arXiv subjects

Qiong Chen

Publications and source records attributed to Qiong Chen.

At least 19 recordsLinked to original sources

arXiv2026

A Connectivity-Order Law and Conditional Minimal-Mechanism Identification in Many-Body Geometric Phases

The value of a multiqubit geometric phase at a single operating point does not reveal whether it was generated directly or through a connected sequence of lower-body interactions. For analytic, gapped, nondegenerate Abelian holonomies, we jointly resolve logical support and independently calibrated coupling support. Every nonzero connected response must involve an active set that connects and covers the target and must contain one factor of each active coupling, yielding the sharp onset bound $\nu_S \geq \tau_E(S)$. The same selection law applies locally to projected Berry curvature. Conversely, an unrestricted finite-dimensional construction realizes all responses allowed by the connected-cover condition simultaneously, making the condition necessary and sufficient within this theorem class. Operationally, simultaneous confidence bands certify detected mixed responses while controlling false positives; a factor-of-two separation condition additionally recovers exact library-relative response minima. In a complete dictionary, nontriviality of every minimal-cover response is generically necessary and sufficient for these minima to coincide with the minimal calibrated mechanisms. Three-qubit Wilson calculations distinguish direct and pair-mediated routes with the same endpoint phase, and a synthetic Ramsey audit locates the finite-resolution boundary.

quant-ph↗

arXiv2026

Rollout-Training Co-Design for Efficient LLM-Based Multi-Agent Reinforcement Learning

Despite algorithm-level innovations for multi-agent reinforcement learning (MARL), the underlying networked infrastructure for large-scale MARL training remains underexplored. Existing training frameworks primarily optimize for single-agent scenarios and fail to address the unique system-level challenges of MARL, including rollout-training synchronization barriers, rollout load imbalance, and training resource underutilization. To bridge this gap, we propose FlexMARL, the first end-to-end training framework that holistically optimizes rollout, training, and their orchestration for large-scale LLM-based MARL. Specifically, FlexMARL introduces the joint orchestrator to manage data flow under the rollout-training disaggregated architecture. Building upon the experience store, a novel micro-batch driven asynchronous pipeline eliminates the synchronization barriers while providing strong consistency guarantees. Rollout engine adopts a parallel sampling scheme combined with hierarchical load balancing, which adapts to skewed inter/intra-agent request patterns. Training engine achieves on-demand hardware binding through agent-centric resource allocation. The training states of different agents are swapped via unified and location-agnostic communication. Empirical results on a large-scale production cluster demonstrate that FlexMARL achieves up to 7.3x speedup and improves hardware utilization by up to 5.6x compared to existing frameworks.

cs.LG↗

arXiv2025

Wide band magnetometry based on color centers with transverse zero-field splitting

Solid-state color centers embedded in diamond, silicon carbide (SiC) and other host-matrices offer a promising platform for nanoscale quantum sensing. Sometimes a relatively strong transverse zero-field splitting (ZFS) is introduced in a color center due to its local strain and low symmetry of the host structure. While clock transitions induced by transverse zero-field splitting (ZFS) serve as a method to prolong the coherence time, the Zeeman sub-levels of solid-state color centers become insensitive to first-order magnetic field signals, thereby limiting their utility as magnetometers. In this work, we address this challenge and achieve wide band AC field detection spanning from hundreds of kHz to hundreds of MHz by utilizing a combination of orthogonal microwaves and phase modulation at different frequencies. Our control method effectively suppresses the system noise and the amplitude fluctuation of the driving field, which extends the coherence time of the quantum system.

