arXiv subjects

Xinwei Li

Publications and source records attributed to Xinwei Li.

At least 19 recordsLinked to original sources

arXiv2026

Mitigating sloppiness with intermediate controls in sequential multiparameter spin interferometry

Multiparameter quantum sensing requires different parameters to produce distinguishable probe responses while allowing their information to be extracted simultaneously. In sequential sensing without intermediate control, two phases encoded through the same generator are locally indistinguishable. Here we show that parameter-independent intermediate controls overcome this limitation in a collective-spin interferometer by separating the corresponding effective generators in the probe covariance geometry. Specifically, a transverse collective rotation provides an analytically tractable realization, and intermediate one-axis twisting (OAT) generates additional nonlinear operator directions and a broad regime of enhanced joint estimation. For fixed total particle number $N$ and matched twisting resource, both marginal precision bounds lie below those of a separated-OAT strategy while measurement incompatibility remains weak. For intermediate OAT, conditional optimization of an upper bound on the Holevo cost yields asymptotic $N^{-2}$ scaling. For this protocol, a fixed rotated-Dicke measurement retains simultaneous two-parameter enhancement over a broad phase region. These results show that nonlinear intermediate control can mitigate sloppiness while preserving quantum-enhanced precision with a common readout.

quant-ph↗

arXiv2026

Quantum-enhanced multiphase estimation with spin-1 Bose-Einstein condensates

We investigate quantum-enhanced two-phase estimation in a spin-$1$ Bose-Einstein condensate, where spin-exchange collisions generate a spin-nematic squeezed state in the $m_F=\pm1$ modes. The resulting pair-correlated state has a rank-one quantum Fisher information matrix (QFIM) for the two mode phases relative to the $m_F=0$ mode and therefore cannot resolve them independently. Nevertheless, we find that a parameter-independent passive three-mode transformation lifts this rank deficiency and enables joint estimation of both phases. In the undepleted-pump regime, we derive expressions for the QFIM, optimize the equal-weight multiparameter quantum Cramér-Rao bound, and obtain a full-rank probe with squeezing-enhanced sensitivity below the matched fixed-total-number standard quantum limit (SQL). These analytical predictions are confirmed by exact-diagonalization calculations. We further construct a parameter-independent readout that maps the phase combinations onto displacement channels accessible through atomic homodyne or parity measurements. Our results provide a route from spin-mixing-generated quantum correlations to two-phase estimation in spinor condensates.

cond-mat.quant-gas↗

arXiv2026

Vectorized Generalized Nearest Neighbor Decoding for In-block Memory Channel

This work extends the generalized nearest neighbor decoding (GNND), originally developed as a receiver architecture for memoryless channels, to a vectorized GNND (Vec-GNND) suitable for in-block memory (IBM) channels. Leveraging the generalized mutual information (GMI) as an operational lower bound on the mismatch capacity, an analytical characterization of the optimal Vec-GNND is obtained for general IBM channels with Gaussian codebooks. The formalism further provides closed-form optimality conditions and achievable GMIs for restricted variants of the receiver architecture. Furthermore, we formulate a GMI-based joint design viewpoint for Gaussian codebook covariance and decoding metrics. Since the metric optimization admits a closed-form solution for each fixed covariance, the joint design is reduced to an input-covariance optimization problem; for the diagonal covariance family, we derive first-order self-consistent optimality conditions. Numerical evaluations on block noncoherent AWGN and phase-noise channels demonstrate consistent GMI gains over conventional scaling-based GNND schemes, suggesting potential performance advantages of Vec-GNNDR in practical communication systems.

