arXiv subjects

Li Feng

Publications and source records attributed to Li Feng.

At least 37 records · Page 2Linked to original sources

arXiv2025

Ultrahigh-Energy Gamma-ray Emission Associated with Black Hole-Jet Systems

Black holes (BH), one of the most intriguing objects in the universe, can manifest themselves through electromagnetic radiation initiated by the accretion flow. Some stellar-mass BHs drive relativistic jets when accreting matter from their companion stars, forming microquasars. Non-thermal emission from the radio to tera-electronvolt (TeV) gamma-ray band has been observed from microquasars, indicating the acceleration of relativistic particles. Here we report detection of four microquasars (SS 433, V4641 Sgr, GRS 1915+105, MAXI J1820+070) of spectrum extending to the ultrahigh-energy (UHE; photon energy $E>100$ TeV) band and one microquasar (Cygnus X-1) of spectrum approaching 100 TeV, using the Large High Altitude Air Shower Observatory (LHAASO). Notably, the total emission associated with SS 433 cannot be interpreted with a single leptonic component. In the UHE band, its emission is in spatial coincidence with a giant atomic cloud, which is consistent with a hadronic origin. An elongated source is discovered from V4641 Sgr with the spectrum continuing up to 800 TeV. The detection of UHE gamma rays demonstrates that accreting BHs and their environments can operate as extremely efficient accelerators of particles out of 1 peta-electronvolt (PeV), suggesting microquasars to be important contributors to Galactic cosmic rays especially around the `knee' region.

astro-ph.HE↗

arXiv2025

A Survey of Quantum Transformers: Architectures, Challenges and Outlooks

Quantum Transformers integrate the representational power of classical Transformers with the computational advantages of quantum computing. Since 2022, research in this area has rapidly expanded, giving rise to diverse technical paradigms and early applications. To address the growing need for consolidation, this paper presents the first comprehensive, systematic, and in-depth survey of quantum Transformer models. First, we delineate the research scope, focusing on improving Transformer parts with quantum methods, and introduce foundational concepts in classical Transformers and quantum machine learning. Then we organize existing studies into two main paradigms: PQC-based and QLA-based, with PQC-based paradigm further divided into QKV-only Quantum Mapping, Quantum Pairwise Attention, Quantum Holistic Attention. and Quantum-Assisted Optimization, analyzing their core mechanisms and architectural traits. We also summarize empirical results that demonstrate preliminary quantum advantages, especially on small-scale tasks or resource-constrained settings. Following this, we examine key technical challenges, such as complexity-resource trade-offs, scalability and generalization limitations, and trainability issues including barren plateaus, and provide potential solutions, including quantumizing classical transformer variants with lower complexity, hybrid designs, and improved optimization strategies. Finally, we propose several promising future directions, e.g., scaling quantum modules into large architectures, applying quantum Transformers to domains with inherently quantum data (e.g., physics, chemistry), and developing theory-driven designs grounded in quantum information science. This survey will help researchers and practitioners quickly grasp the overall landscape of current quantum Transformer research and promote future developments in this emerging field.

quant-ph↗

arXiv2025

PeriodNet: Boosting the Potential of Attention Mechanism for Time Series Forecasting

The attention mechanism has demonstrated remarkable potential in sequence modeling, exemplified by its successful application in natural language processing with models such as Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformer (GPT). Despite these advancements, its utilization in time series forecasting (TSF) has yet to meet expectations. Exploring a better network structure for attention in TSF holds immense significance across various domains. In this paper, we present PeriodNet with a brand new structure to forecast univariate and multivariate time series. PeriodNet incorporates period attention and sparse period attention mechanism for analyzing adjacent periods. It enhances the mining of local characteristics, periodic patterns, and global dependencies. For efficient cross-variable modeling, we introduce an iterative grouping mechanism which can directly reduce the cross-variable redundancy. To fully leverage the extracted features on the encoder side, we redesign the entire architecture of the vanilla Transformer and propose a period diffuser for precise multi-period prediction. Through comprehensive experiments conducted on eight datasets, we demonstrate that PeriodNet outperforms six state-of-the-art models in both univariate and multivariate TSF scenarios in terms of mean square error and mean absolute error. In particular, PeriodNet achieves a relative improvement of 22% when forecasting time series with a length of 720, in comparison to other models based on the conventional encoder-decoder Transformer architecture.

