arXiv subjects

Haibin Zhang

Publications and source records attributed to Haibin Zhang.

At least 37 records · Page 2Linked to original sources

arXiv2025

A Joint Search for the Electromagnetic Counterpart to the Gravitational-Wave Binary Black-Hole Merger Candidate S250328ae with the Dark Energy Camera and the Prime Focus Spectrograph

The first detection of an optical counterpart to a gravitational wave signal revealed that collaborative efforts between instruments with different specializations provide a unique opportunity to acquire impactful multi-messenger data. We present results of such a joint search with the Dark Energy Camera (DECam) and Prime Focus Spectrograph (PFS) for the optical counterpart of the LIGO-Virgo-KAGRA event S250328ae, a binary black hole merger candidate of high significance detected at a distance of 511$\pm$82 Mpc and localized within an area of 3 (15) square degrees at 50% (90%) confidence. We observed the 90% confidence area with DECam and identified 36 high-confidence transient candidates after image processing, candidate selection, and candidate vetting. We observed with PFS to obtain optical spectra of DECam candidates, Swift-XRT candidates, and potential host galaxies of S250328ae. In total, 3897 targets were observed by seven pointings covering ~50% of the 90% confidence area. After template fitting and visual inspection, we identified 12 SNe, 159 QSOs, 2975 galaxies, and 131 stars. With the joint observations of DECam and PFS, we found variability in 12 SNe, 139 QSOs, 37 galaxies, and 2 stars. We do not identify any confident optical counterparts, though the association is not ruled out for three variable candidates that are not observed by PFS and 6 QSO candidates without clear variability if the optical counterpart of S250328ae is faint. Despite the lack of confident optical counterparts, this paper serves as a framework for future collaborations between wide-field imagers and multi-object spectrographs to maximize multi-messenger analyses.

astro-ph.HE↗

arXiv2025

Can Optical Denoising Clean Sonar Images? A Benchmark and Fusion Approach

Object detection in sonar images is crucial for underwater robotics applications including autonomous navigation and resource exploration. However, complex noise patterns inherent in sonar imagery, particularly speckle, reverberation, and non-Gaussian noise, significantly degrade detection accuracy. While denoising techniques have achieved remarkable success in optical imaging, their applicability to sonar data remains underexplored. This study presents the first systematic evaluation of nine state-of-the-art deep denoising models with distinct architectures, including Neighbor2Neighbor with varying noise parameters, Blind2Unblind with different noise configurations, and DSPNet, for sonar image preprocessing. We establish a rigorous benchmark using five publicly available sonar datasets and assess their impact on four representative detection algorithms: YOLOX, Faster R-CNN, SSD300, and SSDMobileNetV2. Our evaluation addresses three unresolved questions: first, how effectively optical denoising architectures transfer to sonar data; second, which model families perform best against sonar noise; and third, whether denoising truly improves detection accuracy in practical pipelines. Extensive experiments demonstrate that while denoising generally improves detection performance, effectiveness varies across methods due to their inherent biases toward specific noise types. To leverage complementary denoising effects, we propose a mutually-supervised multi-source denoising fusion framework where outputs from different denoisers mutually supervise each other at the pixel level, creating a synergistic framework that produces cleaner images.

cs.CV↗

arXiv2025

RRTL: Red Teaming Reasoning Large Language Models in Tool Learning

While tool learning significantly enhances the capabilities of large language models (LLMs), it also introduces substantial security risks. Prior research has revealed various vulnerabilities in traditional LLMs during tool learning. However, the safety of newly emerging reasoning LLMs (RLLMs), such as DeepSeek-R1, in the context of tool learning remains underexplored. To bridge this gap, we propose RRTL, a red teaming approach specifically designed to evaluate RLLMs in tool learning. It integrates two novel strategies: (1) the identification of deceptive threats, which evaluates the model's behavior in concealing the usage of unsafe tools and their potential risks; and (2) the use of Chain-of-Thought (CoT) prompting to force tool invocation. Our approach also includes a benchmark for traditional LLMs. We conduct a comprehensive evaluation on seven mainstream RLLMs and uncover three key findings: (1) RLLMs generally achieve stronger safety performance than traditional LLMs, yet substantial safety disparities persist across models; (2) RLLMs can pose serious deceptive risks by frequently failing to disclose tool usage and to warn users of potential tool output risks; (3) CoT prompting reveals multi-lingual safety vulnerabilities in RLLMs. Our work provides important insights into enhancing the security of RLLMs in tool learning.

