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Gang Cao

Publications and source records attributed to Gang Cao.

At least 73 records · Page 4Linked to original sources
arXiv2023

Single spin qubit geometric gate in a silicon quantum dot

Preserving qubit coherence and maintaining high-fidelity qubit control under complex noise environment is an enduring challenge for scalable quantum computing. Here we demonstrate an addressable fault-tolerant single spin qubit with an average control fidelity of 99.12% via randomized benchmarking on a silicon quantum dot device with an integrated micromagnet. Its dephasing time T2* is 1.025 us and can be enlarged to 264 us by using the Hahn echo technique, reflecting strong low-frequency noise in our system. To break through the noise limitation, we introduce geometric quantum computing to obtain high control fidelity by exploiting its noise-resilient feature. However, the control fidelities of the geometric quantum gates are lower than 99%. According to our simulation, the noise-resilient feature of geometric quantum gates is masked by the heating effect. With further optimization to alleviate the heating effect, geometric quantum computing can be a potential approach to reproducibly achieving high-fidelity qubit control in a complex noise environment.

cond-mat.mes-hall↗
arXiv2023

Transferable Adversarial Attack on Image Tampering Localization

It is significant to evaluate the security of existing digital image tampering localization algorithms in real-world applications. In this paper, we propose an adversarial attack scheme to reveal the reliability of such tampering localizers, which would be fooled and fail to predict altered regions correctly. Specifically, the adversarial examples based on optimization and gradient are implemented for white/black-box attacks. Correspondingly, the adversarial example is optimized via reverse gradient propagation, and the perturbation is added adaptively in the direction of gradient rising. The black-box attack is achieved by relying on the transferability of such adversarial examples to different localizers. Extensive evaluations verify that the proposed attack sharply reduces the localization accuracy while preserving high visual quality of the attacked images.

cs.CV↗
arXiv2023

Spatio-temporal Co-attention Fusion Network for Video Splicing Localization

Digital video splicing has become easy and ubiquitous. Malicious users copy some regions of a video and paste them to another video for creating realistic forgeries. It is significant to blindly detect such forgery regions in videos. In this paper, a spatio-temporal co-attention fusion network (SCFNet) is proposed for video splicing localization. Specifically, a three-stream network is used as an encoder to capture manipulation traces across multiple frames. The deep interaction and fusion of spatio-temporal forensic features are achieved by the novel parallel and cross co-attention fusion modules. A lightweight multilayer perceptron (MLP) decoder is adopted to yield a pixel-level tampering localization map. A new large-scale video splicing dataset is created for training the SCFNet. Extensive tests on benchmark datasets show that the localization and generalization performances of our SCFNet outperform the state-of-the-art. Code and datasets will be available at https://github.com/multimediaFor/SCFNet.

cs.CV↗
arXiv2023

Correcting on-chip distortion of control pulses with silicon spin qubits

Pulse distortion, as one of the coherent error sources, hinders the characterization and control of qubits. In the semiconductor quantum dot system, the distortions on measurement pulses and control pulses disturb the experimental results, while no effective calibration procedure has yet been reported. Here, we demonstrate two different calibration methods to calibrate and correct the distortion using the two-qubit system as a detector. The two calibration methods have different correction accuracy and complexity. One is the coarse predistortion (CPD) method, with which the distortion is partly relieved. The other method is the all predistortion (APD) method, with which we measure the transfer function and significantly improve the exchange oscillation homogeneity. The two methods use the exchange oscillation homogeneity as the metric and are appropriate for any qubit that oscillates with a diabatic pulse. With the APD procedure, an arbitrary control waveform can be accurately delivered to the device, which is essential for characterizing qubits and improving gate fidelity.

cond-mat.mes-hall↗
arXiv2023

Singlet-triplet-state readout in silicon-metal-oxide-semiconductor double quantum dots

High-fidelity singlet-triplet state readout is essential for large-scale quantum computing. However, the widely used threshold method of comparing a mean value with the fixed threshold will limit the judgment accuracy, especially for the relaxed triplet state, under the restriction of relaxation time and signal-to-noise ratio. Here, we achieve an enhanced latching readout based on Pauli spin blockade in a Si-MOS double quantum dot device and demonstrate an average singlet-triplet state readout fidelity of 97.59% by the threshold method. We reveal the inherent deficiency of the threshold method for the relaxed triplet state classification and introduce machine learning as a relaxation-independent readout method to reduce the misjudgment. The readout fidelity for classifying the simulated single-shot traces can be improved to 99.67% by machine learning method, better than the threshold method of 97.54% which is consistent with the experimental result. This work indicates that machine learning method can be a strong potential candidate for alleviating the restrictions of stably achieving high-fidelity and high-accuracy singlet-triplet state readout in large-scale quantum computing.

