arXiv subjects

Yuqin Wang

Publications and source records attributed to Yuqin Wang.

6 recordsLinked to original sources

arXiv2026

Measure of set imaginarity

Recent studies have shown that Bargmann invariants provide effective detectors of set imaginarity. In this paper, we investigate set imaginarity as a quantum resource in qubit systems. By exploiting the structure of Bargmann invariants, we show that the free operations for qubit set imaginarity consist precisely of common unital operations and common planarized operations. Based on this characterization, we introduce an axiomatic framework for set-imaginarity measures (SIMs). In particular, we propose two refined notions, namely unified SIMs and complete SIMs, which allow a more fine-grained quantification of set imaginarity. To make these notions concrete, we construct two qubit SIMs from the Bargmann invariants of three-state subsets. We prove that one of them is a unified SIM, while the other satisfies the stronger requirements of a complete SIM. Furthermore, we revisit the robustness of set imaginarity previously introduced in the literature. We show that, although this robustness is a valid SIM for qubit systems, it is neither a unified SIM nor a complete SIM. To overcome this limitation, we propose an improved robustness-type measure and rigorously prove that it defines a complete qubit SIM.

quant-ph↗

arXiv2026

Emergent trans-moir\'e orbitals and topology in rhombohedral graphene

The fractional quantum anomalous Hall effect (FQAHE) exhibited in fractional Chern insulators has recently been demonstrated in twisted MoTe2 and rhombohedral graphene/hBN moir\'e superlattices, promising new routes toward topological quantum computation. Central to realizing this promise is the understanding of the underlying microscopic mechanism. This, however, remains elusive in the case of rhombohedral graphene, with the crux being its two seemingly paradoxical conditions: a pronounced small-twist-angle ({\theta}) moir\'e interface, yet only when electrons are kept distant from it. Here, by scanning tunnelling microscopic imaging with both conditions fulfilled, we capture dramatic electronic structure reshaping in rhombohedral hexalayer graphene by unforeseen 'trans-moir\'e orbitals', which emerge on the other, distant side of the moir\'e interface but nevertheless enforce the moir\'e periodicity at all measured fillings. We visualize a hierarchy of spatially and energetically distinct trans-moir\'e orbitals which doped electrons must sequentially occupy--the lowest-energy orbital, expectedly responsible for the FQAHE at small fillings, carries a hollow-cage-like shape. Remarkably, these trans-moir\'e orbitals vanish at {\theta} {\gtrsim} 1{\deg}, and so do QAHE plateaus in similar devices. Simulations reveal an interaction-driven charge-redistribution mechanism which shapes the trans-moir\'e orbitals and corresponding Chern minibands. With our findings providing the missing microscopic link, the paradoxical conditions find a natural explanation: electrons are not simply kept distant from a small-{\theta} moir\'e interface; they are forced into topological trans-moir\'e orbitals, forged precisely under such conditions. Our microscopic diagnostics unlocks a wide range of possible 'synthetic' FQAHE platforms.

cond-mat.mes-hall↗

arXiv2026

Interfacial Potential Transduction for Diagnostics

A major barrier to decentralized, near-patient diagnostics is the lack of a signal transduction modality that is both analytically precise and accessible at the point of care. Optical readouts remain instrument-dependent and difficult to miniaturize, while compact electrochemical readouts are prone to matrix-derived signal distortion, limiting their biomarker coverage in real clinical settings. Here, we define interfacial potential transduction as a standardized electrical modality for portable, clinical-grade diagnostics across diverse assay formats. A mechanistic framework identifying key sample matrix parameters within the interfacial potentials transduction system enables control of biofluid-derived interference, and is demonstrated in a widely accessible lateral flow immunoassay format through quantitative detection of estradiol, progesterone, and luteinizing hormone in human plasma with high correlation (r2 > 0.97) to clinical analyzers. Broader applicability across representative diagnostic sectors is further demonstrated through exceptional performance including glucose quantification for biochemical analysis with limit of detection (LOD) of 0.92 ug/dL, HIV p24 capsid protein under an immunomagnetic separation workflow (LOD = 44.8 fg/mL), and hepatitis B virus detection within 5 min via loop-mediated isothermal amplification for molecular diagnostics. Together, these results establish interfacial potentials transduction as a unified diagnostic paradigm for near-patient deployment beyond optical and electrochemical approaches.