cs.CL↗

arXiv2025

Cosmic Himalayas: The Highest Quasar Density Peak Identified in a 10,000 deg$^2$ Sky with Spatial Discrepancies between Galaxies, Quasars, and IGM HI

We report the identification of a quasar overdensity in the BOSSJ0210 field, dubbed Cosmic Himalayas, consisting of 11 quasars at $z=2.16-2.20$, the densest overdensity of quasars ($17σ$) in the $\sim$10,000 deg$^2$ of the Sloan Digital Sky Survey. We present the spatial distributions of galaxies and quasars and an HI absorption map of the intergalactic medium (IGM). On the map of 465 galaxies selected from the MAMMOTH-Subaru survey, we find two galaxy density peaks that do not fall on the quasar overdensity but instead exist at the northwest and southeast sides, approximately 25 $h^{-1}$ comoving-Mpc apart from the quasar overdensity. With a spatial resolution of 15 $h^{-1}$ comoving Mpc in projection, we produce a three-dimensional HI tomography map by the IGM Ly$α$ forest in the spectra of 23 SDSS/eBOSS quasars behind the quasar overdensity. Surprisingly, the quasar overdensity coincides with neither an absorption peak nor a transmission peak of IGM HI but lies near the border separating opaque and transparent volumes, with the more luminous quasars located in an environment with lesser IGM HI. Hence remarkably, the overdensity region traced by the 11 quasars, albeit all in coherently active states, has no clear coincidence with peaks of galaxies or HI absorption densities. Current physical scenarios with mixtures of HI overdensities and quasar photoionization cannot fully interpret the emergence of Cosmic Himalayas, suggesting this peculiar structure is an excellent laboratory to unveil the interplay between galaxies, quasars, and the IGM.

astro-ph.GA↗

arXiv2025

Ramp Up NTT in Record Time using GPU-Accelerated Algorithms and LLM-based Code Generation

Homomorphic encryption (HE) is a core building block in privacy-preserving machine learning (PPML), but HE is also widely known as its efficiency bottleneck. Therefore, many GPU-accelerated cryptographic schemes have been proposed to improve the performance of HE. However, these methods often require complex modifications tailored to specific algorithms and are tightly coupled with specific GPU and operating systems. It is interesting to ask how to generally offer more practical GPU-accelerated cryptographic algorithm implementations. Given the powerful code generation capabilities of large language models (LLMs), we aim to explore their potential to automatically generate practical GPU-friendly algorithm code using CPU-friendly code. In this paper, we focus on number theoretic transform (NTT) -- the core mechanism of HE. We first develop and optimize a GPU-friendly NTT (GNTT) family that exploits PyTorch's fast matrix computation and precomputation, achieving an approximately 62x speedup -- a significant boost over existing ones. Then we explore GPU-friendly code generation using various LLMs, including DeepSeek-R1, OpenAI o1 and o3-mini. We discover many interesting findings throughout the process. For instance, somewhat surprisingly, our experiments demonstrate that DeepSeek-R1 significantly outperforms OpenAI o3-mini and o1, but still cannot beat our optimized protocol. The findings provide valuable insights for turbocharging PPML and enhancing code generation capabilities of LLMs. Codes are available at: https://github.com/LMPC-Lab/GenGPUCrypto.

cs.CR↗

arXiv2025

MAMMOTH-Subaru IV. Large Scale Structure and Clustering Analysis of Ly$α$ Emitters and Ly$α$ Blobs at $z=2.2-2.3$

We report the large scale structure and clustering analysis of Ly$α$ emitters (LAEs) and Ly$α$ blobs (LABs) at $z=2.2-2.3$. Using 3,341 LAEs, 117 LABs, and 58 bright (Ly$α$ luminosity $L_{\rm Lyα}>10^{43.4}$ erg s$^{-1}$) LABs at $z=2.2-2.3$ selected with Subaru/Hyper Suprime-Cam, we calculate the LAE overdensity to investigate the large scale structure at $z=2$. We show that $79\%$ LABs and $83\%$ bright LABs locate in overdense regions, which is consistent with the trend found by previous studies that LABs generally locate in overdense regions. We find that one of our 8 fields dubbed J1349 contains $39/117\approx33\%$ of our LABs and $22/58\approx38\%$ of our bright LABs. A unique and overdense $24'\times12'$ ($40\times20$ comoving Mpc$^2$) region (J1347 protocluster) has 12 LABs (8 bright LABs). By comparing to SSA22 that is one of the most overdense LAB regions found by previous studies, we show that the J1347 protocluster region has a higher bright LAB density than the SSA22 protocluster region with a $1σ$ significance. We calculate the angular correlation functions (ACFs) of LAEs and LABs in the unique J1349 field and fit the ACFs with a power-law function to measure the slopes. The bright LABs show a $5σ$ larger slope suggesting that bright LABs are more clustered than faint LAEs. Our LABs have a large galaxy bias of $\sim 5-7$, which suggests that LABs generally reside in more massive dark matter halos (halo masses $M \gtrsim 10^{13}$ M$_{\odot}$) than faint LAEs.