cs.IT↗

arXiv2026

Recovery-Directed Symbolic Distillation of Neural Likelihoods

Amortized neural likelihoods enable computationally expensive inference for models with analytically intractable or unspecified likelihoods, but their black-box nature limits interpretability. We introduce a symbolic distillation pipeline that converts trained neural likelihoods into explicit, interpretable expressions optimized for efficient parameter estimation. Our approach uses a recovery-directed objective to guide symbolic regression toward expressions that preserve parameter-recovery accuracy rather than merely approximating the likelihood function. Candidate expressions are evaluated on held-out datasets and selected using a criterion that jointly accounts for expression complexity, parameter-recovery performance, and distributional distance from the learned likelihood. We evaluate the pipeline on the diffusion decision model, a classical cognitive model, whose analytically tractable likelihood provides ground truth for controlled evaluation. The proposed recovery-directed objective improves parameter recovery over standard symbolic-regression objectives. The resulting symbolic likelihoods enable over 100 times faster parameter evaluation than both neural likelihoods and, when available, the exact likelihood, while maintaining a manageable loss in precision. We further demonstrate these computational benefits in Bayesian hierarchical inference on empirical data. Our pipeline provides a lightweight interface for integrating symbolic distillation with existing neural-likelihood estimation methods and can be adapted to a range of simulation-based inference settings.

cs.LG↗

arXiv2026

Observing Bell Inequality Violation Beyond the Qubit Bound in a Spinor Bose--Einstein Condensate

Correlations allowed by quantum mechanics can defy any classical explanation, with Bell nonlocality standing as their most profound and operationally powerful manifestation. While nonlocality has been demonstrated across a wide range of platforms, in many-body systems it has remained limited to ensembles of qubits, leaving the observation of higher-dimensional multipartite Bell correlations an open challenge. Here we report the observation of qutrit Bell correlations in a spin-1 $^{87}$Rb Bose-Einstein condensate via the violation of a Bell witness based on collective spin observables only. Exploiting spin-exchange collisions in an ensemble of $N \simeq 3.1 \times 10^{4}$ atoms, we generate spin-nematic squeezing of $-11.8(7)$ dB and observe a violation that surpasses the minimum bound achievable by a collection of $N$ qubits, providing direct evidence of genuine multipartite qutrit Bell correlations. Our results establish spinor Bose-Einstein condensates as a viable platform for investigating high-dimensional Bell correlations in the many-body regime, and demonstrate that coarse-grained collective measurements suffice to certify the dimensionality of quantum correlations at macroscopic scales.

quant-ph↗

arXiv2026

Deep adaptive design with an evidential bias criterion

Bayesian optimal experimental design (BOED) aims to collect informative data by optimizing an expected utility reflecting the goals of an experiment. However, this optimization is computationally challenging for common utilities and complex models. This is especially so for sequential or adaptive designs, where design and data collection alternate, so that feedback from already observed data must be taken into account. Most existing BOED research employs information gain as the utility, leading to the expected information gain (EIG) criterion. While EIG is widely useful, it may not always adequately reflect experimental goals. EIG can be viewed as rewarding experiments that produce large positive evidence for the truth on average, but it does not directly control the risk of an experiment producing misleading evidence. Here we consider an alternative criterion, which we call bias against (BA), that prioritizes such control. To address computational challenges when applying this criterion for adaptive design, we consider a policy-based deep adaptive design framework, which has previously been used for the EIG criterion. Minimizing a tractable upper bound on the BA objective is equivalent to maximizing a variance-penalized EIG criterion, and we optimize the latter by approximating it by Monte Carlo and learning design policies using stochastic gradient methods. The differences between BA and EIG designs are demonstrated in several examples including the adaptive design of a complex discrete choice experiment.

stat.ME↗

arXiv2026

MVFM-3DAD: Multi-view Flow Matching for 3D Anomaly Detection via Density Proxy Estimation

In 3D anomaly detection (3DAD), most existing methods rely on Memory bank retrieval or reconstruction. However, memory-based methods are constrained by the coverage of stored normal features, while reconstruction-based methods may learn identity shortcuts that also reconstruct anomalous inputs well. These limitations motivate a density-oriented approach that evaluates whether a test sample follows the learned normal distribution. To this end, we propose MVFM-3DAD, a flow-based framework that reframes 3DAD as density proxy estimation over the normal data distribution. MVFM-3DAD introduces a Bidirectional Geometric Projector (BGP), whose forward process converts irregular point clouds into structured multi-view representations. The Flow-guided Density Proxy Estimator (FDPE) estimates a reference density for each view feature, after which the backward process of BGP maps these multi-view density estimates to their corresponding 3D points. Building on it, anomalous features can be identified by their terminal normality. Unlike conventional flow-based likelihood estimation, our formulation requires neither input reconstruction nor explicit Jacobian evaluation, yielding a simple and efficient anomaly-scoring mechanism. Extensive experiments show that MVFM-3DAD outperforms the strongest competing methods on Real3D-AD and MVTec3D-AD. Code is available at https://github.com/lil-wayne-0319/MV3D-AD

cs.GR↗

arXiv2026

Ferroelectric brightening of spin forbidden dark excitons in a WSe2/hybrid perovskite heterostructure

