arXiv subjects

Hong Yao

Publications and source records attributed to Hong Yao.

At least 19 recordsLinked to original sources

arXiv2026

Floquet Majorana XYZ Codes with Tunable Logical Dynamics

We construct Floquet codes from the Majorana XYZ subsystem code with local realizations both in qubits and directly in microscopic Majorana modes with lattice size $L\times L$. For a three-step cycle, odd $L$ supports one static logical qubit, whereas even $L$ supports two. For even $L$ with $L=4n+2$, both logical qubits admit time-independent Pauli representatives. For $L=4n$, by contrast, one Pauli of the second logical qubit remains fixed throughout the cycle, while every representative of its conjugate must evolve through the measurement cycle. This distinction follows from a parity-dependent algebraic obstruction and disappears when the cycle is reduced to two steps, which restores a fully static logical pair. Thus, the same encoded logical degree of freedom can be switched between static and partially dynamical forms by the measurement schedule. With one open direction, suitable protocols can be implemented using only local Majorana parity measurements. To our knowledge, this is the first Floquet-code family with both a local qubit representation and a direct microscopic Majorana realization.

quant-ph↗

arXiv2026

Exact pair density wave in topological moire flat bands and universal superfluid stiffness

Pair-density waves (PDWs) are unconventional superconducting states beyond the BCS paradigm, but identifying them unambiguously in microscopic lattice models is generally challenging due to various competing orders. In particular, an exact realization of PDW in interacting models with topological bands remains elusive. Here we construct an interacting model on a twisted bilayer checkerboard lattice (TBCB) featuring topological flat bands, and rigorously show that an exact PDW ground state can be induced by quantum geometric nesting (QGN), owing to its momentum-space nonsymmorphic symmetry. We further prove that the superfluid stiffness of any nondegenerate QGN superconductor (including the PDW studied here and uniform superconductor) obeys the universal relation $D_s=2N_{\rm flat}\nu(1-\nu)m_{\rm pair}^{-1}$, where $N_{\rm flat}$, $\nu$, and $m_{\rm pair}$ are the number of flat bands, flat band filling, and the two-particle effective mass, respectively. Our results reveal a rich interplay among PDW order, quantum geometry, and band topology.

cond-mat.supr-con↗

arXiv2026

Chiral spin liquid and chiral antiferromagnetism in half-filled moir\'e Hubbard model: possible applications to twisted bilayer TMDs

Twisted transition metal dichalcogenides offer an exceptionally tunable moir\'e platform for studying correlation physics beyond conventional condensed matter systems. In particular, the intriguing interplay between the displacement field and the twist angle remains to be fully resolved. In this paper, we use large-scale density matrix renormalization group simulations to study the minimal moir\'e Hubbard model on a triangular lattice at half-filling, where the displacement field effect is captured by a spin-dependent staggered flux. We find that the displacement field significantly enriches the triangular Hubbard phase diagram in several qualitative ways. It rapidly destabilizes the chiral spin liquid phase beyond a narrow weak-field regime, induces pronounced chiral correlations in the strong-coupling $120^\circ$-antiferromagnetic phase, and stabilizes incommensurate spin-density wave phases at weaker coupling. We further find signatures of a continuous transition between the chiral spin liquid and chiral antiferromagnetic phases at a finite displacement field, potentially driven by spinon condensation. Our results uncover rich displacement-field-driven many-body physics and provide useful guidance for future experiments in moir\'e superlattice systems.

cond-mat.str-el↗

arXiv2026

Enhancement of E-commerce Sponsored Search Relevancy with LLM

Sponsored search plays a crucial role as a revenue stream for search engines, wherein advertisers competitively bid on keywords that align with the users' search queries. The task of matching relevant keywords to these queries is complicated by the vast and ever-evolving space of keywords, the ambiguity of user and advertiser intentions, and the wide range of topics and languages involved. Consequently, ensuring that ads are pertinent to user queries presents significant challenges. In the fast-paced world of e-commerce, the accuracy of sponsored search results is vital for boosting user satisfaction and optimizing business operations. This paper presents the development of an advanced Ad Relevance Model within a sponsored search framework, utilizing the power of a pretrained large language model. We detail a pioneering adaptation of the LLAMA2 7B model through Low-Rank Adaptation (LoRA), which markedly enhances search precision and operational efficiency, thus opening new avenues for improving user interactions in extensive online marketplaces such as Walmart.com. We introduce a novel query and ad title classifier, which discerns the relevance of search interactions across three categories: Relevant, Partially Relevant, and Irrelevant. Our approach involved adapting the pretrained model specifically for the e-commerce sponsored search context, training it on a large dataset. The fine-tuned model demonstrated a marked improvement in ad relevance accuracy, achieving 89.43% accuracy on a comprehensive test dataset -- outperforming both the baseline model and other advanced language models like GPT-4. The integration of LoRA with the based model represents a significant stride in customizing language models for e-commerce applications, resulting in enhanced search accuracy, cost efficiency, and operational privacy -- a triad essential for the modern digital marketplace.