astro-ph.GA↗

arXiv2024

MStableChain: Towards Multi-Native Stablecoins in EVM-Compatible Blockchain for Stable Fee and Mass Adoption

Traditional blockchain systems, such as Ethereum, typically rely on a \emph{single volatile cryptocurrency for transaction fees}. This leads to fluctuating transaction fee prices and limits the flexibility of users' payment options. To address these issues, we propose MStableChain, which leverage multiple stablecoins as native tokens for transaction fee settlements, thus ensuring stable transaction fees and flexible payment options. To address the challenges of mass adoption and practicality, we propose several core designs. To maintain compatibility with the Ethereum Virtual Machine (EVM) for mass adoption while supporting multiple native stablecoins, MStableChain employs a multi-currency units, multi-type RPCs mechanism. This mechanism enables the system to handle multiple stablecoins without altering the EVM or requiring changes to user applications. Furthermore, an oracle-based gas fee adjustment mechanism is proposed to manage exchange rates between different stablecoins, ensuring equitable transaction costs across various currencies. The system also introduces a secure, on-chain voting-based management protocol for the administrative functions related to these stablecoins. Experimental results from a prototype implementation demonstrate that MStableChain provides stable transaction fee prices, high effectiveness, and good usability.

cs.CE↗

arXiv2024

MAMMOTH-Subaru. II. Diverse Populations of Circumgalactic Ly$α$ Nebulae at Cosmic Noon

Circumgalactic Lyman-alpha (Ly$α$) nebulae are gaseous halos around galaxies exhibiting luminous extended Ly$α$ emission. This work investigates Ly$α$ nebulae from deep imaging of $\sim12~\mathrm{deg}^2$ sky, targeted by the MAMMOTH-Subaru survey. Utilizing the wide-field capability of Hyper Suprime-Cam (HSC), we present one of the largest blind Ly$α$ nebula selections, including QSO nebulae, Ly$α$ blobs, and radio galaxy nebulae down to typical $2σ$ Ly$α$ surface brightness of $(5-10)\times10^{-18}\mathrm{~erg~s^{-1}~cm^{-2}~arcsec^{-2}}$. The sample contains 117 nebulae with Ly$α$ sizes of 40 - 400 kpc, and the most gigantic one spans about 365 kpc, referred to as the Ivory Nebula. Combining multiwavelength data, we investigate diverse nebula populations and associated galaxies. We find a small fraction of Ly$α$ nebulae have QSOs ($\sim7\%$), luminous infrared galaxies ($\sim1\%$), and radio galaxies ($\sim 2\%$). Remarkably, among the 28 enormous Ly$α$ nebulae (ELANe) exceeding 100 kpc, about 80\% are associated with UV-faint galaxies ($M_\mathrm{UV} > -22$), categorized as Type II ELANe. We underscore that Type II ELANe constitute the majority but remain largely hidden in current galaxy and QSO surveys. Dusty starburst and obscured AGN activity are proposed to explain the nature of Type II ELANe. The SED of stacking all Ly$α$ nebulae also reveals signs of massive dusty star-forming galaxies with obscured AGNs. We propose a model to explain the dusty nature where the diverse populations of Ly$α$ nebulae capture massive galaxies at different evolutionary stages undergoing violent assembling. Ly$α$ nebulae provide critical insights into the formation and evolution of today's massive cluster galaxies at cosmic noon.

astro-ph.GA↗

arXiv2024

Edge Computing for IoT: Novel Insights from a Comparative Analysis of Access Control Models

