beams by changing the elliptical polarization state of the incident light and selecting the appropriate propagation distances. The metasurface is composed of two sub-metasurfaces, and the two sets of a-Si:H meta-atoms are uniformly arranged on concentric rings of different radii with an equal interval. The propagation and Pancharatnam–Berry phases are utilized to construct the axicon and helical phase profiles. As a result, two sub-metasurfaces, respectively, generate the first- and second-order VBs with longitudinally varying polarization states. The polarization states of generated VBs correspond to points on different meridians of nth-order HOP spheres from the south pole to the north pole. The consistency between the theoretical and simulated results demonstrates the feasibility and practicability of the proposed method. This study provides an innovative strategy to extend the modulation of light fields from two-dimensional to three-dimensional space. Full article

Skin cancer is a class of disorder defined by the growth of abnormal cells on the body. Accurately identifying and diagnosing skin lesions is quite difficult because skin malignancies share many common characteristics and a wide range of morphologies. To face this challenge, deep learning algorithms have been proposed. Deep learning algorithms have shown diagnostic efficacy comparable to dermatologists in the discipline of images-based skin lesion diagnosis in recent research articles. This work proposes a novel deep learning algorithm to detect skin cancer. The proposed CAD-Skin system detects and classifies skin lesions using deep convolutional neural networks and autoencoders to improve the classification efficiency of skin cancer. The CAD-Skin system was designed and developed by the use of the modern preprocessing approach, which is a combination of multi-scale retinex, gamma correction, unsharp masking, and contrast-limited adaptive histogram equalization. In this work, we have implemented a data augmentation strategy to deal with unbalanced datasets. This step improves the model’s resilience to different pigmented skin conditions and avoids overfitting. Additionally, a Quantum Support Vector Machine (QSVM) algorithm is integrated for final-stage classification. Our proposed CAD-Skin enhances category recognition for different skin disease severities, including actinic keratosis, malignant melanoma, and other skin cancers. The proposed system was tested using the PAD-UFES-20-Modified, ISIC-2018, and ISIC-2019 datasets. The system reached accuracy rates of 98%, 99%, and 99%, consecutively, which is higher than state-of-the-art work in the literature. The minimum accuracy achieved for certain skin disorder diseases reached 97.43%. Our research study demonstrates that the proposed CAD-Skin provides precise diagnosis and timely detection of skin abnormalities, diversifying options for doctors and enhancing patient satisfaction during medical practice. Full article