Long-lived dark excitons in monolayer WSe2 present promising candidates for carrying spin and valley information, but their optical access and spin manipulation have conventionally required the use of strong external magnetic fields. Here, using a ferroelectric hybrid perovskite heterostructure, we leverage the ferroelectric proximity effect to break the WSe2's in-plane rotational symmetry and brighten the spin-forbidden dark excitons under zero magnetic field conditions. Furthermore, we show that the twist angle between the WSe2 and perovskite crystals controls the ferroelectric coupling strength and valley-contrasting polarization. Our proposed mechanism, supported by a four-band tight-binding model, suggests that the ferroelectric proximity effect induces an asymmetric intersublattice interaction, generating an effective in-plane spin-orbit coupling (SOC) field that rotates spin/valley polarization and brightens dark excitons. Our work establishes ferroelectric proximity coupling as an electrically reconfigurable, magnetic-field-free strategy for spin exciton control in two-dimensional semiconductors.

cond-mat.mtrl-sci↗

arXiv2026

Rotational anisotropy Raman spectrometer for high-sensitivity crystallographic symmetry analysis

Raman spectroscopy stands as a cornerstone technique for probing collective excitations and emergent quantum phases in solids. While polarization-resolved Raman scattering has been widely used to extract symmetry information of eigenmodes, its conventional geometry suffers from significant limitations: it accesses only a subset of Raman tensor elements, enforces π-periodic intensity patterns that obscure intrinsic crystalline symmetries, and lacks sensitivity to wavevector-dependent anisotropy. To overcome these constraints, here we introduce rotational-anisotropy Raman spectroscopy (RA-Raman). By measuring scattering intensity during full azimuthal rotation of the optical scattering plane at oblique incidence, this geometry enables complete reconstruction of the Raman tensor and reveals rich rotational anisotropy patterns essential for accessing subtle symmetry information elusive to conventional methods. We developed a prototype instrument to validate this approach experimentally. For centrosymmetric crystals, RA-Raman unambiguously identifies phonon symmetry representations and determines crystallographic axes. In noncentrosymmetric crystals, it resolves directional anisotropy and angular dispersion of phonon-polaritons, enabling quantitative determination of the Faust-Henry coefficient and the separation of deformation-potential and electro-optic scattering contributions. By demonstrating unprecedented symmetry-resolving power in standard benchmark crystals, we establish RA-Raman as a powerful tool with far-reaching potential to discover and characterize symmetry-breaking phases and topological excitations in quantum materials.

cond-mat.mtrl-sci↗

arXiv2025

Segmental Attention Decoding With Long Form Acoustic Encodings

We address the fundamental incompatibility of attention-based encoder-decoder (AED) models with long-form acoustic encodings. AED models trained on segmented utterances learn to encode absolute frame positions by exploiting limited acoustic context beyond segment boundaries, but fail to generalize when decoding long-form segments where these cues vanish. The model loses ability to order acoustic encodings due to permutation invariance of keys and values in cross-attention. We propose four modifications: (1) injecting explicit absolute positional encodings into cross-attention for each decoded segment, (2) long-form training with extended acoustic context to eliminate implicit absolute position encoding, (3) segment concatenation to cover diverse segmentations needed during training, and (4) semantic segmentation to align AED-decoded segments with training segments. We show these modifications close the accuracy gap between continuous and segmented acoustic encodings, enabling auto-regressive use of the attention decoder.