IoT edge computing positions computing resources closer to the data sources to reduce the latency, relieve the bandwidth pressure on the cloud, and enhance data security. Nevertheless, data security in IoT edge computing still faces critical threats (e.g., data breaches). Access control is fundamental for mitigating these threats. However, IoT edge computing introduces notable challenges for achieving resource-conserving, low-latency, flexible, and scalable access control. To review recent access control measures, we novelly organize them according to different data lifecycles--data collection, storage, and usage--and, meanwhile, review blockchain technology in this novel organization. In this way, we provide novel insights and envisage several potential research directions. This survey can help readers find gaps systematically and prompt the development of access control techniques in IoT edge computing under the intricacy of innovations in access control.

eess.SY↗

arXiv2024

Neural Implicit Surface Reconstruction of Freehand 3D Ultrasound Volume with Geometric Constraints

Three-dimensional (3D) freehand ultrasound (US) is a widely used imaging modality that allows non-invasive imaging of medical anatomy without radiation exposure. Surface reconstruction of US volume is vital to acquire the accurate anatomical structures needed for modeling, registration, and visualization. However, traditional methods cannot produce a high-quality surface due to image noise. Despite improvements in smoothness, continuity, and resolution from deep learning approaches, research on surface reconstruction in freehand 3D US is still limited. This study introduces FUNSR, a self-supervised neural implicit surface reconstruction method to learn signed distance functions (SDFs) from US volumes. In particular, FUNSR iteratively learns the SDFs by moving the 3D queries sampled around volumetric point clouds to approximate the surface, guided by two novel geometric constraints: sign consistency constraint and onsurface constraint with adversarial learning. Our approach has been thoroughly evaluated across four datasets to demonstrate its adaptability to various anatomical structures, including a hip phantom dataset, two vascular datasets and one publicly available prostate dataset. We also show that smooth and continuous representations greatly enhance the visual appearance of US data. Furthermore, we highlight the potential of our method to improve segmentation performance, and its robustness to noise distribution and motion perturbation.

eess.IV↗

arXiv2024

Unveiling Luminous Ly$α$ Emitters at $z\approx6$ through JWST/NIRCam Imaging in the COSMOS Field

We study a sample of 14 spectroscopically confirmed Ly$α$ Emitters (LAEs) in the late era of reionization (at redshift $z\approx6$) based on the JWST/NIRCam imaging dataset. These LAEs with high Ly$α$ luminosity of $L$(Ly$α$) $\sim10^{42.4-43.4}$ erg s$^{-1}$ have been covered by the (ongoing) COSMOS-Web survey (Kartaltepe et al. 2021; Casey et al. 2022) over $0.28$ deg$^2$ in four NIRCam bands (F115W, F150W, F277W, and F444W). With JWST imaging, we determine the UV continua with $M_{\rm UV}$ ranging from ${-}20.5$ to ${-}18.5$ mag. The UV slopes have a median value of $β\approx-2.35$, and the steepest slope can reach $β<-3$. Under an excellent spatial resolution of JWST, we identify three out of the sample as potential merging/interacting systems. The 14 LAEs (and their components) are compact in morphology residing substantially below the mass-size relation of high-$z$ galaxies. We further investigate the stellar mass ($M_*$) and star-formation rates (SFRs). Most of the LAEs lie on the SFR-$M_*$ main-sequence relation while two of them featured as "little red dots" likely host active galactic nuclei (AGN), implying a ${\sim}10\%$ AGN fraction. Moreover, we reveal that a new correlation may exist between Ly$α$ equivalent width and the offset between Ly$α$ and UV emission ($Δd_{\rm Lyα}$) with a median $Δd_{\rm Lyα} \sim 1$ kpc. This could be explained by Ly$α$ radiative transfer process in both ISM and CGM. The results usher a new era of detailed analysis on high-$z$ LAEs with the JWST capability.

astro-ph.GA↗

arXiv2024

Training-free image style alignment for self-adapting domain shift on handheld ultrasound devices

Handheld ultrasound devices face usage limitations due to user inexperience and cannot benefit from supervised deep learning without extensive expert annotations. Moreover, the models trained on standard ultrasound device data are constrained by training data distribution and perform poorly when directly applied to handheld device data. In this study, we propose the Training-free Image Style Alignment (TISA) framework to align the style of handheld device data to those of standard devices. The proposed TISA can directly infer handheld device images without extra training and is suited for clinical applications. We show that TISA performs better and more stably in medical detection and segmentation tasks for handheld device data. We further validate TISA as the clinical model for automatic measurements of spinal curvature and carotid intima-media thickness. The automatic measurements agree well with manual measurements made by human experts and the measurement errors remain within clinically acceptable ranges. We demonstrate the potential for TISA to facilitate automatic diagnosis on handheld ultrasound devices and expedite their eventual widespread use.