cond-mat.mes-hall↗
arXiv2023

Effective Image Tampering Localization via Enhanced Transformer and Co-attention Fusion

Powerful manipulation techniques have made digital image forgeries be easily created and widespread without leaving visual anomalies. The blind localization of tampered regions becomes quite significant for image forensics. In this paper, we propose an effective image tampering localization network (EITLNet) based on a two-branch enhanced transformer encoder with attention-based feature fusion. Specifically, a feature enhancement module is designed to enhance the feature representation ability of the transformer encoder. The features extracted from RGB and noise streams are fused effectively by the coordinate attention-based fusion module at multiple scales. Extensive experimental results verify that the proposed scheme achieves the state-of-the-art generalization ability and robustness in various benchmark datasets. Code will be public at https://github.com/multimediaFor/EITLNet.

cs.CV↗
arXiv2023

Anisotropy of Antiferromagnetic Domains in a Spin-orbit Mott Insulator

The temperature-dependent behavior of magnetic domains plays an essential role in the magnetic properties of materials, leading to widespread applications. However, experimental methods to access the three-dimensional (3D) magnetic domain structures are very limited, especially for antiferromagnets. Over the past decades, the spin-orbit Mott insulator iridate $Sr_2IrO_4$ has attracted particular attention because of its interesting magnetic structure and analogy to superconducting cuprates. Here, we apply resonant x-ray magnetic Bragg coherent diffraction imaging to track the real-space 3D evolution of antiferromagnetic ordering inside a $Sr_2IrO_4$ single crystal as a function of temperature, finding that the antiferromagnetic domain shows anisotropic changes. The anisotropy of the domain shape reveals the underlying anisotropy of the antiferromagnetic coupling strength within $Sr_2IrO_4$. These results demonstrate the high potential significance of 3D domain imaging in magnetism research.

cond-mat.str-el↗
arXiv2023

Current-sensitive Hall effect in a chiral-orbital-current state

Chiral orbital currents (COC) underpin a novel colossal magnetoresistance (CMR) in ferrimagnetic Mn3Si2Te6 [1]. Here we report the Hall effect in the COC state which exhibits the following unprecedented features: (1) A sharp, current-sensitive peak in the magnetic field dependence of the Hall resistivity; (2) An unusually large Hall angle reaching up to 0.15 (comparable to the highest values yet reported); and (3) A current-sensitive scaling relation between the Hall conductivity sigma_xy and the longitudinal conductivity sigma_xx, namely, sigma_xy ~ sigma_xx^alpha with alpha ranging between 3 and 5, which is both sensitive to external current and exceptionally large compared to alpha < 2 typical of most solids. These anomalies point to a giant, current-sensitive Hall effect that is unique to the COC state. We argue that a magnetic field induced by the fully developed COC combines with the applied magnetic field to exert the greatly enhanced transverse force on charge carriers, which dictates the novel Hall responses. The COC Hall effect is unique, as it is generated and controlled via the interaction between intrinsic COC and applied external currents, which leads to novel transport phenomena of fundamental and technological significance and requires new physics for explanation.

cond-mat.str-el↗
arXiv2023

Structural changes induced by electric currents in a single crystal of Pr$_2$CuO$_4$

We demonstrate a novel approach to the structural and electronic property modification of perovskites, focusing on Pr$_2$CuO$_4$, an undoped parent compound of a class of electron-doped copper-oxide superconductors. Currents were passed parallel or perpendicular to the copper-oxygen layers with the voltage ramped up until a rapid drop in the resistivity was achieved, a process referred to as "flash". The current was then further increased tenfold in current-control mode. This state was quenched by immersion into liquid nitrogen. Flash can drive many compounds into different atomic structures with new properties, whereas the quench freezes them into a long-lived state. Single-crystal neutron diffraction of as-grown and modified Pr$_2$CuO$_4$ revealed a $\sqrt{10}$x$\sqrt{10}$ superlattice due to oxygen-vacancy order. The diffraction peak intensities of the superlattice of the modified sample were significantly enhanced relative to the pristine sample. Raman-active phonons in the modified sample were considerably sharper. Measurements of electrical resistivity, magnetization and two-magnon Raman scattering indicate that the modification affected only the Pr-O layers, but not the Cu-O planes. These results point to enhanced oxygen-vacancy order in the modified samples well beyond what can be achieved without passing electrical current. Our work opens a new avenue toward electric field/quench control of structure and properties of layered perovskite oxides.