cs.LG↗

arXiv2025

A Giant Peanut-shaped Ultra-High-Energy Gamma-Ray Emitter Off the Galactic Plane

Ultra-high-energy (UHE), exceeding 100 TeV (10^12 electronvolts), γ-rays manifests extreme particle acceleration in astrophysical sources. Recent observations by γ-ray telescopes, particularly by the Large High Altitude Air Shower Observatory (LHAASO), have revealed a few tens of UHE sources, indicating numerous Galactic sources capable of accelerating particles to PeV (10^15 electronvolts) energies. However, discerning the dominant acceleration mechanisms (leptonic versus hadronic), the relative contributions of specific source classes, and the role of particle transport in shaping their observed emission are central goals of modern UHE astrophysics. Here we report the discovery of a giant UHE γ-ray emitter at -17.5° off the Galactic plane - a region where UHE γ-ray sources are rarely found. The emitter exhibits a distinctive asymmetric shape, resembling a giant "Peanut" spanning 0.45° \times 4.6°, indicative of anisotropic particle distribution over a large area. A highly aged millisecond pulsar (MSP) J0218+4232 is the sole candidate accelerator positionally coincident with the Peanut region. Its association with UHE γ-rays extending to 0.7 PeV, if confirmed, would provide the first evidence of a millisecond pulsar powering PeV particles. Such a finding challenges prevailing models, which posit that millisecond pulsars cannot sustain acceleration to PeV energies. The detection reveals fundamental gaps in understanding particle acceleration, cosmic-ray transport, and interstellar magnetic field effects, potentially revealing new PeV accelerator (PeVatron) classes.

astro-ph.HE↗

arXiv2025

Multiwavelength spectroscopic observations of a quiescent prominence

In this paper we focus on the analysis of the multiwavelength spectroscopic observations of a quiescent prominence. The spectral and geometrical parameters in the prominence were derived and used to constrain the NLTE radiative transfer models. Applying this method with multiwavelength observations provides a good opportunity to reduce the large range of thermodynamic parameters in solar prominences. We used time-slice and optical flow methods in order to derive the plane-of-sky (POS) velocities, and used gravity center and peak position methods on Mg II h&k and H I Ly-alpha profiles to compute the line-of-sight (LOS) velocities. We used the integrated intensities and FWHM values of the H-alpha, Ca II H, and Mg II h&k lines to compare with the NLTE radiative transfer computations. Ionization degree and thickness of the prominence plasma could be further derived. Opposite flows are observed along two strands between prominence barbs. The POS velocity can reach 20 km/s and the largest LOS velocity is > 90 km/s. The derived electron densities range from 6.5e9 cm-3 to 2.7e10 cm-3, and the derived total hydrogen densities range from 7.4e9 cm-3 to 6.6e10 cm-3. The temperature ranges from 7 000 to 14 000 K. The ionization degree of hydrogen is in the range of 0.40 to 0.91. The comparison between averaged and modeled profiles of Mg II and Ly-alpha lines shows that macro-velocities of 15 km/s and 20 km/s are required, respectively. The bulk motions among prominence barbs indicate that the prominence plasma is not confined within magnetic dips but exhibits a large-scale behavior. The presence of high-speed cool plasma flows, along with a wide range of plasma densities and temperatures, suggests that the prominence plasma is far from thermodynamic equilibrium and is inherently dynamic in nature.

astro-ph.SR↗

arXiv2025

Multisession Longitudinal Dynamic MRI Incorporating Patient-Specific Prior Image Information Across Time

Serial Magnetic Resonance Imaging (MRI) exams are often performed in clinical practice, offering shared anatomical and motion information across imaging sessions. However, existing reconstruction methods process each session independently without leveraging this valuable longitudinal information. In this work, we propose a novel concept of longitudinal dynamic MRI, which incorporates patient-specific prior images to exploit temporal correlations across sessions. This framework enables progressive acceleration of data acquisition and reduction of scan time as more imaging sessions become available. The concept is demonstrated using the 4D Golden-angle RAdial Sparse Parallel (GRASP) MRI, a state-of-the-art dynamic imaging technique. Longitudinal reconstruction is performed by concatenating multi-session time-resolved 4D GRASP datasets into an extended dynamic series, followed by a low-rank subspace-based reconstruction algorithm. A series of experiments were conducted to evaluate the feasibility and performance of the proposed method. Results show that longitudinal 4D GRASP reconstruction consistently outperforms standard single-session reconstruction in image quality, while preserving inter-session variations. The approach demonstrated robustness to changes in anatomy, imaging intervals, and body contour, highlighting its potential for improving imaging efficiency and consistency in longitudinal MRI applications. More generally, this work suggests a new context-aware imaging paradigm in which the more we see a patient, the faster we can image.