eess.IV↗

arXiv2023

Law of the logarithm for the maximum interpoint distance constructed by high-dimensional random matrix

Suppose $\left \{ X_{i,k}; 1\le i \le p, 1\le k \le n \right \} $ is an array of i.i.d.~real random variables. Let $\left \{ p=p_{n}; n \ge1 \right \} $ be positive integers. Consider the maximum interpoint distance $M_{n}=\max_{1\le i< j\le p} \left \| \boldsymbol{X}_{i}- \boldsymbol{X}_{j} \right \|_{2} $ where $\boldsymbol{X}_{i}$ and $\boldsymbol{X}_{j}$ denote the $i$-th and $j$-th rows of the $p \times n$ matrix $\mathcal{M} _{p,n}=\left( X_{i,k} \right)_{p \times n}$, respectively. This paper shows the laws of the logarithm for $M_{n}$ under two high-dimensional settings: the polynomial rate and the exponential rate. The proofs rely on the moderation deviation principle of the partial sum of i.i.d.~random variables, the Chen--Stein Poisson approximation method and Gaussian approximation.

math.PR↗

arXiv2023

Automatic Diagnosis of Carotid Atherosclerosis Using a Portable Freehand 3D Ultrasound Imaging System

The objective of this study is to develop a deep-learning based detection and diagnosis technique for carotid atherosclerosis using a portable freehand 3D ultrasound (US) imaging system. A total of 127 3D carotid artery scans were acquired using a portable 3D US system which consisted of a handheld US scanner and an electromagnetic tracking system. A U-Net segmentation network was firstly applied to extract the carotid artery on 2D transverse frame, then a novel 3D reconstruction algorithm using fast dot projection (FDP) method with position regularization was proposed to reconstruct the carotid artery volume. Furthermore, a convolutional neural network was used to classify healthy and diseased cases qualitatively. 3D volume analysis methods including longitudinal image acquisition and stenosis grade measurement were developed to obtain the clinical metrics quantitatively. The proposed system achieved sensitivity of 0.714, specificity of 0.851 and accuracy of 0.803 respectively for diagnosis of carotid atherosclerosis. The automatically measured stenosis grade illustrated good correlation (r=0.762) with the experienced expert measurement. The developed technique based on 3D US imaging can be applied to the automatic diagnosis of carotid atherosclerosis. The proposed deep-learning based technique was specially designed for a portable 3D freehand US system, which can provide more convenient carotid atherosclerosis examination and decrease the dependence on clinician's experience.

eess.IV↗

arXiv2023

MAMMOTH-Subaru III. Ly$α$ Halo Extended to $\sim200$ kpc Identified by Stacking $\sim 3300$ Ly$α$ Emitters at $z=2.2-2.3$

In this paper, we present a Ly$α$ halo extended to $\sim200$ kpc identified by stacking $\sim 3300$ Ly$α$ emitters at $z=2.2-2.3$. We carry out imaging observations and data reduction with Subaru/Hyper Suprime-Cam (HSC). Our total survey area is $\sim12$ deg$^2$ and imaging depths are $25.5-27.0$ mag. Using the imaging data, we select 1,240 and 2,101 LAE candidates at $z=2.2$ and 2.3, respectively. We carry out spectroscopic observations of our LAE candidates and data reduction with Magellan/IMACS to estimate the contamination rate of our LAE candidates. We find that the contamination rate of our sample is low (8%). We stack our LAE candidates with a median stacking method to identify the Ly$α$ halo at $z=2$. We show that the Ly$α$ halo is extended to $\sim200$ kpc at a surface brightness level of $10^{-20}$ erg s$^{-1}$ cm$^{-2}$ arcsec$^{-2}$. Comparing to previous studies, our Ly$α$ halo is more extended at radii of $\sim25-100$ kpc, which is not likely caused by the contamination in our sample but by different redshifts and fields instead. To investigate how central galaxies affect surrounding LAHs, we divide our LAEs into subsamples based on the Ly$α$ luminosity ($L_{\rm Lyα}$), rest-frame Ly$α$ equivalent width (EW$_0$), and UV magnitude (M$_{\rm uv}$). We stack the subsamples and find that higher $L_{\rm Lyα}$, lower EW$_0$, and brighter M$_{\rm uv}$ cause more extended halos. Our results suggest that more massive LAEs generally have more extended Ly$α$ halos.