quant-ph↗

arXiv2025

Uncertainty Aware Human-machine Collaboration in Camouflaged Object Detection

Camouflaged Object Detection (COD), the task of identifying objects concealed within their environments, has seen rapid growth due to its wide range of practical applications. A key step toward developing trustworthy COD systems is the estimation and effective utilization of uncertainty. In this work, we propose a human-machine collaboration framework for classifying the presence of camouflaged objects, leveraging the complementary strengths of computer vision (CV) models and noninvasive brain-computer interfaces (BCIs). Our approach introduces a multiview backbone to estimate uncertainty in CV model predictions, utilizes this uncertainty during training to improve efficiency, and defers low-confidence cases to human evaluation via RSVP-based BCIs during testing for more reliable decision-making. We evaluated the framework in the CAMO dataset, achieving state-of-the-art results with an average improvement of 4.56\% in balanced accuracy (BA) and 3.66\% in the F1 score compared to existing methods. For the best-performing participants, the improvements reached 7.6\% in BA and 6.66\% in the F1 score. Analysis of the training process revealed a strong correlation between our confidence measures and precision, while an ablation study confirmed the effectiveness of the proposed training policy and the human-machine collaboration strategy. In general, this work reduces human cognitive load, improves system reliability, and provides a strong foundation for advancements in real-world COD applications and human-computer interaction. Our code and data are available at: https://github.com/ziyuey/Uncertainty-aware-human-machine-collaboration-in-camouflaged-object-identification.

cs.CV↗

arXiv2025

CSSSTN: A Class-sensitive Subject-to-subject Semantic Style Transfer Network for EEG Classification in RSVP Tasks

The Rapid Serial Visual Presentation (RSVP) paradigm represents a promising application of electroencephalography (EEG) in Brain-Computer Interface (BCI) systems. However, cross-subject variability remains a critical challenge, particularly for BCI-illiterate users who struggle to effectively interact with these systems. To address this issue, we propose the Class-Sensitive Subject-to-Subject Semantic Style Transfer Network (CSSSTN), which incorporates a class-sensitive approach to align feature distributions between golden subjects (BCI experts) and target (BCI-illiterate) users on a class-by-class basis. Building on the SSSTN framework, CSSSTN incorporates three key components: (1) subject-specific classifier training, (2) a unique style loss to transfer class-discriminative features while preserving semantic information through a modified content loss, and (3) an ensemble approach to integrate predictions from both source and target domains. We evaluated CSSSTN using both a publicly available dataset and a self-collected dataset. Experimental results demonstrate that CSSSTN outperforms state-of-the-art methods, achieving mean balanced accuracy improvements of 6.4\% on the Tsinghua dataset and 3.5\% on the HDU dataset, with notable benefits for BCI-illiterate users. Ablation studies confirm the effectiveness of each component, particularly the class-sensitive transfer and the use of lower-layer features, which enhance transfer performance and mitigate negative transfer. Additionally, CSSSTN achieves competitive results with minimal target data, reducing calibration time and effort. These findings highlight the practical potential of CSSSTN for real-world BCI applications, offering a robust and scalable solution to improve the performance of BCI-illiterate users while minimizing reliance on extensive training data. Our code is available at https://github.com/ziyuey/CSSSTN.

eess.SP↗

arXiv2024

Robust external spin hyperpolarization of quadrupolar nuclei enabled by strain

In a theoretical study, we investigate the spin dynamics of interacting nitrogen-vacancy (NV) centers and quadrupolar I = 3/2 nuclear spins, specifically 11B spins in hexagonal boron nitride (h-BN) nanosheets located near the microdiamond surface. We demonstrate the possibility of obtaining external spin-polarization by magnetic-field sweeps across the level anticrossings around zero-field. To achieve this, we leverage crystal strains to establish a polarization transfer mechanism that remains robust against variations in NV orientation, crystal strain inhomogeneity, and electron-nuclear effective couplings. These results pave the way for hyperpolarization of spins in nanomaterials near the diamond surface without experiencing polarization loss to intrinsic nuclear spin-1/2 species, such as 13C and 1H nuclear spins in diamond. The 11B spins in h-BN nanosheets, with their extended relaxation time and large surface area, present a promising alternative for relayed nuclear polarization to the liquid phase and for the development of quantum simulators based on surface nuclear spins.

quant-ph↗

arXiv2024

Coherently controlling robust spin-orbit qubits of electrons in nanowire quantum dots

We consider an electron confined in a gated nanowire quantum dot (NQD) with arbitrarily strong spin-orbit coupling (SOC) and weak static magnetic field, and treat the latter as a perturbation to seek the maximal spin-motion entangled states with the exact general solutions of the perturbed equations. From the boundedness and self-consistent conditions of the general solutions we find two corrected energies to any n level of the unperturbed system with ground state n = 0, which are much less than the unperturbed level-difference and corresponds to a spin-orbit qubit. We demonstrate the metastability of the two-level states and the decoherence-averse effect of SOC, and suggest an alternative scheme to perform the qubit control, simply by adjusting the orientation of magnetic field for any fixed SOC. Such a adjustment can lead to the spin flipping of the state vector and the position exchanging of the probability-density wavepackets which can be proposed as the non-Abelian quasiparticles. The results could be directly extended to a weakly coupled array of NQDs for coherently encoding the robust spin-orbit qubits.