eess.IV↗

arXiv2025

Probing Solar Polar Regions

The magnetic fields and dynamical processes in the solar polar regions play a crucial role in the solar magnetic cycle and in supplying mass and energy to the fast solar wind, ultimately being vital in controlling solar activities and driving space weather. Despite numerous efforts to explore these regions, to date no imaging observations of the Sun's poles have been achieved from vantage points out of the ecliptic plane, leaving their behavior and evolution poorly understood. This observation gap has left three top-level scientific questions unanswered, 1) How does the solar dynamo work and drive the solar magnetic cycle? 2) What drives the fast solar wind? 3) How do space weather processes globally originate from the Sun and propagate throughout the solar system? The Solar Polar-orbit Observatory (SPO) mission, a solar polar exploration spacecraft, is proposed to address these three unanswered scientific questions by imaging the Sun's poles from high heliolatitudes. In order to achieve its scientific goals, SPO will carry six remote-sensing and four in-situ instruments to measure the vector magnetic fields and Doppler velocity fields in the photosphere, to observed the Sun in the extreme ultraviolet, X-ray, and radio wavelengths, to image the corona and the heliosphere up to 45 $R_\odot$, and to perform in-situ detection of magnetic fields, and low- and high-energy particles in the solar wind.

astro-ph.SR↗

arXiv2025

The Role of Far-side Magnetic Structures in Modeling 2024 Solar Eclipse

The corona is a crucial region that connects the solar surface to the solar wind and serves as the primary site of solar activity. The 2024 total solar eclipse (TSE) provides a unique opportunity to investigate the large-scale coronal structure. Combined with TSE observations, we study the impact of the magnetic structure of the far-side active region, located in the eastern hemisphere of the Sun that has not yet rotated into the Earth Field-of-View (FoV), on a global Magnetohydrodynamic (MHD) simulation. To address the limitation of single-view measurements in the routine synoptic map, we correct the magnetic field in the far-side region by incorporating full-disk magnetograms measured several days after the TSE, allowing us to capture the temporal evolution of the photospheric magnetic field in near real-time. Simulation results demonstrate that the local magnetic field in the far-side active region can significantly influence the global coronal structure by altering the position of the heliospheric current sheet (HCS), and further affect the global distribution of plasma parameters, even in polar regions. A comparison of the simulation results with white-light (WL) TSE + LASCO C2 observations and in situ measurements by the Parker Solar Probe (PSP) reveals that the composite synoptic map improves the accuracy of coronal modeling. This work provides robust support for advancing our understanding of coronal evolution, as well as deepens the link between the photosphere and large-scale coronal structure. Furthermore, it establishes a theoretical foundation for the future development of multi-view, stereoscopic measurements of the photospheric magnetic field.

astro-ph.SR↗

arXiv2025

Early Evolution of the Cavity and Core of a Coronal Mass Ejection in the Inner Corona

Coronal mass ejections (CMEs) typically exhibit a three-component structure in white-light (WL) coronagraphs. Utilizing the seamless observations of the inner corona ($\le$ 3 R$_\odot$), we have revealed the early evolution of the cavity and core of a CME starting at $\sim$18:20 UT on 2014 October 14. The CME originates from a hot channel (HC), which appears as the bright core and compresses the cavity in WL images. Specifically, most of the dark cavity in WL is filled by bright loop-like structures in 174 Å. The differential emission measure (DEM) analysis indicates that the electron temperature decreases from the core ($\sim$13.4 MK) to the cavity ($\sim$1.35 MK), and the CME cavity is significantly cooler than that enshrouding a prominence ($\ge$ 2 MK). The effective temperature of the cavity increases over time in general, probably due to the compression by the HC expansion. The evolution of the CME bright core includes slow-rise, fast-rise (up to $\sim$330 km s$^{-1}$), and residual-acceleration phases. The cavity exhibits an evolution similar to the core but lags by $\sim$4 minutes, with a lower speed peaking at $\sim$220 km s$^{-1}$. Moreover, the 2D radial speed distribution exhibits the highest speeds at the core apex. The kinematical results further confirm the compression of the cavity. The present event supports the new explanation of the CME structures, i.e., the magnetic flux rope (MFR), which is proxied by the HC, is only responsible for the core, while the cavity is likely a low-density region between the CME front and the MFR.