cs.IR↗

arXiv2026

AutoRelAnnotator: Calibrated Model Cascades for Cost-Efficient Relevance Evaluation in Sponsored Search

How can we generate high-quality relevance annotations at scale without the cost and delays of human labeling? Relevance annotations are the backbone of search ranking systems which is needed for training data preparation, NDCG evaluation, and root cause analysis. However, human annotation is slow and off-the-shelf LLMs suffer from accuracy on domain-specific tasks. We propose a calibrated model cascade, a systematic approach for cost-efficient offline relevance annotation by routing queries through progressively larger fine-tuned classifiers. Our central insight is that accuracy and cost are orthogonal optimizations: domain-specific fine-tuning drives accuracy, cascading drives cost, and per-class isotonic calibration adds a small but reliable gain on top. Our contribution is threefold: (a) we decompose the gains and show that fine-tuning contributes 20 accuracy points while cascading is approximately accuracy-neutral but halves compute cost, (b) we introduce per-class isotonic calibration as one component of the cascade, contributing a small but statistically significant gain (+0.6 points over the strongest calibration baseline), and (c) we validate the system in production across six offline use cases, processing 150M+ annotations and enabling faster experimentation cycles. Our work is a building block for scalable, high-quality offline annotation pipelines in search and advertising systems.

cs.IR↗

arXiv2026

INSPIRE: Intent-aware Neural Sponsored Product Retrieval for E-commerce

Walmart holds the largest share of the U.S. ecommerce grocery market, where food and beverage categories generate some of the highest search traffic and, consequently, drive a substantial portion of sponsored search revenue. At this scale, even small mismatches between user intent and retrieved products can lead to losses in both user engagement and monetization. Yet, understanding user intent in grocery search is inherently challenging. Queries are typically short, ambiguous, and highly diverse, often underspecifying critical preferences. From the advertisers perspective, many products are explicitly designed to target specific intents such as dietary preferences or size variants and must be surfaced at the right moment to be effective. Thus, we propose INSPIRE (Intent aware Neural Sponsored Product Retrieval for Ecommerce), an intent aware retrieval framework for sponsored search that leverages structured intent signals to better align user queries with relevant food and beverage products. INSPIRE represents intent as a set of structured, multi dimensional attributes derived from both user queries and product content, capturing explicit signals (e.g., brand, flavor) as well as implicit preferences (e.g., dietary constraints, cuisine types) that are often not directly expressed in queries. We develop a weakly supervised intent learning pipeline, where a large language model serves as a teacher to generate structured intent annotations from product titles and descriptions. We then distill these annotations by using them to finetune a lightweight student LLM model through LoRA based supervised finetuning that predicts intent attributes. We then introduce an intent augmented dense retrieval framework, where predicted intents are incorporated into query and product representations within a biencoder, enabling more precise matching between queries and sponsored products.

cs.IR↗

arXiv2026

Scaling Dense Retrieval with LLM-Annotated Training Data: Structured Mining and Progressive Curriculum for E-Commerce Sponsored Search

How can we generate high-quality training data for dense retrieval models at production scale, without relying on click signals or manual annotation? This question is critical for e-commerce sponsored search, where click-based training suffers from position bias and tail-query sparsity, and manual labeling at the scale of hundreds of millions of query-item pairs is economically infeasible. Our work is driven by the following insight: heterogeneous retrieval systems disagree on most items they retrieve, and this disagreement creates a natural source of structured training signal -- easy positives where all systems agree, hard positives that only lexical systems find, and hard negatives that fool exactly one system. As our key novelty, we combine three ideas into an end-to-end pipeline: (a) multi-channel retrieval mining with rank metadata from three production systems, (b) graded-relevance annotation by a calibrated three-model cascade ) that reaches 89.1% agreement with trained human annotators, and (c) three-stage progressive curriculum training that organizes 240M+ training examples across five difficulty levels. We deploy the trained two-tower BERT model on Walmart's sponsored search and evaluate it against 30K queries labeled by trained third-party human annotators. First, we show that the system achieves +5.1% NDCG@10 over the click-trained production baseline, with the largest gain on tail queries . Second, we show that embarrassing retrievals (rating 0) drop from 8.7% to 3.5%. Third, a two-week online A/B test with tens of millions of ad requests per arm confirms +2.80% ad spend, +1.4% CTR, +2.8% eCPM, and +2.9% click conversion rate. Overall, our work provides a practical and scalable blueprint for replacing click-based training with structured LLM-annotated supervision in production retrieval systems.

