arXiv subjects

Zhenxing Zhang

Publications and source records attributed to Zhenxing Zhang.

At least 37 records · Page 2Linked to original sources

arXiv2025

Occamy: A Preemptive Buffer Management for On-chip Shared-memory Switches

Today's high-speed switches employ an on-chip shared packet buffer. The buffer is becoming increasingly insufficient as it cannot scale with the growing switching capacity. Nonetheless, the buffer needs to face highly intense bursts and meet stringent performance requirements for datacenter applications. This imposes rigorous demand on the Buffer Management (BM) scheme, which dynamically allocates the buffer across queues. However, the de facto BM scheme, designed over two decades ago, is ill-suited to meet the requirements of today's network. In this paper, we argue that shallow-buffer switches, intense bursts, along with dynamic traffic call for a highly agile BM that can quickly adjust the buffer allocation as traffic changes. However, the agility of the current BM is fundamentally limited by its non-preemptive nature. Nonetheless, we find that preemptive BM, considered unrealizable in history, is now feasible on modern switch chips. We propose Occamy, a preemptive BM that can quickly adjust buffer allocation. Occamy utilizes the redundant memory bandwidth to actively reclaim and reallocate the over-allocated buffer. Testbed experiments and large-scale simulations show that Occamy can improve the end-to-end performance by up to ~55%.

cs.NI↗

arXiv2024

A Hybrid Quantum Computing Pipeline for Real World Drug Discovery

Quantum computing, with its superior computational capabilities compared to classical approaches, holds the potential to revolutionize numerous scientific domains, including pharmaceuticals. However, the application of quantum computing for drug discovery has primarily been limited to proof-of-concept studies, which often fail to capture the intricacies of real-world drug development challenges. In this study, we diverge from conventional investigations by developing \rev{a hybrid} quantum computing pipeline tailored to address genuine drug design problems. Our approach underscores the application of quantum computation in drug discovery and propels it towards more scalable system. We specifically construct our versatile quantum computing pipeline to address two critical tasks in drug discovery: the precise determination of Gibbs free energy profiles for prodrug activation involving covalent bond cleavage, and the accurate simulation of covalent bond interactions. This work serves as a pioneering effort in benchmarking quantum computing against veritable scenarios encountered in drug design, especially the covalent bonding issue present in both of the case studies, thereby transitioning from theoretical models to tangible applications. Our results demonstrate the potential of a quantum computing pipeline for integration into real world drug design workflows.

physics.chem-ph↗

arXiv2024

Feature Selection Based on Orthogonal Constraints and Polygon Area

The goal of feature selection is to choose the optimal subset of features for a recognition task by evaluating the importance of each feature, thereby achieving effective dimensionality reduction. Currently, proposed feature selection methods often overlook the discriminative dependencies between features and labels. To address this problem, this paper introduces a novel orthogonal regression model incorporating the area of a polygon. The model can intuitively capture the discriminative dependencies between features and labels. Additionally, this paper employs a hybrid non-monotone linear search method to efficiently tackle the non-convex optimization challenge posed by orthogonal constraints. Experimental results demonstrate that our approach not only effectively captures discriminative dependency information but also surpasses traditional methods in reducing feature dimensions and enhancing classification performance.

cs.LG↗

arXiv2024

Tantalum airbridges for scalable superconducting quantum processors

The unique property of tantalum (Ta), particularly its long coherent lifetime in superconducting qubits and its exceptional resistance to both acid and alkali, makes it promising for superconducting quantum processors. It is a notable advantage to achieve high-performance quantum processors with neat and unified fabrication of all circuit elements, including coplanar waveguides (CPW), qubits, and airbridges, on the tantalum film-based platform. Here, we propose a reliable tantalum airbridges with separate or fully-capped structure fabricated via a novel lift-off method, where a barrier layer with aluminium (Al) film is first introduced to separate two layers of photoresist and then etched away before the deposition of tantalum film, followed by cleaning with piranha solution to remove the residual photoresist on the chip. We characterize such tantalum airbridges as the control line jumpers, the ground plane crossovers and even coupling elements. They exhibit excellent connectivity, minimal capacitive loss, effectively suppress microwave and flux crosstalk and offer high freedom of coupling. Besides, by presenting a surface-13 tunable coupling superconducting quantum processor with median $T_1$ reaching above 100 $μ$s, the overall adaptability of tantalum airbridges is verified. The median single-qubit gate fidelity shows a tiny decrease from about 99.95% for the isolated Randomized Benchmarking to 99.94% for the simultaneous one. This fabrication method, compatible with all known superconducting materials, requires mild conditions of film deposition compared with the commonly used etching and grayscale lithography. Meanwhile, the experimental achievement of non-local coupling with controlled-Z (CZ) gate fidelity exceeding 99.2% may further facilitate qLDPC codes, laying a foundation for scalable quantum computation and quantum error correction with entirely tantalum elements.