cond-mat.supr-con↗
arXiv2023

Threshold-independent method for single-shot readout of spin qubits in semiconductor quantum dots

The single-shot readout data process is essential for the realization of high-fidelity qubits and fault-tolerant quantum algorithms in semiconductor quantum dots. However, the fidelity and visibility of the readout process is sensitive to the choice of the thresholds and limited by the experimental hardware. By demonstrating the linear dependence between the measured spin state probabilities and readout visibilities along with dark counts, we describe an alternative threshold-independent method for the single-shot readout of spin qubits in semiconductor quantum dots. We can obtain the extrapolated spin state probabilities of the prepared probabilities of the excited spin state through the threshold-independent method. Then, we analyze the corresponding errors of the method, finding that errors of the extrapolated probabilities cannot be neglected with no constraints on the readout time and threshold voltage. Therefore, by limiting the readout time and threshold voltage we ensure the accuracy of the extrapolated probability. Then, we prove that the efficiency and robustness of this method is 60 times larger than that of the most commonly used method. Moreover, we discuss the influence of the electron temperature on the effective area with a fixed external magnetic field and provide a preliminary demonstration for a single-shot readout up to 0.7 K/1.5T in the future.

cond-mat.mes-hall↗
arXiv2023

Evidence for bootstrap percolation dynamics in a photo-induced phase transition

Upon intense femtosecond photo-excitation, a many-body system can undergo a phase transition through a non-equilibrium route, but understanding these pathways remains an outstanding challenge. Here, we use time-resolved second harmonic generation to investigate a photo-induced phase transition in Ca$_3$Ru$_2$O$_7$ and show that mesoscale inhomogeneity profoundly influences the transition dynamics. We observe a marked slowing down of the characteristic time, $τ$, that quantifies the transition between two structures. $τ$ evolves non-monotonically as a function of photo-excitation fluence, rising from below 200~fs to $\sim$1.4~ps, then falling again to below 200~fs. To account for the observed behavior, we perform a bootstrap percolation simulation that demonstrates how local structural interactions govern the transition kinetics. Our work highlights the importance of percolating mesoscale inhomogeneity in the dynamics of photo-induced phase transitions and provides a model that may be useful for understanding such transitions more broadly.

cond-mat.str-el↗
arXiv2023

Ultrafast and Electrically Tunable Rabi Frequency in a Germanium Hut Wire Hole Spin Qubit

Hole spin qubits based on germanium (Ge) have strong tunable spin orbit interaction (SOI) and ultrafast qubit operation speed. Here we report that the Rabi frequency (f_Rabi) of a hole spin qubit in a Ge hut wire (HW) double quantum dot (DQD) is electrically tuned through the detuning energy and middle gate voltage (V_M). f_Rabi gradually decreases with increasing detuning energy; on the contrary, f_Rabi is positively correlated with V_M. We attribute our results to the change of electric field on SOI and the contribution of the excited state in quantum dots to f_Rabi. We further demonstrate an ultrafast f_Rabi exceeding 1.2 GHz, which evidences the strong SOI in our device. The discovery of an ultrafast and electrically tunable f_Rabi in a hole spin qubit has potential applications in semiconductor quantum computing.

cond-mat.mes-hall↗
arXiv2023

FlexMoE: Scaling Large-scale Sparse Pre-trained Model Training via Dynamic Device Placement

With the increasing data volume, there is a trend of using large-scale pre-trained models to store the knowledge into an enormous number of model parameters. The training of these models is composed of lots of dense algebras, requiring a huge amount of hardware resources. Recently, sparsely-gated Mixture-of-Experts (MoEs) are becoming more popular and have demonstrated impressive pretraining scalability in various downstream tasks. However, such a sparse conditional computation may not be effective as expected in practical systems due to the routing imbalance and fluctuation problems. Generally, MoEs are becoming a new data analytics paradigm in the data life cycle and suffering from unique challenges at scales, complexities, and granularities never before possible. In this paper, we propose a novel DNN training framework, FlexMoE, which systematically and transparently address the inefficiency caused by dynamic dataflow. We first present an empirical analysis on the problems and opportunities of training MoE models, which motivates us to overcome the routing imbalance and fluctuation problems by a dynamic expert management and device placement mechanism. Then we introduce a novel scheduling module over the existing DNN runtime to monitor the data flow, make the scheduling plans, and dynamically adjust the model-to-hardware mapping guided by the real-time data traffic. A simple but efficient heuristic algorithm is exploited to dynamically optimize the device placement during training. We have conducted experiments on both NLP models (e.g., BERT and GPT) and vision models (e.g., Swin). And results show FlexMoE can achieve superior performance compared with existing systems on real-world workloads -- FlexMoE outperforms DeepSpeed by 1.70x on average and up to 2.10x, and outperforms FasterMoE by 1.30x on average and up to 1.45x.