cond-mat.mes-hall↗

arXiv2023

A dual-branch model with inter- and intra-branch contrastive loss for long-tailed recognition

Real-world data often exhibits a long-tailed distribution, in which head classes occupy most of the data, while tail classes only have very few samples. Models trained on long-tailed datasets have poor adaptability to tail classes and the decision boundaries are ambiguous. Therefore, in this paper, we propose a simple yet effective model, named Dual-Branch Long-Tailed Recognition (DB-LTR), which includes an imbalanced learning branch and a Contrastive Learning Branch (CoLB). The imbalanced learning branch, which consists of a shared backbone and a linear classifier, leverages common imbalanced learning approaches to tackle the data imbalance issue. In CoLB, we learn a prototype for each tail class, and calculate an inter-branch contrastive loss, an intra-branch contrastive loss and a metric loss. CoLB can improve the capability of the model in adapting to tail classes and assist the imbalanced learning branch to learn a well-represented feature space and discriminative decision boundary. Extensive experiments on three long-tailed benchmark datasets, i.e., CIFAR100-LT, ImageNet-LT and Places-LT, show that our DB-LTR is competitive and superior to the comparative methods.

cs.CV↗

arXiv2023

MIANet: Aggregating Unbiased Instance and General Information for Few-Shot Semantic Segmentation

Existing few-shot segmentation methods are based on the meta-learning strategy and extract instance knowledge from a support set and then apply the knowledge to segment target objects in a query set. However, the extracted knowledge is insufficient to cope with the variable intra-class differences since the knowledge is obtained from a few samples in the support set. To address the problem, we propose a multi-information aggregation network (MIANet) that effectively leverages the general knowledge, i.e., semantic word embeddings, and instance information for accurate segmentation. Specifically, in MIANet, a general information module (GIM) is proposed to extract a general class prototype from word embeddings as a supplement to instance information. To this end, we design a triplet loss that treats the general class prototype as an anchor and samples positive-negative pairs from local features in the support set. The calculated triplet loss can transfer semantic similarities among language identities from a word embedding space to a visual representation space. To alleviate the model biasing towards the seen training classes and to obtain multi-scale information, we then introduce a non-parametric hierarchical prior module (HPM) to generate unbiased instance-level information via calculating the pixel-level similarity between the support and query image features. Finally, an information fusion module (IFM) combines the general and instance information to make predictions for the query image. Extensive experiments on PASCAL-5i and COCO-20i show that MIANet yields superior performance and set a new state-of-the-art. Code is available at https://github.com/Aldrich2y/MIANet.

cs.CV↗

arXiv2022

MoTiAC: Multi-Objective Actor-Critics for Real-Time Bidding

Online Real-Time Bidding (RTB) is a complex auction game among which advertisers struggle to bid for ad impressions when a user request occurs. Considering display cost, Return on Investment (ROI), and other influential Key Performance Indicators (KPIs), large ad platforms try to balance the trade-off among various goals in dynamics. To address the challenge, we propose a Multi-ObjecTive Actor-Critics algorithm based on reinforcement learning (RL), named MoTiAC, for the problem of bidding optimization with various goals. In MoTiAC, objective-specific agents update the global network asynchronously with different goals and perspectives, leading to a robust bidding policy. Unlike previous RL models, the proposed MoTiAC can simultaneously fulfill multi-objective tasks in complicated bidding environments. In addition, we mathematically prove that our model will converge to Pareto optimality. Finally, experiments on a large-scale real-world commercial dataset from Tencent verify the effectiveness of MoTiAC versus a set of recent approaches

cs.AI↗

arXiv2022

Geometrical bounds on irreversibility in squeezed thermal bath

Irreversible entropy production (IEP) plays an important role in quantum thermodynamic processes. Here we investigate the geometrical bounds of IEP in nonequilibrium thermodynamics by exemplifying a system coupled to a squeezed thermal bath subject to dissipation and dephasing, respectively. We find that the geometrical bounds of the IEP always shift in contrary way under dissipation and dephasing, where the lower and upper bounds turning to be tighter occurs in the situation of dephasing and dissipation, respectively. However, either under dissipation or under dephasing, we may reduce both the critical time of the IEP itself and the critical time of the bounds for reaching an equilibrium by harvesting the benefits of squeezing effects, in which the values of the IEP, quantifying the degree of thermodynamic irreversibility, also becomes smaller. Therefore, due to the nonequilibrium nature of the squeezed thermal bath, the system-bath interaction energy brings prominent impact on the IEP, leading to tightness of its bounds. Our results are not contradictory with the second law of thermodynamics by involving squeezing of the bath as an available resource, which can improve the performance of quantum thermodynamic devices.