quant-ph↗

arXiv2023

A Scalable, Fast and Programmable Neural Decoder for Fault-Tolerant Quantum Computation Using Surface Codes

Quantum error-correcting codes (QECCs) can eliminate the negative effects of quantum noise, the major obstacle to the execution of quantum algorithms. However, realizing practical quantum error correction (QEC) requires resolving many challenges to implement a high-performance real-time decoding system. Many decoding algorithms have been proposed and optimized in the past few decades, of which neural network (NNs) based solutions have drawn an increasing amount of attention due to their high efficiency. Unfortunately, previous works on neural decoders are still at an early stage and have only relatively simple architectures, which makes them unsuitable for practical QEC. In this work, we propose a scalable, fast, and programmable neural decoding system to meet the requirements of FTQEC for rotated surface codes (RSC). Firstly, we propose a hardware-efficient NN decoding algorithm with relatively low complexity and high accuracy. Secondly, we develop a customized hardware decoder with architectural optimizations to reduce latency. Thirdly, our proposed programmable architecture boosts the scalability and flexibility of the decoder by maximizing parallelism. Fourthly, we build an FPGA-based decoding system with integrated control hardware for evaluation. Our $L=5$ ($L$ is the code distance) decoder achieves an extremely low decoding latency of 197 ns, and the $L=7$ configuration also requires only 1.136 $μ$s, both taking $2L$ rounds of syndrome measurements. The accuracy results of our system are close to minimum weight perfect matching (MWPM). Furthermore, our programmable architecture reduces hardware resource consumption by up to $3.0\times$ with only a small latency loss. We validated our approach in real-world scenarios by conducting a proof-of-concept benchmark with practical noise models, including one derived from experimental data gathered from physical hardware.

quant-ph↗

arXiv2023

Fusion-S2iGan: An Efficient and Effective Single-Stage Framework for Speech-to-Image Generation

The goal of a speech-to-image transform is to produce a photo-realistic picture directly from a speech signal. Recently, various studies have focused on this task and have achieved promising performance. However, current speech-to-image approaches are based on a stacked modular framework that suffers from three vital issues: 1) Training separate networks is time-consuming as well as inefficient and the convergence of the final generative model strongly depends on the previous generators; 2) The quality of precursor images is ignored by this architecture; 3) Multiple discriminator networks are required to be trained. To this end, we propose an efficient and effective single-stage framework called Fusion-S2iGan to yield perceptually plausible and semantically consistent image samples on the basis of given spoken descriptions. Fusion-S2iGan introduces a visual+speech fusion module (VSFM), constructed with a pixel-attention module (PAM), a speech-modulation module (SMM) and a weighted-fusion module (WFM), to inject the speech embedding from a speech encoder into the generator while improving the quality of synthesized pictures. Fusion-S2iGan spreads the bimodal information over all layers of the generator network to reinforce the visual feature maps at various hierarchical levels in the architecture. We conduct a series of experiments on four benchmark data sets, i.e., CUB birds, Oxford-102, Flickr8k and Places-subset. The experimental results demonstrate the superiority of the presented Fusion-S2iGan compared to the state-of-the-art models with a multi-stage architecture and a performance level that is close to traditional text-to-image approaches.

cs.CV↗

arXiv2023

Determination of Molecular Energies via Quantum Imaginary Time Evolution in a Superconducting Qubit System

As a valid tool for solving ground state problems, imaginary time evolution (ITE) is widely used in physical and chemical simulations. Different ITE-based algorithms in their quantum counterpart have recently been proposed and applied to some real systems. We experimentally realize the variational-based quantum imaginary time evolution (QITE) algorithm to simulate the ground state energy of hydrogen (H2) and lithium hydride (LiH) molecules in a superconducting qubit system. The H2 molecule is directly simulated using the 3-qubit circuit with unitary-coupled clusters (UCC) ansatz. We also combine QITE with the cluster mean-field (CMF) method to obtain an effective Hamiltonian. The LiH molecule is correspondingly simulated using the 3-qubit circuit with hardware-efficient ansatz. For comparison, the LiH molecule is also directly simulated using the 4-qubit circuit with UCC ansatz at the equilibrium point. All the experimental results show a convergence within 4 iterations, with high-fidelity ground state energy obtained. For a more complex system in the future, the CMF may allow further grouping of interactions to obtain an effective Hamiltonian, then the hybrid QITE algorithm can possibly simulate a relatively large-scale system with fewer qubits.