cs.DC↗
arXiv2023

Flopping-mode spin qubit in a Si-MOS quantum dot

Spin qubits based on silicon metal-oxide semiconductor (Si-MOS) quantum dots (QDs) are promising platforms for large-scale quantum computers. To control spin qubits in QDs, electric dipole spin resonance (EDSR) has been most commonly used in recent years. By delocalizing an electron across a double quantum dots charge state, flopping-mode EDSR has been realized in Si/SiGe QDs. Here, we demonstrate a flopping-mode spin qubit in a Si-MOS QD via Elzerman single-shot readout. When changing the detuning with a fixed drive power, we achieve s-shape spin resonance frequencies, an order of magnitude improvement in the spin Rabi frequencies, and virtually constant spin dephasing times. Our results offer a route to large-scale spin qubit systems with higher control fidelity in Si-MOS QDs.

cond-mat.mes-hall↗
arXiv2023

Full tunability and quantum coherent dynamics of a driven multilevel system

Tunability of an artificial quantum system is crucial to its capability to process quantum information. However, tunability usually poses significant demand on the design and fabrication of a device. In this work, we demonstrate that Floquet engineering based on longitudinal driving provides distinct possibilities in enhancing the tunability of a quantum system without needing additional resources. In particular, we study a multilevel model based on gate-defined double quantum dots, where coherent interference occurs when the system is driven longitudinally. We develop an effective model to describe the driven dynamics of this multilevel system, and show that it is highly tunable via the driving field. We then illustrate the versatility and rich physics of a driven multilevel system by exploring phenomena such as driving modulation of resonances, adiabatic state transfer, and dark state. In the context of qubit control, we propose noise-resistant quantum gates based on adiabatic passage. The theoretical consideration we present here is rather general, and is in principle valid for other multilevel quantum systems.

cond-mat.mes-hall↗
arXiv2023

Effective Image Tampering Localization with Multi-Scale ConvNeXt Feature Fusion

With the widespread use of powerful image editing tools, image tampering becomes easy and realistic. Existing image forensic methods still face challenges of low generalization performance and robustness. In this letter, we propose an effective image tampering localization scheme based on ConvNeXt network and multi-scale feature fusion. Stacked ConvNeXt blocks are used as an encoder to capture hierarchical multi-scale features, which are then fused in decoder for locating tampered pixels accurately. Combined loss and effective data augmentation are adopted to further improve the model performance. Extensive experimental results show that localization performance of our proposed scheme outperforms other state-of-the-art ones. The source code will be available at https://github.com/ZhuHC98/ITL-SSN.

cs.CV↗
arXiv2022

Probing two driven double quantum dots strongly coupled to a cavity

We experimentally and theoretically study a driven hybrid circuit quantum electrodynamics (cQED) system beyond the dispersive coupling regime. Treating the cavity as part of the driven system, we develop a theory applicable to such strongly coupled and to multi-qubit systems. The fringes measured for a single driven double quantum dot (DQD)-cavity setting and the enlarged splittings of the hybrid Floquet states in the presence of a second DQD are well reproduced with our model. This opens a path to study Floquet states of multi-qubit systems with arbitrarily strong coupling and reveals a new perspective for understanding strongly driven hybrid systems.

cond-mat.mes-hall↗
arXiv2022

Black-Box Attack against GAN-Generated Image Detector with Contrastive Perturbation

Visually realistic GAN-generated facial images raise obvious concerns on potential misuse. Many effective forensic algorithms have been developed to detect such synthetic images in recent years. It is significant to assess the vulnerability of such forensic detectors against adversarial attacks. In this paper, we propose a new black-box attack method against GAN-generated image detectors. A novel contrastive learning strategy is adopted to train the encoder-decoder network based anti-forensic model under a contrastive loss function. GAN images and their simulated real counterparts are constructed as positive and negative samples, respectively. Leveraging on the trained attack model, imperceptible contrastive perturbation could be applied to input synthetic images for removing GAN fingerprint to some extent. As such, existing GAN-generated image detectors are expected to be deceived. Extensive experimental results verify that the proposed attack effectively reduces the accuracy of three state-of-the-art detectors on six popular GANs. High visual quality of the attacked images is also achieved. The source code will be available at https://github.com/ZXMMD/BAttGAND.

cs.CV↗