quant-ph↗

arXiv2021

On-edge Multi-task Transfer Learning: Model and Practice with Data-driven Task Allocation

On edge devices, data scarcity occurs as a common problem where transfer learning serves as a widely-suggested remedy. Nevertheless, transfer learning imposes a heavy computation burden to resource-constrained edge devices. Existing task allocation works usually assume all submitted tasks are equally important, leading to inefficient resource allocation at a task level when directly applied in Multi-task Transfer Learning (MTL). To address these issues, we first reveal that it is crucial to measure the impact of tasks on overall decision performance improvement and quantify \emph{task importance}. We then show that task allocation with task importance for MTL (TATIM) is a variant of the NP-complete Knapsack problem, where the complicated computation to solve this problem needs to be conducted repeatedly under varying contexts. To solve TATIM with high computational efficiency, we propose a Data-driven Cooperative Task Allocation (DCTA) approach. Finally, we evaluate the performance of DCTA by not only a trace-driven simulation, but also a new comprehensive real-world AIOps case study that bridges model and practice via a new architecture and main components design within the AIOps system. Extensive experiments show that our DCTA reduces 3.24 times of processing time, and saves 48.4\% energy consumption compared with the state-of-the-art when solving TATIM.

cs.DC↗

arXiv2020

Limited-control metrology approaching the Heisenberg limit without entanglement preparation

Current metrological bounds typically assume full control over all particles that are involved in the protocol. Relaxing this assumption we study metrological performance when only limited control is available. As an example, we measure a static magnetic field when a fully controlled quantum sensor is supplemented by particles over which only global control is possible. We show that even for a noisy quantum sensor, a protocol that maps the magnetic field to a precession frequency can achieve transient super-Heisenberg scaling in measurement time and Heisenberg scaling in the particle number. This leads to an estimation uncertainty that approaches that achievable under full control to within a factor independent of the particle number for a given total time. Applications to hybrid sensing devices and the crucial role of the quantum character of the sensor are discussed.

quant-ph↗

arXiv2019

Comparative studies of optoelectrical properties of prominent PV materials: Halide Perovskite, CdTe, and GaAs

We compare three representative high performance PV materials: halide perovskite MAPbI3, CdTe, and GaAs, in terms of photoluminescence (PL) efficiency, PL lineshape, carrier diffusion, and surface recombination, over multiple orders of photo-excitation density. An analytic model is used to describe the excitation density dependence of PL intensity and extract the internal PL efficiency and multiple pertinent recombination parameters. A PL imaging technique is used to obtain carrier diffusion length without using a PL quencher, thus, free of unintended influence beyond pure diffusion. Our results show that perovskite samples tend to exhibit lower Shockley-Read-Hall (SRH) recombination rate in both bulk and surface, thus higher PL efficiency than the inorganic counterparts, particularly under low excitation density, even with no or preliminary surface passivation. PL lineshape and diffusion analysis indicate that there is considerable structural disordering in the perovskite materials, and thus photo-generated carriers are not in global thermal equilibrium, which in turn suppresses the nonradiative recombination. This study suggests that relatively low point-defect density, less detrimental surface recombination, and moderate structural disordering contribute to the high PV efficiency in the perovskite. This comparative photovoltaics study provides more insights into the fundamental material science and the search for optimal device designs by learning from different technologies.