quant-ph↗

arXiv2023

DCNetBench: Scaleable Data Center Network Benchmarking

Data center networking is the central infrastructure of the modern information society. However, benchmarking them is very challenging as the real-world network traffic is difficult to model, and Internet service giants treat the network traffic as confidential. Several industries have published a few publicly available network traces. However, these traces are collected from specific data center environments, e.g., applications, network topology, protocols, and hardware devices, and thus cannot be scaled to different users, underlying technologies, and varying benchmarking requirements. This article argues we should scale different data center applications and environments in designing, implementing, and evaluating data center networking benchmarking. We build DCNetBench, the first application-driven data center network benchmarking that can scale to different users, underlying technologies, and varying benchmarking requirements. The methodology is as follows. We built an emulated system that can simulate networking with different configurations. Then we run applications on the emulated systems to capture the realistic network traffic patterns; we analyze and classify these patterns to model and replay those traces. Finally, we provide an automatic benchmarking framework to support this pipeline. The evaluations on DCNetBench show its scaleability, effectiveness, and diversity for data center network benchmarking.

cs.NI↗

arXiv2022

Optimized latent-code selection for explainable conditional text-to-image GANs

The task of text-to-image generation has achieved remarkable progress due to the advances in the conditional generative adversarial networks (GANs). However, existing conditional text-to-image GANs approaches mostly concentrate on improving both image quality and semantic relevance but ignore the explainability of the model which plays a vital role in real-world applications. In this paper, we present a variety of techniques to take a deep look into the latent space and semantic space of the conditional text-to-image GANs model. We introduce pairwise linear interpolation of latent codes and `linguistic' linear interpolation to study what the model has learned within the latent space and `linguistic' embeddings. Subsequently, we extend linear interpolation to triangular interpolation conditioned on three corners to further analyze the model. After that, we build a Good/Bad data set containing unsuccessfully and successfully synthetic samples and corresponding latent codes for the image-quality research. Based on this data set, we propose a framework for finding good latent codes by utilizing a linear SVM. Experimental results on the recent DiverGAN generator trained on two benchmark data sets qualitatively prove the effectiveness of our presented techniques, with a better than 94\% accuracy in predicting ${Good}$/${Bad}$ classes for latent vectors. The Good/Bad data set is publicly available at https://zenodo.org/record/5850224#.YeGMwP7MKUk.

cs.CV↗

arXiv2022

OptGAN: Optimizing and Interpreting the Latent Space of the Conditional Text-to-Image GANs

Text-to-image generation intends to automatically produce a photo-realistic image, conditioned on a textual description. It can be potentially employed in the field of art creation, data augmentation, photo-editing, etc. Although many efforts have been dedicated to this task, it remains particularly challenging to generate believable, natural scenes. To facilitate the real-world applications of text-to-image synthesis, we focus on studying the following three issues: 1) How to ensure that generated samples are believable, realistic or natural? 2) How to exploit the latent space of the generator to edit a synthesized image? 3) How to improve the explainability of a text-to-image generation framework? In this work, we constructed two novel data sets (i.e., the Good & Bad bird and face data sets) consisting of successful as well as unsuccessful generated samples, according to strict criteria. To effectively and efficiently acquire high-quality images by increasing the probability of generating Good latent codes, we use a dedicated Good/Bad classifier for generated images. It is based on a pre-trained front end and fine-tuned on the basis of the proposed Good & Bad data set. After that, we present a novel algorithm which identifies semantically-understandable directions in the latent space of a conditional text-to-image GAN architecture by performing independent component analysis on the pre-trained weight values of the generator. Furthermore, we develop a background-flattening loss (BFL), to improve the background appearance in the edited image. Subsequently, we introduce linear interpolation analysis between pairs of keywords. This is extended into a similar triangular `linguistic' interpolation in order to take a deep look into what a text-to-image synthesis model has learned within the linguistic embeddings. Our data set is available at https://zenodo.org/record/6283798#.YhkN_ujMI2w.

cs.CV↗

arXiv2022

Suppressing ZZ Crosstalk of Quantum Computers through Pulse and Scheduling Co-Optimization