eess.AS↗

arXiv2025

Quantum Transport in Ultrahigh-Conductivity Carbon Nanotube Fibers

We investigate quantum transport in aligned carbon nanotube (CNT) fibers fabricated via solution spinning, focusing on the roles of structural dimensionality and quantum interference effects. The fibers exhibit metallic behavior at high temperatures, with conductivity increasing monotonically as the temperature decreases from room temperature to approximately 36 K. Below this temperature, the conductivity gradually decreases with further cooling, signaling the onset of quantum conductance corrections associated with localization effects. Magnetoconductance measurements in both parallel and perpendicular magnetic fields exhibit pronounced positive corrections at low temperatures, consistent with weak localization (WL). To determine the effective dimensionality of electron transport, we analyzed the data using WL models in 1D, 2D, and 3D geometries. We found that while the 2D model can reproduce the field dependence, it lacks physical meaning in the context of our fiber architecture and requires an unphysical scaling factor to match the experimental magnitude. By contrast, we developed a hybrid 3D+1D WL framework that quantitatively captures both the field and temperature dependences using realistic coherence lengths and a temperature-dependent crossover parameter. Although this combined model also employs a scaling factor for magnitude correction, it yields a satisfactory fit, reflecting the hierarchical structure of CNT fibers in which transport occurs through quasi-1D bundles embedded in a 3D network. Our results establish a physically grounded model of phase-coherent transport in macroscopic CNT assemblies, providing insights into enhancing conductivity for flexible, lightweight power transmission applications.

cond-mat.mes-hall↗

arXiv2025

Multisetting protocol for Bell correlated states detection with spin-$f$ systems

We propose a multisetting protocol for the detection of two-body Bell correlations, and apply it to spin-nematic squeezed states realized in $f$ pairs of SU(2) subsystems within spin-$f$ atomic Bose-Einstein condensates. Experimental data for $f=1$, alongside with numerical simulations using the truncated Wigner method for $f=1,\,2,\,3$, demonstrate the effectiveness of the proposed protocol. Our findings extend the reach of multisetting Bell tests in ultracold atomic system, paving the way for extended quantum information processing in high-spin ensemble platforms.

cond-mat.quant-gas↗

arXiv2025

Ultrafast cooperative electronic, structural, and magnetic switching in an altermagnet

Femtosecond laser control of antiferromagnetic order is a cornerstone for future memory and logic devices operating at terahertz clock rates. The advent of altermagnets -- antiferromagnets with unconventional spin-group symmetries -- creates new opportunities in this evolving field. Here, we demonstrate ultrafast laser-induced switching in altermagnetic $α$-MnTe that orchestrates the concerted dynamics of charge, lattice, and spin degrees of freedom. Time-resolved reflectivity and birefringence measurements reveal that the transient melting of spin order is accompanied by pronounced structural and electronic instabilities, as evidenced by phonon nonlinearity and accelerated band gap shrinkage. Theoretical modeling highlights the key roles of robust magnetic correlations and spin-charge coupling pathways intrinsic to this altermagnet.

cond-mat.str-el↗

arXiv2025

Joint Estimation of a Two-Phase Spin Rotation beyond Classical Limit

Quantum metrology employs entanglement to enhance measurement precision. The focus and progress so far have primarily centered on estimating a single parameter. In diverse application scenarios, the estimation of more than one single parameter is often required. Joint estimation of multiple parameters can benefit from additional advantages for further enhanced precision. Here we report quantum-enhanced measurement of simultaneous spin rotations around two orthogonal axes, making use of spin-nematic squeezing in an atomic Bose-Einstein condensate. Aided by the $F=2$ atomic ground hyperfine manifold coupled to the nematic-squeezed $F=1$ states as an auxiliary field through a sequence of microwave (MW) pulses, simultaneous measurement of multiple spin-1 observables is demonstrated, reaching an enhancement of 3.3 to 6.3 decibels (dB) beyond the classical limit over a wide range of rotation angles. Our work realizes the first enhanced multi-parameter estimation using entangled massive particles as a probe. The techniques developed and the protocols implemented also highlight the application of two-mode squeezed vacuum states in quantum-enhanced sensing of noncommuting spin rotations simultaneously.

quant-ph↗

arXiv2025

An Optimal Transport-Based Method for Computing LM Rate and Its Convergence Analysis