cs.IR↗

arXiv2026

Unified Multi-Task Relevance Modeling for E-Commerce: Comparing Task Routing Architectures Across LLMs and Cross-Encoders

How can we build a single relevance model that handles six different entity pair relationship types in e commerce from query product matching to product type similarity when each task has different data volumes, different semantic requirements, and potentially conflicting learning signals? This question is important because current industry practice relies on separate models for each task, preventing knowledge transfer and producing inconsistent relevance signals. Our work is driven by the following insight: encoder based and decoder only models encode task identity through different mechanisms, so the choice of task routing architecture how task identity is communicated to the shared model affects these two families in asymmetric ways. As our key novelty, we combine three ideas: (a) a unified multi task framework that jointly trains on six entity pair tasks under a shared three point relevance scale, (b) a systematic comparison of three task routing architectures (text prefix routing, multi head classification, and multihead with private transformer layers) across both LoRA adapted LLMs and fully finetuned cross encoders, and (c) a majority vote ensemble that exploits the diversity induced by private layer routing. First, we show that the MHP Ensemble (multi head with private layers) achieves 89.96% accuracy on 453K test examples the highest across all configurations . Second, we show that removing text prefixes without private layers causes severe degradation for decoder only LLMs while cross encoders remain robust , suggesting an encoder decoder asymmetry in task identity encoding. Third, we show that multi task training yields up to 14% improvement on low resource tasks over single task baselines.

cs.IR↗

arXiv2026

Analytic Bootstrap for $O(N)$ Boundary Conformal Field Theories with Interacting Boundaries

We investigate $O(N)$ boundary conformal field theories (BCFTs) with boundary interactions in $d=4-\epsilon$ and $d=3-\epsilon$ employing the analytic bootstrap. By deriving universal constraints on conformal data, we show that infinitely many operator expansions can be expressed in terms of a finite set of inputs. Complementing the analytic bootstrap with a perturbative renormalization-group analysis, we identify totally new boundary fixed points in $d=4-\epsilon$, including non-unitary ones, generated by a boundary cubic coupling, and compute their conformal data to leading order. Moreover, we leverage our solution in $d=3-\epsilon$ to extract, for the first time, the boundary conformal data for the tricritical $O(N)$ model. Altogether, our approach provides a unified prescription for BCFTs with interacting boundaries and streamlines the determination of bulk and boundary operator expansions.

hep-th↗

arXiv2026

Unified Supervision for Walmart's Sponsored Search Retrieval via Joint Semantic Relevance and Behavioral Engagement Modeling

Modern search systems rely on a fast first stage retriever to fetch relevant items from a massive catalog of items. Deployed search systems often use user engagement signals to supervise bi-encoder retriever training at scale, because these signals are continuously logged from real traffic and require no additional annotation effort. However, engagement is an imperfect proxy for semantic relevance. Items may receive interactions due to popularity, promotion, attractive visuals, titles, or price, despite weak query-item relevance. These limitations are further accentuated in Walmart's e-commerce sponsored search. User engagement on ad items is often structurally sparse because the frequency with which an ad is shown depends on factors beyond relevance such as whether the advertiser is currently running that ad, the outcome of the auction for available ad slots, bid competitiveness, and advertiser budget. Thus, even highly relevant query ad pairs can have limited engagement signals simply due to limited impressions. We propose a bi-encoder training framework for Walmart's sponsored search retrieval in e-commerce that uses semantic relevance as the primary supervision signal, with engagement used only as a preference signal among relevant items. Concretely, we construct a context-rich training target by combining 1. graded relevance labels from a cascade of cross-encoder teacher models, 2. a multichannel retrieval prior score derived from the rank positions and cross-channel agreement of retrieval systems running in production, and 3. user engagement applied only to semantically relevant items to refine preferences. Our approach outperforms the current production system in both offline evaluation and online AB tests, yielding consistent gains in average relevance and NDCG.

