arXiv subjects

Xiao Xue

Publications and source records attributed to Xiao Xue.

At least 19 recordsLinked to original sources

arXiv2026

Exascale-ready HemeLB for large-eddy simulation of full-human arterial blood flow

Direct numerical simulation (DNS) of blood flow through the whole human arterial tree is limited by the number of lattice sites that must be advanced on a high-performance computer. We address this limit by combining the lattice Boltzmann solver HemeLB with Smagorinsky large-eddy simulation (LES). HemeLB runs with the same scaling behaviour on AMD, Intel and NVIDIA graphics processing units (GPUs) and sustains about 10 trillion lattice-site updates per second (TLUPS) on both the Frontier and Aurora exascale supercomputers. In stenotic aortic flow, LES on lattices five and ten times coarser than a 20~$μ$m DNS reproduces the wall pressure and the cross-sectional pressure and velocity, and its plane-averaged pressure differs from that of the DNS by at most 0.75~mmHg, below 1\% of the local pressure. At 100~$μ$m, LES reduces the estimated cost of a full-human simulation by a factor of about 625 relative to DNS. We use this resolution to compute blood flow from the head to the feet in a full-human arterial model with 295 million fluid sites, one inlet at the aortic root and 432 Windkessel outlets, at about 15--18 node-hours per cardiac cycle on two Frontier nodes. A single simulation resolves every region simultaneously, from the cerebral arteries to the arteries of the feet. The pressure pulse grows as it travels from the aorta to the feet, the shear stress concentrates in the branches rather than in the aorta, the pulse reaches the outlets later with increasing distance from the heart, and the flow in the legs and feet is triphasic, as in Doppler measurements of the human leg. These results bring patient-specific whole-body lattice Boltzmann simulations within reach of current supercomputers and pave the way for ensemble simulations towards a standard human model.

physics.flu-dyn↗

arXiv2026

Quantum advantage by designing beneficial dissipation from lossless dynamics

Passive loss removes probability from a system and therefore seems unable to improve an unconditional target. This intuition is exact for classical population dynamics under sink-only removal: passive killing obeys a finite-strength monotonicity principle even after optimization over the remaining controls. Here we show that coherent quantum dynamics can violate this classical monotonicity because coherent contributions to the final target can interfere destructively. We derive a trajectory-level criterion that determines when such a detrimental contribution is physically accessible to passive loss and accumulates into a net unconditional benefit. The criterion is encoded in a response operator constructed entirely from the lossless dynamics: its spectrum determines whether weak passive loss can help, while its principal eigenvector identifies the optimal environmental mode. Applying this criterion to vibronically assisted transfer, we identify a beneficial mode formed by coherently combining two individually detrimental local loss directions. The predicted mode remains near-optimal at finite loss, producing an $8.73\%$ enhancement over the independently optimized lossless benchmark, compared with $9.52\%$ under full finite-loss optimization. Our results establish how coherent quantum dynamics can evade classical passive-loss monotonicity and provide a general task-dependent principle for designing beneficial passive environments from lossless dynamics.

quant-ph↗

arXiv2026

Quantum Statistical Memory Advantage Reveals Predictive Structure in Chaotic Invariant Measures

Long-horizon prediction of a chaotic system is governed by fidelity to its invariant measure, and which parts of that measure a learned predictor needs is a physical question not known in advance. We establish a quantum statistical memory advantage for interrogating it. A quantum statistical prior (Q-Prior), trained once on classical data, stores an efficiently preparable $k$-point marginal in polynomially many circuit parameters against exponentially many for explicit tabulation. Collective Bell measurements on two copies then estimate any post hoc Pauli-expectation magnitude at a copy cost independent of register size, with a worst-case exponential separation from single-copy protocols. A single Bell dataset supports an entire family of candidate observables at a cost growing only logarithmically in the family size, so an exponentially large candidate space stays open for later analysis. We implement the protocol on IQM superconducting processors with two-copy registers of up to 54 physical-qubit chips. We apply this to turbulent channel flow and ERA5 forecasting, resolving invariant structure by statistical sector and order. Turbulent phase correlations persist through eighth order, while most predictive gains arise from low-order constraints; in ERA5, planetary-wave phase coherence complements covariance regularisation, identifying distinct low-order sectors with predictive value. Quantum statistical memory is therefore both a near-term computational resource and an instrument for identifying which invariant structures matter for prediction.