The mismatch capacity characterizes the highest information rate of the channel under a prescribed decoding metric and serves as a critical performance indicator in numerous practical communication scenarios. Compared to the commonly used Generalized Mutual Information (GMI), the Lower bound on the Mismatch capacity (LM rate) generally provides a tighter lower bound on the mismatch capacity. However, the efficient computation of the LM rate is significantly more challenging than that of the GMI, particularly as the size of the channel input alphabet increases. This growth in complexity renders standard numerical methods (e.g., interior point methods) computationally intensive and, in some cases, impractical. In this work, we reformulate the computation of the LM rate as a special instance of the optimal transport (OT) problem with an additional constraint. Building on this formulation, we develop a novel numerical algorithm based on the Sinkhorn algorithm, which is well known for its efficiency in solving entropy regularized optimization problems. We further provide the convergence analysis of the proposed algorithm, revealing that the algorithm has a sub-linear convergence rate. Numerical experiments demonstrate the feasibility and efficiency of the proposed algorithm for the computation of the LM rate.

cs.IT↗

arXiv2025

Time-hidden magnetic order in a multi-orbital Mott insulator

Photo-excited quantum materials can be driven into thermally inaccessible metastable states that exhibit structural, charge, spin, topological and superconducting orders. Metastable states typically emerge on timescales set by the intrinsic electronic and phononic energy scales, ranging from femtoseconds to picoseconds, and can persist for weeks. Therefore, studies have primarily focused on ultrafast or quasi-static limits, leaving the intermediate time window less explored. Here we reveal a metastable state with broken glide-plane symmetry in photo-doped Ca$_2$RuO$_4$ using time-resolved optical second-harmonic generation and birefringence measurements. We find that the metastable state appears long after intralayer antiferromagnetic order has melted and photo-carriers have recombined. Its properties are distinct from all known states in the equilibrium phase diagram and are consistent with intralayer ferromagnetic order. Furthermore, model Hamiltonian calculations reveal that a non-thermal trajectory to this state can be accessed via photo-doping. Our results expand the search space for out-of-equilibrium electronic matter to metastable states emerging at intermediate timescales.

cond-mat.str-el↗

arXiv2025

The Role of Excitatory Parvalbumin-positive Neurons in the Tectofugal Pathway of Pigeon (Columba livia) Hierarchical Visual Processing

The visual systems of birds and mammals exhibit remarkable organizational similarities: the dorsal ventricular ridge (DVR) demonstrates a columnar microcircuitry that parallels the cortical architecture observed in mammals. However, the specific neuronal subtypes involved and their functional roles in pigeon hierarchical visual processing remain unclear. This study investigates the role of excitatory parvalbumin (PV+) neurons within the Ento-MVL (entoallium-mesopallium venterolaterale) circuit of pigeons underlying hierarchical moving target recognition. Electrophysiological recordings and immunofluorescence staining reveal that excitatory PV+ neurons originating from the entopallial internal (Ei) predominantly modulate MVL responses to varying visual stimuli. Using a heterochronous-speed recurrent neural network (HS-RNN) model, we further validated these dynamics, replicating the rapid adaptation of the Ento-MVL circuit to moving visual targets. The findings suggest that the fast-spiking and excitatory properties of PV+ neurons enable rapid processing of motion-related information within the Ento-MVL circuit. Our results elucidate the functional role of excitatory PV+ neurons in hierarchical information processing under the columnar organization of the visual DVR and underscore the convergent neural processing strategies shared by avian and mammalian visual systems.

q-bio.NC↗

arXiv2025

Enhancing Dynamic Range of Sub-Quantum-Limit Measurements via Quantum Deamplification

Balancing high sensitivity with a broad dynamic range is a fundamental challenge in measurement science, as improving one often compromises the other. While traditional quantum metrology has prioritized enhancing local sensitivity, a large dynamic range is crucial for applications such as atomic clocks, where extended phase interrogation times contribute to wider phase range. In this Letter, we introduce a novel quantum deamplification mechanism that extends dynamic range at a minimal cost of sensitivity. Our approach uses two sequential spin-squeezing operations to generate and detect an entangled probe state, respectively. We demonstrate that the optimal quantum interferometer limit can be approached through two-axis counter-twisting dynamics. Further expansion of dynamic range is possible by using sequential quantum deamplification interspersed with phase encoding processes. Additionally, we show that robustness against detection noise can be enhanced by a hybrid sensing scheme that combines quantum deamplification with quantum amplification. Our protocol is within the reach of state-of-the-art atomic-molecular-optical platforms, offering a scalable, noise-resilient pathway for entanglement-enhanced metrology.

quant-ph↗