physics.app-ph↗

arXiv2019

Deep Reinforcement Learning for Imbalanced Classification

Data in real-world application often exhibit skewed class distribution which poses an intense challenge for machine learning. Conventional classification algorithms are not effective in the case of imbalanced data distribution, and may fail when the data distribution is highly imbalanced. To address this issue, we propose a general imbalanced classification model based on deep reinforcement learning. We formulate the classification problem as a sequential decision-making process and solve it by deep Q-learning network. The agent performs a classification action on one sample at each time step, and the environment evaluates the classification action and returns a reward to the agent. The reward from minority class sample is larger so the agent is more sensitive to the minority class. The agent finally finds an optimal classification policy in imbalanced data under the guidance of specific reward function and beneficial learning environment. Experiments show that our proposed model outperforms the other imbalanced classification algorithms, and it can identify more minority samples and has great classification performance.

cs.LG↗

arXiv2018

Pulsed polarisation for robust DNP

Dynamical nuclear polarisation (DNP) is an important technique that uses polarisation transfer from electron to nuclear spins to achieve nuclear hyperpolarisation. As the electron spin of the nitrogen vacancy (NV) centres in diamond can be optically initialised nearly perfectly even at room temperature and ambient conditions, new opportunities become possible by the combination of efficient DNP with optically polarised NV centres. Among such applications are nanoscale nuclear magnetic resonance spectroscopy of liquids, hyperpolarised nanodiamonds as MRI contrast agents as well as the initialisation of nuclear spin based diamond quantum simulators. Current realisations of DNP perform the polarisation transfer by achieving energetic resonance between electrons and nuclei via carefully tuned microwave fields or by using quasi-adiabatic sweep-based schemes across resonance points. The former limits robustness against control errors while the latter limits polarisation rates, making the realisation of the applications extremely challenging. Here we introduce the concept of Hamiltonian engineering by pulse sequences and use it for the systematic design of polarisation sequences that are simultaneously robust and fast. We derive sequences theoretically and demonstrate experimentally that they are capable of efficient polarisation transfer from an optically polarised nitrogen-vacancy centre in diamond to the surrounding $^{13}$C nuclear spin bath even in the presence of control errors, making it an ideal tool for the realisation of the above NV centre based applications.

quant-ph↗

arXiv2018

Braiding a novel kind of Majorana-like quasiparticles in nanowire quantum dots

For an electrically driven electron confined in a nanowire quantum dot with spin-orbit coupling (SOC), we find a SOC-magnetism phase-locked condition under which we derive a complete set of Schrödinger kitten states which contains some novel degenerate ground states with oscillating wave packets or stationary double packets in undriven case. We identify such wave packets as Majorana-like quasiparticles and demonstrate that they obey non-Abelian statistics and behave similarly to neutral particles. The braiding operations based on the interchanges of the degenerate non-Abelian quasiparticles are shown, which shift the system between different ground states and may be insensitive to perturbations and weak noise from the environment. The results could be tested experimentally in the existing setups and could be treated as the leading-order results to directly extended to an array of weakly coupled single-electron quantum dots for topological quantum computation.

cond-mat.mes-hall↗

arXiv2017

Robust techniques for polarization and detection of nuclear spin ensembles

Highly sensitive nuclear spin detection is crucial in many scientific areas including nuclear magnetic resonance spectroscopy (NMR), imaging (MRI) and quantum computing. The tiny thermal nuclear spin polarization represents a major obstacle towards this goal which may be overcome by Dynamic Nuclear Spin Polarization (DNP) methods. The latter often rely on the transfer of the thermally polarized electron spins to nearby nuclear spins, which is limited by the Boltzmann distribution of the former. Here we demonstrate the polarization and read out of a nuclear spin bath consisting of $^{13}$C nuclear spins in diamond by using a single nitrogen-vacancy (NV) center. Our method utilizes microwave dressed states to transfer the NV's high ($>$~92~\%) non-equilibrium electron spin polarization induced by short laser pulses to the surrounding carbon nuclear spins, where the NV is repeatedly repolarized optically, thus providing an effectively infinite polarization reservoir. A saturation of the polarization in the nuclear "frozen core" is achieved, which is confirmed by the decay of the polarization transfer signal and shows an excellent agreement with theoretical simulations. Hereby we introduce the Polarization Read Out by Polarization Inversion (PROPI) method as a quantitative magnetization measure of the nuclear spin bath. Moreover, we show that using the integrated solid effect both for single and double quantum transitions a nuclear spin polarization can be achieved even when the static magnetic field is not aligned along the NV's crystal axis. This opens a path for the application of our DNP technique to spins in and outside of nanodiamonds, enabling their application as MRI tracers.

quant-ph↗