cs.IR↗

arXiv2026

Production probability of super-heavy nuclei in fusion

The synthesis of super-heavy nuclei (SHN) through fusion reactions is a critical area of nuclear physics, offering insights into nuclear stability and the limits of the periodic table. However, theoretical predictions of evaporation residue cross sections $ \sigma_{\rm {ER} }$ remain challenging due to large uncertainties arising from complex reaction mechanisms and sensitive model parameters. In this work, the average production probabilities of SHN with atomic number $Z\ge 110$ are systematically analyzed, based on barrier tunneling concept. Together with the empirical barrier distribution method for describing capture, a phenomenological formula, EBD3, is proposed, which reproduces 58 measured $ \sigma_{\rm {ER} }$ within one order of magnitude, with a root-mean-square deviation of 0.369. The formula successfully captures key quantities in fission-like process, including fission barrier height, mass asymmetry, depth of capture pocket and the effective fusion barrier height. Predictions for the synthesis of element 119 are presented, identifying promising projectile-target combinations such as $^{50}$Ti + $^{249}$Bk with a maximum cross section of $54.9_{-29.0}^{+61.3}$ fb at excitation energy of 36.5 MeV. The maximum cross section falls to $3.2_{-1.7}^{+3.6}$ fb for $^{54}$Cr + $^{243}$Am at the optimal incident energy of 244 MeV.

nucl-th↗

arXiv2026

TensorCircuit-NG: A Universal, Composable, and Scalable Platform for Quantum Computing and Quantum Simulation

We present TensorCircuit-NG, a next-generation quantum software platform designed to bridge the gap between quantum physics, artificial intelligence, and high-performance computing. Moving beyond the scope of traditional circuit simulators, TensorCircuit-NG establishes a unified, tensor-native programming paradigm where quantum circuits, tensor networks, and neural networks fuse into a single, end-to-end differentiable computational graph. Built upon industry-standard machine learning backends (JAX, TensorFlow, PyTorch), the framework introduces comprehensive capabilities for approximate circuit simulation, analog dynamics, fermion Gaussian states, qudit systems, and scalable noise modeling. To tackle the exponential complexity of deep quantum circuits, TensorCircuit-NG implements advanced distributed computing strategies, including automated data parallelism and model-parallel tensor network slicing. We validate these capabilities on GPU clusters, demonstrating a near-linear speedup in distributed variational quantum algorithms. TensorCircuit-NG enables flagship applications, including end-to-end QML for CIFAR-100 computer vision, efficient pipelines from quantum states to neural networks via classical shadows, and differentiable optimization of tensor network states for many-body physics.

quant-ph↗

arXiv2025

Fractional quantum anomalous Hall and anyon density-wave halo in a minimal interacting lattice model of twisted bilayer MoTe$_2$

The experimental discovery of fractional quantum anomalous Hall (FQAH) states in tunable moir\'e superlattices has sparked intense interest in exploring the interplay between topological order and symmetry breaking phases. In this paper, we present a comprehensive numerical study of this interplay through large-scale density matrix renormalization group (DMRG) simulations on a minimal two-band lattice model of twisted bilayer MoTe$_2$ at filling $\nu=-2/3$. We find robust FQAH ground states and provide clear numerical evidences for anyon excitations with fractional charge and pronounced real-space density modulations, directly supporting the recently proposed anyon density-wave halo picture. We also map out the displacement field dependent phase diagram, uncovering a rich landscape of charge ordered states emerging from the FQAH, including a quantum anomalous Hall crystal (QAHC) with an integer quantized Hall conductance. We expect our work to inspire further research interest of intertwined correlated topological phases in moir\'e systems.

cond-mat.str-el↗

arXiv2025

Fractionalized topological d+id superconductivity in the Yao-Lee-Kondo model

A conclusive experimental realization of 2D chiral topological superconductivity remains elusive. Here we present a theoretical demonstration that a topological $d+id$ fractionalized superconducting phase (SC*) can emerge in the weak-coupling limit of a Kondo lattice model, where conduction electrons interact with a Yao-Lee spin liquid on the honeycomb lattice (the Yao-Lee-Kondo model). Using a renormalization-group analysis, we show that exchanging Majorana spinons from the Yao-Lee spin liquid generates effective interactions among the conduction electrons and drives a Cooper instability even for arbitrarily weak Kondo coupling. We further find that the induced leading inter-orbital antiferromagnetic interaction selects topological $d+id$ spin-singlet pairing with Chern number $C=\pm 2$. Meanwhile, the Majorana fermions in the Yao-Lee spin liquid remain gapless and deconfined in this regime, so the resulting state is a fractionalized topological $d+id$ superconductor (SC*). For sufficiently strong Kondo coupling, the system instead enters a heavy Fermi liquid phase with fractionalization (HFL*).