astro-ph.SR↗

arXiv2025

Self-Supervised Joint Reconstruction and Denoising of T2-Weighted PROPELLER MRI of the Lungs at 0.55T

Purpose: This study aims to improve 0.55T T2-weighted PROPELLER lung MRI through a self-supervised joint reconstruction and denoising model. Methods: T2-weighted 0.55T lung MRI dataset including 44 patients with previous covid infection were used. A self-supervised learning framework was developed, where each blade of the PROPELLER acquisition was split along the readout direction into two partitions. One subset trains the unrolled reconstruction network, while the other subset is used for loss calculation, enabling self-supervised training without clean targets and leveraging matched noise statistics for denoising. For comparison, Marchenko-Pastur Principal Component Analysis (MPPCA) was performed along the coil dimension, followed by conventional parallel imaging reconstruction. The quality of the reconstructed lung MRI was assessed visually by two experienced radiologists independently. Results: The proposed self-supervised model improved the clarity and structural integrity of the lung images. For cases with available CT scans, the reconstructed images demonstrated strong alignment with corresponding CT images. Additionally, the proposed model enables further scan time reduction by requiring only half the number of blades. Reader evaluations confirmed that the proposed method outperformed MPPCA-denoised images across all categories (Wilcoxon signed-rank test, p<0.001), with moderate inter-reader agreement (weighted Cohen's kappa=0.55; percentage of exact and within +/-1 point agreement=91%). Conclusion: By leveraging intrinsic structural redundancies between two disjoint splits of k-space subsets, the proposed self-supervised learning model effectively reconstructs the image while suppressing the noise for 0.55T T2-weighted lung MRI with PROPELLER sampling.

eess.IV↗

arXiv2025

Responses of a Coronal Hole to a Fast Flare-Driven Coronal Wave

Coronal waves, significant solar phenomena, act as diagnostic tools for scientists studying solar atmosphere properties. Here, we present a novel observation detailing how a coronal wave event, associated with an X5.0 class flare, influenced the properties of an adjacent coronal hole through interaction. The coronal wave was observed in both extreme ultraviolet observations from the Atmospheric Imaging Assembly aboard the Solar Dynamics Observatory and Lyman-alpha observations from the Solar Disk Imager aboard the Advanced Space-based Solar Observatory. Utilizing the method of differential emission measure, we found that as the coronal wave passed through, the adjacent coronal hole experienced an increase in temperature from 1.31 to 1.43 MK and a rise in density from $\sim$1.62$\times10^{8}$ to 1.76$\times10^{8}$ cm$^{-3}$ within the rising period of $\sim$7 minutes. Subsequently, after the wave passed, the entire coronal hole transitioned to a new state with a slight temperature increase and a 14$\%$ decrease in density, with more pronounced changes observed at the coronal hole's boundary. Taking into account the impacts of radiative loss and heat conduction, the coronal wave was estimated to provide an average energy of 2.2$\times10^{8}$ erg cm$^{-2}$ to the coronal hole during the short rising period. This study highlights the identification of the coronal wave in both extreme ultraviolet and Lyman-alpha observations, shedding light on the significant energy input, particularly within the coronal hole. These findings provide new insights into better understanding kinematics of fast coronal waves, energy transfer processes open versus closed magnetic topologies, and the possible acceleration of solar winds.

astro-ph.SR↗

arXiv2025

Understanding the Lyα Emission Observed by the Solar Disk Imager Aboard the Advanced Space-based Solar Observatory