q-bio.BM↗

arXiv2024

A high-performance nitrogen-rich ZIF-8-derived Fe-Co-NC electrocatalyst for the oxygen reduction reaction

Exploring and developing low-cost, high-performance, and stable catalysts for the oxygen reduction reaction (ORR) is of great importance, though progress is ongoing. In this work, a simple evaporation-pyrolysis method is employed to synthesize 3D porous electrocatalysts, referred to as Fe-Co-NC, which are carbonization products at 900°C. These catalysts are composed of carbon nanoparticles and metallic FeCo doped nitrogen-enriched carbon nanotubes, produced through the carbonization of pristine ZIF-8, resulting in highly efficient and durable ORR electrocatalysts. The Fe-Co-NC structure exhibits a 3D open porous texture, abundant active sites, favorable nitrogen bonding, and a high specific surface area, all of which contribute to excellent ORR activity. The optimal Fe-Co-NC catalyst demonstrates exceptional ORR performance, with a high onset potential of 0.96 V and a half-wave potential of 0.86 V versus RHE, rivaling the performance of commercially available Pt/C in an alkaline electrolyte. Additionally, the Fe-Co-NC catalyst shows superior stability compared to Pt/C, which is crucial for the development of novel electrocatalysts based on non-precious metals.

cond-mat.mtrl-sci↗

arXiv2019

Unsupervised Domain Adaptation with Adversarial Residual Transform Networks

Domain adaptation is widely used in learning problems lacking labels. Recent studies show that deep adversarial domain adaptation models can make markable improvements in performance, which include symmetric and asymmetric architectures. However, the former has poor generalization ability whereas the latter is very hard to train. In this paper, we propose a novel adversarial domain adaptation method named Adversarial Residual Transform Networks (ARTNs) to improve the generalization ability, which directly transforms the source features into the space of target features. In this model, residual connections are used to share features and adversarial loss is reconstructed, thus making the model more generalized and easier to train. Moreover, a special regularization term is added to the loss function to alleviate a vanishing gradient problem, which enables its training process stable. A series of experiments based on Amazon review dataset, digits datasets and Office-31 image datasets are conducted to show that the proposed ARTN can be comparable with the methods of the state-of-the-art.

cs.CV↗

arXiv2019

Virtual Conditional Generative Adversarial Networks

When trained on multimodal image datasets, normal Generative Adversarial Networks (GANs) are usually outperformed by class-conditional GANs and ensemble GANs, but conditional GANs is restricted to labeled datasets and ensemble GANs lack efficiency. We propose a novel GAN variant called virtual conditional GAN (vcGAN) which is not only an ensemble GAN with multiple generative paths while adding almost zero network parameters, but also a conditional GAN that can be trained on unlabeled datasets without explicit clustering steps or objectives other than the adversary loss. Inside the vcGAN's generator, a learnable ``analog-to-digital converter (ADC)" module maps a slice of the inputted multivariate Gaussian noise to discrete/digital noise (virtual label), according to which a selector selects the corresponding generative path to produce the sample. All the generative paths share the same decoder network while in each path the decoder network is fed with a concatenation of a different pre-computed amplified one-hot vector and the inputted Gaussian noise. We conducted a lot of experiments on several balanced/imbalanced image datasets to demonstrate that vcGAN converges faster and achieves improved Frechét Inception Distance (FID). In addition, we show the training byproduct that the ADC in vcGAN learned the categorical probability of each mode and that each generative path generates samples of specific mode, which enables class-conditional sampling. Codes are available at \url{https://github.com/annonnymmouss/vcgan}

cs.CV↗