cond-mat.str-el↗

arXiv2025

A "negative" route to pair density wave order

Pair density waves (PDW) are novel forms of superconducting states that exhibit periodically modulated pairing. A remaining challenge is to elucidate how intrinsic PDW order can emerge robustly in strongly correlated electrons. Here we propose that PDW is prone to form in strongly coupled multiband superconductors simply with interband Cooper pairing between electrons from oppositely dispersing bands. This scenario is heuristically motivated by the observation that uniform interband pairing in such systems would exhibit negative superfluid weight -- a signature of an instability towards pairing modulation, implying that PDW emerges naturally in the true ground state. Using large-scale density-matrix-renormalization-group calculations with finite-size scaling analysis, we demonstrate this PDW mechanism in a minimal model with strong interband attractions. Our simulations reveal power-law superconducting correlations characterized by incommensurate modulations. The exponent $K_{sc}$ of the power-law PDW correlation decreases systematically with increasing ladder width, confirming a genuine long-range PDW order in the 2D limit. Our study therefore demonstrates a promising route to robust PDW states in multiband systems.

cond-mat.supr-con↗

arXiv2025

Unraveling Deconfined Quantum Criticality in Non-Hermitian Easy-Plane $J$-$Q$ Model

Deconfined quantum critical point (DQCP) characterizes the continuous transition beyond Landau-Ginzburg-Wilson paradigm, occurring between two phases that exhibit distinct symmetry breaking. The debate over whether genuine DQCP exists in physical SU(2) spin systems or the transition is weakly first-order has persisted for many years. In this letter, we construct a non-Hermitian easy-plane $J$-$Q$ model and perform sign-problem-free quantum Monte Carlo (QMC) simulation to explore the impact of non-Hermitian microscopic interactions on the transition that potentially features a DQCP. Our results demonstrate that the intensity of the first-order transitions significantly diminishes with the amplification of non-Hermitian interactions, serving as numerical evidence to support the notion that the transition in $J$-$Q$ model is quasi-critical, possibly in the vicinity of the fixed point governing DQCP in the complex plane, described by a non-unitary conformal field theory (CFT). The non-Hermitian interaction facilitates the approach towards such a complex fixed point in the parameter regime. Furthermore, our QMC study on the non-Hermitian J-Q model opens a new route to numerically investigating the nature of complex CFT in the microscopic model.

cond-mat.str-el↗

arXiv2025

Sub-barrier fusion enhancement due to positive Q-value four-neutron transfer

The influence of positive $Q$-value four-neutron transfer (PQ4NT) effects on the sub-barrier capture cross sections is systematically investigated using the empirical barrier distribution (EBD2) method. For 13 fusion reactions with $Q_{4n}>0$, sustained neutron-pair transfer is found to reduce barrier heights and enhance capture cross sections at sub-barrier energies. In contrast, reactions such as $^{18}$O+$^{58}$Ni, which have $Q_{2n}>0$ but $Q_{4n}<0$, exhibit no enhancement due to the stalling of subsequent neutron-pair transfer after the initial 2n transfer. By incorporating PQ4NT effects into EBD2 for systems with $Q_{4n}>0$, the average deviation between predicted and experimental capture cross sections (113 datasets) is significantly reduced by $20\%$. Additionally, comparing with the systems induced by $^{48}$Ca ($Q_{4n}<0$), much larger neutron pickup probabilities are observed in the quasi-elastic scattering of $^{40}$Ca-induced reactions ($Q_{4n}>0$) from the time-dependent Hartree-Fock (TDHF) calculations.

nucl-th↗

arXiv2025

Emergence of charge-$4e$ superconductivity from 2D nematic superconductors

Charge-$4e$ superconductivity is an exotic state of matter that may emerge as a vestigial order from a charge-$2e$ superconductor with multicomponent superconducting order parameters. Showing its emergence in a lattice phase model from numerically exact large-scale computations has remained rare. Here, we propose a kagome lattice model with a nematic superconducting ground state and show that it supports a rich set of vestigial phases at elevated temperatures, including a charge-$4e$ phase and a quasi-long-range nematic phase, through large-scale Monte Carlo simulations. Combining theoretical analysis with Monte Carlo simulations, we uncover the nature of these phases and show that the phase transitions are governed by the proliferation of distinct topological defects: the $(\tfrac{1}{2},0)$ half superconducting vortices, the $(\tfrac{1}{2},\tfrac{1}{2})$ vortices, the $(0,1)$ integer nematic vortices, and domain-wall excitations. In particular, we demonstrate that domain-wall proliferation is crucial for the quasi-nematic phase and should be carefully accounted for when analyzing phase transitions associated with vestigial charge-$4e$ order.

cond-mat.supr-con↗