The H I Lyman-alpha (Ly$α$) emission, with a wavelength of 1216 Å, is the brightest solar ultraviolet (UV) line. However, comprehensive observations of the Ly$α$ emission line across the full solar disk remain limited. As part of the ASO-S mission, the Solar Disk Imager (SDI) has successfully captured full-disk images in the Ly$α$ band. Gaussian fitting of SDI's spectral response function (SRF) yields a full width at half maximum (FWHM) of approximately 85 Å, which is significantly broader than the distance of Si III line at 1206 Å and the Ly$α$ line. Thus, the emission contribution of Si III to the SDI Ly$α$ passband needs to be considered. For flares, in practice, we calculated the integrated intensity ratio $I$(Si III)/$I$(Ly$α$) by analyzing spectral observations from the SOLSTICE instrument. It yields values between 1.7% and 14.6%. Empirically, the ratio is proportional to the SXR flux. Further analysis of spectral data from the SUMER instrument reveals that the ratio $I$(Si III)/$I$(Ly$α$) is approximately 0.5% for prominences, 0.7%--0.9% for the inner disk, and 1.4%--1.9% close to the limb. These findings suggest that $I$(Si III)/$I$(Ly$α$) is minimal for prominences and the inner disk, and the varying ratios across regions align with the center-to-limb variation of the Si III and Ly$α$ lines. Additionally, we compared Ly$α$ image intensity with 304 Å, 1600 Å, and 1700 Å observations from AIA, as well as H$α$ from CHASE, in multiple regions (a prominence region, two active regions, and a quiet region). A relatively higher correlation of about 85% is found between Ly$α$ and 304 Å in active regions, whereas in the quiet region and prominence, their correlation coefficients are about 55%.

astro-ph.SR↗

arXiv2025

Tracking an eruptive intermediate prominence originating from the farside of the Sun

In this paper, we carry out multiwavelength and multiview observations of the eruption of an intermediate prominence originating from the farside of the Sun on 2023 March 12. The southeast footpoint of the prominence is located in active region (AR) 13252. The eruption generates a B7.8 class flare and a partial halo coronal mass ejection (CME). The prominence takes off at 02:00 UT and accelerates for nearly three hours. Rotation of the southeast leg of the prominence in the counterclockwise direction is revealed by spectroscopic and imaging observations. The apex of the prominence changes from a smooth loop to a cusp structure during the rising motion and the northwest leg displays a drift motion after 04:30 UT, implying a writhing motion. Hence, the prominence eruption is most likely triggered by ideal kink instability. For the first time, we apply the Graduated Cylindrical Shell (GCS) modeling in three-dimensional reconstruction and tracking of the prominence for nearly two hours. Both the source region (110$\degr$E, 43$\degr$N) and northwest footpoint (162$\degr$E, 44$\degr$N) are located. The edge-on and face-on angular widths of the prominence are $\sim$6$\degr$ and $\sim$86$\degr$, respectively. The axis has a tilt angle of $\sim$70$\degr$ with the meridian. The heliocentric distance of the prominence leading edge increases from $\sim$1.26\,$R_{\sun}$ to $\sim$2.27\,$R_{\sun}$. The true speed of the CME increases from $\sim$610 to $\sim$849 km s$^{-1}$.

astro-ph.SR↗

arXiv2025

Self-Supervised Noise Adaptive MRI Denoising via Repetition to Repetition (Rep2Rep) Learning

Purpose: This work proposes a novel self-supervised noise-adaptive image denoising framework, called Repetition to Repetition (Rep2Rep) learning, for low-field (<1T) MRI applications. Methods: Rep2Rep learning extends the Noise2Noise framework by training a neural network on two repeated MRI acquisitions, using one repetition as input and another as target, without requiring ground-truth data. It incorporates noise-adaptive training, enabling denoising generalization across varying noise levels and flexible inference with any number of repetitions. Performance was evaluated on both synthetic noisy brain MRI and 0.55T prostate MRI data, and compared against supervised learning and Monte Carlo Stein's Unbiased Risk Estimator (MC-SURE). Results: Rep2Rep learning outperforms MC-SURE on both synthetic and 0.55T MRI datasets. On synthetic brain data, it achieved denoising quality comparable to supervised learning and surpassed MC-SURE, particularly in preserving structural details and reducing residual noise. On the 0.55T prostate MRI dataset, a reader study showed radiologists preferred Rep2Rep-denoised 2-average images over 8-average noisy images. Rep2Rep demonstrated robustness to noise-level discrepancies between training and inference, supporting its practical implementation. Conclusion: Rep2Rep learning offers an effective self-supervised denoising for low-field MRI by leveraging routinely acquired multi-repetition data. Its noise-adaptivity enables generalization to different SNR regimes without clean reference images. This makes Rep2Rep learning a promising tool for improving image quality and scan efficiency in low-field MRI.