quant-ph↗

arXiv2026

Reduced Subgrid-Scale Terms for Turbulence Simulations with the Lattice Boltzmann Method

Turbulent flows span a wide range of scales, making direct numerical simulation (DNS) prohibitively expensive at high Reynolds numbers. Large eddy simulation (LES) offers a pragmatic alternative by only resolving the large-scale motions, but its accuracy hinges on the subgrid-scale (SGS) model. We introduce the Tau-orthogonal (TO) framework to the lattice Boltzmann method (LBM) using a macroscopic-velocity forcing. The TO method shifts the modeling target from a high-dimensional SGS field to the time dynamics of a small set of spatially integrated quantities of interest (QoIs). Training data are generated by a predictor-corrector tracking procedure that nudges coarse simulations toward reference QoI trajectories. Lightweight linear regression models with stochastic, multivariate Gaussian residuals (LRS) then drive the LES autonomously. Compact convolution-based kernels replace the sharp Fourier filters of the original formulation, lifting its restriction to rectangular periodic domains. We first evaluate the closure on two-dimensional Kolmogorov flow, where TO-LRS yields stable, statistically accurate online LES. However, bounded high-frequency oscillations are observed in the solution fields. The decisive test is three-dimensional turbulent channel flow, simulated on a grid five times coarser in every direction than the high-fidelity reference. Here the plain BGK solver is unstable, and even calibrated Smagorinsky and WALE models over-dissipate the near-wall flow. TO-LRS, applied on top of an eddy-viscosity substrate and with the QoI set augmented by mean-profile QoIs, reproduces the reference distributions and holds the DNS mean velocity profile, at $40\times$ lower online cost per simulated time unit than the high-fidelity simulation. These results establish reduced, QoI-based closures as an interpretable and inexpensive alternative to high-dimensional neural SGS models for LBM.

physics.flu-dyn↗

arXiv2026

Beyond Driving: Envisioning Activities in Future Autonomous Vehicles through Experience-Centered Design

Autonomous vehicles (AVs) are poised to fundamentally alter personal transportation, offering occupants the freedom to engage in various non-driving-related activities (NDRAs). However, our current understanding of how people might actually use this time in fully autonomous vehicles (FAVs) is limited. Traditional research methods often struggle to capture the influence of diverse travel circumstances and purposes when exploring future scenarios. This paper introduces experience-centered design (ECD) as an approach to investigate potential NDRAs within FAVs by examining the intricate connections between individuals' daily routines, specific travel contexts, and the activities they might undertake in transit. Through a multi-phase study employing participatory techniques that facilitated narrative construction and exploration, including diary studies, scenario scripting, and mixed reality (MR) enactments, we enabled participants to ground speculative future scenarios in their own lived experiences. This process yielded nuanced insights into preferences and behaviors concerning potential NDRAs, alongside the underlying subjective meanings and sociotechnical considerations. Our findings lead us to conceptualize NDRAs not as isolated instances of "travel time use," but as dynamic sequences of interrelated activities deeply shaped by pre- and post-journey contexts. The effectiveness of our ECD approach in bridging current lived experiences with future scenarios was crucial for uncovering these insights. Ultimately, this study reconceptualizes AVs as complex sociotechnical systems that actively mediate human activity and interaction, suggesting a fundamental shift in their role within the urban fabric.

cs.HC↗

arXiv2026

Discovering $μ$Hz gravitational waves and ultra-light dark matter with binary resonances

In the presence of a weak gravitational wave (GW) background, astrophysical binary systems act as high-quality resonators, with efficient transfer of energy and momentum between the orbit and a harmonic GW leading to potentially detectable orbital perturbations. In this work, we develop and apply a novel modeling and analysis framework that describes the imprints of GWs on binary systems in a fully time-resolved manner to study the sensitivity of lunar laser ranging, satellite laser ranging, and pulsar timing to both resonant and nonresonant GW backgrounds. We demonstrate that optimal data collection, modeling, and analysis lead to projected sensitivities which are orders of magnitude better than previously appreciated possible, opening up a new possibility for probing the physics-rich but notoriously challenging to access $μ\mathrm{Hz}$ frequency GWs. We also discuss improved prospects for the detection of the stochastic fluctuations of ultra-light dark matter, which may analogously perturb the binary orbits.

astro-ph.CO↗

arXiv2026

Prospects for gravitational wave and ultra-light dark matter detection with binary resonances beyond the secular approximation