astro-ph.GA↗

arXiv2023

MAMMOTH-Subaru V. Effects of Cosmic Variance on Ly$α$ Luminosity Functions at $z=2.2-2.3$

Cosmic variance introduces significant uncertainties into galaxy number density properties when surveying the high-z Universe with a small volume, such uncertainties produce the field-to-field variance of galaxy number $σ_{g}$ in observational astronomy. This uncertainty significantly affects the Luminosity Functions (LF) measurement of Lya Emitters (LAEs). For most previous Lya LF studies, $σ_{g}$ is often adopted from predictions by cosmological simulations, but barely confirmed by observations. Measuring cosmic variance requires a huge sample over a large volume, exceeding the capabilities of most astronomical instruments. In this study, we demonstrate an observational approach for measuring the cosmic variance contribution for $z\approx2.2$ Lya LFs. The LAE candidates are observed using narrowband and broadband of the Subaru/Hyper Suprime-Cam (HSC), with 8 independent fields, making the total survey area $\simeq11.62$deg$^2$ and a comoving volume of $\simeq8.71\times10^6$Mpc$^3$. These eight fields are selected using the project of MAMMOTH. We report a best-fit Schechter function with parameters $α=-1.75$ (fixed), $L_{Lyα}^{*}=5.18_{-0.40}^{+0.43} \times 10^{42}$erg s$^{-1}$ and $ϕ_{Lya}^{*}=4.87_{-0.55}^{+0.54}\times10^{-4}$Mpc$^{-3}$ for the overall Lya LFs. After clipping out the regions that can bias the cosmic variance measurements, we calculate $σ_{g}$, by sampling LAEs within multiple pointings assigned on the field image. We investigate the relation between $σ_{g}$ and survey volume $V$, and fit a simple power law: $σ_g=k\times(\frac{V_{\rm eff}}{10^5 {\rm Mpc}^3})^β$. We find best-fit values of $-1.209_{-0.106}^{+0.106}$ for $β$ and $0.986_{-0.100}^{+0.108}$ for k. We compare our measurements with predictions from simulations and find that the cosmic variance of LAEs might be larger than that of general star-forming galaxies.

astro-ph.GA↗

arXiv2022

Convergence Analysis of Generalized ADMM with Majorization for Linearly Constrained Composite Convex Optimization

The generalized alternating direction method of multipliers (ADMM) of Xiao et al. [{\tt Math. Prog. Comput., 2018}] aims at the two-block linearly constrained composite convex programming problem, in which each block is in the form of "nonsmooth + quadratic". However, in the case of non-quadratic (but smooth), this method may fail unless the favorable structure of 'nonsmooth + smooth' is no longer used. This paper aims to remedy this defect by using a majorized technique to approximate the augmented Lagrangian function, so that the corresponding subprobllem can be decomposed into some smaller problems and then solved separately. Furthermore, the recent symmetric Gauss-Seidel (sGS) decomposition theorem guarantees the equivalence between the bigger subproblem and these smaller ones. This paper focuses on convergence analysis, that is, we prove that the sequence generated by the proposed method converges globally to a Karush-Kuhn-Tucker point of the considered problem. Finally, we do some numerical experiments on a kind of simulated convex composite optimization problems which illustrate that the proposed method is more efficient than its compared ones.

math.OC↗

arXiv2022

Realization of fast all-microwave CZ gates with a tunable coupler

The development of high-fidelity two-qubit quantum gates is essential for digital quantum computing. Here, we propose and realize an all-microwave parametric Controlled-Z (CZ) gates by coupling strength modulation in a superconducting Transmon qubit system with tunable couplers. After optimizing the design of the tunable coupler together with the control pulse numerically, we experimentally realized a 100 ns CZ gate with high fidelity of 99.38%$ \pm$0.34% and the control error being 0.1%. We note that our CZ gates are not affected by pulse distortion and do not need pulse correction, {providing a solution for the real-time pulse generation in a dynamic quantum feedback circuit}. With the expectation of utilizing our all-microwave control scheme to reduce the number of control lines through frequency multiplexing in the future, our scheme draws a blueprint for the high-integrable quantum hardware design.

quant-ph↗