eess.IV↗

arXiv2025

Learned Primal Dual Splitting for Self-Supervised Noise-Adaptive MRI Reconstruction

Magnetic resonance imaging (MRI) reconstruction has largely been dominated by deep neural networks (DNN); however, many state-of-the-art architectures use black-box structures, which hinder interpretability and improvement. Here, we propose an interpretable DNN architecture for self-supervised MRI reconstruction and denoising by directly parameterizing and learning the classical primal-dual splitting, dubbed LPDSNet. This splitting algorithm allows us to decouple the observation model from the signal prior. Experimentally, we show other interpretable architectures without this decoupling property exhibit failure in the self-supervised learning regime. We report state-of-the-art self-supervised joint MRI reconstruction and denoising performance and novel noise-level generalization capabilities, where in contrast black-box networks fail to generalize.

eess.IV↗

arXiv2025

Radio dimming associated with filament eruptions in the meter and decimeter wavebands

Filament eruptions are considered to be a common phenomenon on the Sun and other stars, yet they are rarely directly imaged in the meter and decimeter wavebands. Using imaging data from the DAocheng solar Radio Telescope (DART) in the 150-450 MHz frequency range, we present two eruptive filaments that manifest as radio dimmings (i.e., emission depressions). Simultaneously, portions of these eruptive filaments are discernible as dark features in the chromospheric images. The sun-as-a-star flux curves of brightness temperature, derived from the DART images, exhibit obvious radio dimmings. The dimming depths range from 1.5% to 8% of the background level and show a negative correlation with radio frequencies and a positive correlation with filament areas. Our investigation suggests that radio dimming is caused by free-free absorption during filament eruptions obscuring the solar corona. This may provide a new method for detecting stellar filament eruptions.

astro-ph.SR↗

arXiv2025

Quantum Complex-Valued Self-Attention Model

Self-attention has revolutionized classical machine learning, yet existing quantum self-attention models underutilize quantum states' potential due to oversimplified or incomplete mechanisms. To address this limitation, we introduce the Quantum Complex-Valued Self-Attention Model (QCSAM), the first framework to leverage complex-valued similarities, which captures amplitude and phase relationships between quantum states more comprehensively. To achieve this, QCSAM extends the Linear Combination of Unitaries (LCUs) into the Complex LCUs (CLCUs) framework, enabling precise complex-valued weighting of quantum states and supporting quantum multi-head attention. Experiments on MNIST and Fashion-MNIST show that QCSAM outperforms recent quantum self-attention models, including QKSAN, QSAN, and GQHAN. With only 4 qubits, QCSAM achieves 100% and 99.2% test accuracies on MNIST and Fashion-MNIST, respectively. Furthermore, we evaluate scalability across 3-8 qubits and 2-4 class tasks, while ablation studies validate the advantages of complex-valued attention weights over real-valued alternatives. This work advances quantum machine learning by enhancing the expressiveness and precision of quantum self-attention in a way that aligns with the inherent complexity of quantum mechanics.

quant-ph↗

arXiv2025

HQViT: Hybrid Quantum Vision Transformer for Image Classification

Transformer-based architectures have revolutionized the landscape of deep learning. In computer vision domain, Vision Transformer demonstrates remarkable performance on par with or even surpassing that of convolutional neural networks. However, the quadratic computational complexity of its self-attention mechanism poses challenges for classical computing, making model training with high-dimensional input data, e.g., images, particularly expensive. To address such limitations, we propose a Hybrid Quantum Vision Transformer (HQViT), that leverages the principles of quantum computing to accelerate model training while enhancing model performance. HQViT introduces whole-image processing with amplitude encoding to better preserve global image information without additional positional encoding. By leveraging quantum computation on the most critical steps and selectively handling other components in a classical way, we lower the cost of quantum resources for HQViT. The qubit requirement is minimized to $O(log_2N)$ and the number of parameterized quantum gates is only $O(log_2d)$, making it well-suited for Noisy Intermediate-Scale Quantum devices. By offloading the computationally intensive attention coefficient matrix calculation to the quantum framework, HQViT reduces the classical computational load by $O(T^2d)$. Extensive experiments across various computer vision datasets demonstrate that HQViT outperforms existing models, achieving a maximum improvement of up to $10.9\%$ (on the MNIST 10-classification task) over the state of the art. This work highlights the great potential to combine quantum and classical computing to cope with complex image classification tasks.

cs.CV↗