Noise is a significant obstacle to quantum computing, and $ZZ$ crosstalk is one of the most destructive types of noise affecting superconducting qubits. Previous approaches to suppressing $ZZ$ crosstalk have mainly relied on specific chip design that can complicate chip fabrication and aggravate decoherence. To some extent, special chip design can be avoided by relying on pulse optimization to suppress $ZZ$ crosstalk. However, existing approaches are non-scalable, as their required time and memory grow exponentially with the number of qubits involved. To address the above problems, we propose a scalable approach by co-optimizing pulses and scheduling. We optimize pulses to offer an ability to suppress $ZZ$ crosstalk surrounding a gate, and then design scheduling strategies to exploit this ability and achieve suppression across the whole circuit. A main advantage of such co-optimization is that it does not require special hardware support. Besides, we implement our approach as a general framework that is compatible with different pulse optimization methods. We have conducted extensive evaluations by simulation and on a real quantum computer. Simulation results show that our proposal can improve the fidelity of quantum computing on $4{\sim}12$ qubits by up to $81\times$ ($11\times$ on average). Ramsey experiments on a real quantum computer also demonstrate that our method can eliminate the effect of $ZZ$ crosstalk to a great extent.

quant-ph↗

arXiv2021

Rapid and Unconditional Parametric Reset Protocol for Tunable Superconducting Qubits

Qubit initialization is a critical task in quantum computation and communication. Extensive efforts have been made to achieve this with high speed, efficiency and scalability. However, previous approaches have either been measurement-based and required fast feedback, suffered from crosstalk or required sophisticated calibration. Here, we report a fast and high-fidelity reset scheme, avoiding the issues above without any additional chip architecture. By modulating the flux through a transmon qubit, we realize a swap between the qubit and its readout resonator that suppresses the excited state population to 0.08% $\pm$ 0.08% within 34 ns (284 ns if photon depletion of the resonator is required). Furthermore, our approach (i) can achieve effective second excited state depletion, (ii) has negligible effects on neighbouring qubits, and (iii) offers a way to entangle the qubit with an itinerant single photon, useful in quantum communication applications.

quant-ph↗

arXiv2021

DiverGAN: An Efficient and Effective Single-Stage Framework for Diverse Text-to-Image Generation

In this paper, we present an efficient and effective single-stage framework (DiverGAN) to generate diverse, plausible and semantically consistent images according to a natural-language description. DiverGAN adopts two novel word-level attention modules, i.e., a channel-attention module (CAM) and a pixel-attention module (PAM), which model the importance of each word in the given sentence while allowing the network to assign larger weights to the significant channels and pixels semantically aligning with the salient words. After that, Conditional Adaptive Instance-Layer Normalization (CAdaILN) is introduced to enable the linguistic cues from the sentence embedding to flexibly manipulate the amount of change in shape and texture, further improving visual-semantic representation and helping stabilize the training. Also, a dual-residual structure is developed to preserve more original visual features while allowing for deeper networks, resulting in faster convergence speed and more vivid details. Furthermore, we propose to plug a fully-connected layer into the pipeline to address the lack-of-diversity problem, since we observe that a dense layer will remarkably enhance the generative capability of the network, balancing the trade-off between a low-dimensional random latent code contributing to variants and modulation modules that use high-dimensional and textual contexts to strength feature maps. Inserting a linear layer after the second residual block achieves the best variety and quality. Both qualitative and quantitative results on benchmark data sets demonstrate the superiority of our DiverGAN for realizing diversity, without harming quality and semantic consistency.

cs.CV↗

arXiv2021

Exploiting Different Levels of Parallelism in the Quantum Control Microarchitecture for Superconducting Qubits

As current Noisy Intermediate Scale Quantum (NISQ) devices suffer from decoherence errors, any delay in the instruction execution of quantum control microarchitecture can lead to the loss of quantum information and incorrect computation results. Hence, it is crucial for the control microarchitecture to issue quantum operations to the Quantum Processing Unit (QPU) in time. As in classical microarchitecture, parallelism in quantum programs needs to be exploited for speedup. However, three challenges emerge in the quantum scenario: 1) the quantum feedback control can introduce significant pipeline stall latency; 2) timing control is required for all quantum operations; 3) QPU requires a deterministic operation supply to prevent the accumulation of quantum errors. In this paper, we propose a novel control microarchitecture design to exploit Circuit Level Parallelism (CLP) and Quantum Operation Level Parallelism (QOLP). Firstly, we develop a Multiprocessor architecture to exploit CLP, which supports dynamic scheduling of different sub-circuits. This architecture can handle parallel feedback control and minimize the potential overhead that disrupts the timing control. Secondly, we propose a Quantum Superscalar approach that exploits QOLP by efficiently executing massive quantum instructions in parallel. Both methods issue quantum operations to QPU deterministically. In the benchmark test of a Shor syndrome measurement, a six-core implementation of our proposal achieves up to 2.59$\times$ speedup compared with a single core. For various canonical quantum computing algorithms, our superscalar approach achieves an average of 4.04$\times$ improvement over a baseline design. Finally, We perform a simultaneous randomized benchmarking (simRB) experiment on a real QPU using the proposed microarchitecture for validation.

quant-ph↗

arXiv2020

DTGAN: Dual Attention Generative Adversarial Networks for Text-to-Image Generation

Most existing text-to-image generation methods adopt a multi-stage modular architecture which has three significant problems: 1) Training multiple networks increases the run time and affects the convergence and stability of the generative model; 2) These approaches ignore the quality of early-stage generator images; 3) Many discriminators need to be trained. To this end, we propose the Dual Attention Generative Adversarial Network (DTGAN) which can synthesize high-quality and semantically consistent images only employing a single generator/discriminator pair. The proposed model introduces channel-aware and pixel-aware attention modules that can guide the generator to focus on text-relevant channels and pixels based on the global sentence vector and to fine-tune original feature maps using attention weights. Also, Conditional Adaptive Instance-Layer Normalization (CAdaILN) is presented to help our attention modules flexibly control the amount of change in shape and texture by the input natural-language description. Furthermore, a new type of visual loss is utilized to enhance the image resolution by ensuring vivid shape and perceptually uniform color distributions of generated images. Experimental results on benchmark datasets demonstrate the superiority of our proposed method compared to the state-of-the-art models with a multi-stage framework. Visualization of the attention maps shows that the channel-aware attention module is able to localize the discriminative regions, while the pixel-aware attention module has the ability to capture the globally visual contents for the generation of an image.

cs.CV↗

arXiv2020

Shelf-stable quantum-dot light-emitting diodes with high operational performance

Quantum-dot light-emitting diodes (QLEDs) promise high-performance and cost-effective electroluminescent devices. However, shelf stability of QLEDs, a must for practical applications, is currently overlooked. Here we reveal that the state-of-the-art QLEDs exhibit abnormal shelf-ageing behaviours, i.e., improvements in performance (positive ageing), followed by deterioration of performance (negative ageing). Mechanism studies show that the organic acids in the encapsulation acrylic resin induce in-situ reactions, which simultaneously improve electrical conductance and minimize interfacial exciton quenching at the positive-ageing stage. Progression of the in-situ reactions results in negative ageing. Inspired by these findings, we design an electron-transporting bi-layer structure, which delivers both improved electrical conductivity and suppressed interfacial exciton quenching. This design enables shelf-stable QLEDs with high operational performance, i.e., neglectable changes of external quantum efficiency (>20.0%) and ultra-long operational lifetime (T95: 5,500 hours at 1,000 cd m-2) after storage for 180 days. Our work paves the way towards the commercialization of QLEDs.

physics.app-ph↗

arXiv2019

Experimental realization of nonadiabatic geometric gates with a superconducting Xmon qubit

Geometric phases are only dependent on evolution paths but independent of evolution details so that they own some intrinsic noise-resilience features. Based on different geometric phases, various quantum gates have been proposed, such as nonadiabatic geometric gates based on nonadiabatic Abelian geometric phases and nonadiabatic holonomic gates based on nonadiabatic non-Abelian geometric phases. Up to now, nonadiabatic holonomic one-qubit gates have been experimentally demonstrated with the supercondunting transmon, where three lowest levels with cascaded configuration are all applied in the operation. However, the second excited states of transmons have relatively short coherence time, which results in a lessened fidelity of quantum gates. Here, we experimentally realize Abelian-geometric-phase-based nonadiabatic geometric one-qubit gates with a superconducting Xmon qubit. The realization is performed on two lowest levels of an Xmon qubit and thus avoids the influence from the short coherence time of the second excited state. The experimental result indicates that the average fidelities of single-qubit gates can be up to 99.6% and 99.7% characterized by quantum process tomography and randomized benchmarking, respectively.

quant-ph↗

arXiv2018

Experimental realization of a fast controlled-Z gate via a shortcut-to-adiabaticity

For a frequency-tunable two-qubit system, a controlled-Z (CZ) gate can be realized by adiabatically driving the qubit system through an avoided level crossing between an auxiliary state and computational levels. Here, we theoretically propose a fast CZ gate using a shortcut-to-adiabaticity (STA). Experimentally, the STA CZ gate is implemented with a 52 ns control pulse for two coupled superconducting Xmon qubits. Measured fidelity of the STA CZ gate is higher than 96.0%, in both quantum process tomography and randomized benchmarking. The protocol allows a flexible design of the evolution time and control waveform. We suggest that this `fast adiabatic' CZ gate can be directly applied to other multi-qubit quantum systems.

quant-ph↗