Precision observations of orbital systems have recently emerged as a promising new means of detecting gravitational waves and ultra-light dark matter, offering sensitivity in new regimes with significant discovery potential. These searches rely critically on precise modeling of the dynamical effects of these signals on the observed system; however, previous analyses have mainly only relied on the secularly-averaged part of the response. We introduce here a fundamentally different approach that allows for a fully time-resolved description of the effects of oscillatory metric perturbations on orbital dynamics. We find that gravitational waves and ultra-light dark matter can induce large oscillations in the orbital parameters of realistic binaries, enhancing the sensitivity to such signals by orders of magnitude compared to previous estimates.

gr-qc↗

arXiv2026

Synchronizing Spectral and Interference Criticalities at an Exceptional Point

Exceptional points (EPs) are spectral singularities, but whether they can be used to control a distinct criticality of coherent interference remains largely unexplored. Here we show in nonlocal superconducting transport how spectral and interference criticalities can be controllably synchronized. In a minimal superconducting double-quantum-dot system, loss imbalance selectively drives one Bogoliubov sector through an EP. The spectral EP and the CAR-ECT interference criticality form distinct structures in control space whose relative ordering can be continuously tuned. At their synchronization point, the interference boundaries between crossed Andreev reflection (CAR) and elastic cotunneling (ECT) coalesce at the EP and bifurcate beyond it, creating a finite CAR-dominated window. The resulting interference window opens with a square-root critical law, whose exponent also governs the finite-temperature observability scale. Detuning unfolds the two criticalities, while retuning restores their intersection, establishing the synchronization as controlled rather than generic. The bifurcation is directly observable in nonlocal conductance. Our results establish exceptional points as spectral control points for independently defined interference criticalities, opening a route to non-Hermitian control of critical quantum interference.

cond-mat.mes-hall↗

arXiv2026

A New Probe of Dark Matter Subhalos: Stellar Aberration with TESS

Small-scale dark matter (DM) structure encodes key information about the particle nature of DM and therefore provides a sensitive test of competing models. Yet, it remains hidden from electromagnetic surveys and is instead inferred through its gravitational effects. Stellar aberration, the apparent shift in a light source's position induced by the observer's motion, offers a largely unexplored channel to access such signatures. DM subhalos can perturb the observer's motion, imprinting characteristic, spatially correlated shifts in stellar positions across the sky. We show that the Transiting Exoplanet Survey Satellite (TESS), with its long temporal baseline, wide sky coverage, and high-cadence observations, is well suited to search for these aberration signals. We derive Fisher-matrix-based sensitivity estimates for constant observer accelerations, forecasting a sensitivity down to $6.3\times 10^{-9}\,\mathrm{m/s^2}$ from the combined sample of TESS stars with magnitude $\mathrm{Tmag}\leq 10$. This sensitivity allows TESS to probe concentrated DM subhalos over a broad parameter space, from $\gtrsim 10^{-6}\,\mathrm{M_{\odot}}$ at AU-scale distances to $\gtrsim 10^{7}\,\mathrm{M_{\odot}}$ at $\mathcal{O}(10\,\mathrm{pc})$. TESS's sector-based observing strategy further provides intrinsic temporal resolution of potential DM-induced aberration signals. Moreover, we briefly discuss challenges for future data analysis, including the modeling of instrumental systematics and stellar astrometric foregrounds, such as parallax and proper motion. Our results establish stellar aberration as a novel probe of DM substructure, paving the way for dedicated searches in TESS and next-generation wide-field surveys.

astro-ph.CO↗

arXiv2026

Controllable interaction between photons and distant spins via vacuum Rabi oscillations

Vacuum Rabi oscillations between a single photon and a single spin demonstrate the capability of harnessing light-matter interaction at the level of a single quantum of energy. Since the observation of strong spin-photon coupling in gate-defined quantum dots, probing this interaction in the time-domain has been a major objective. Here, we carefully engineer a device composed of two spatially separated double quantum dots hosting single electron spin qubits and a superconducting cavity to accommodate microwave photons. We observe multiple vacuum Rabi oscillations between each spin qubit and the cavity. By concatenating vacuum Rabi oscillations involving the two spins, an energy excitation in one qubit can be emitted as a photon and then transferred to the other qubit. When a single photon is emitted, the cavity is prepared in a Fock state, leading to an accelerated vacuum Rabi frequency. These results serve as building blocks not only in exploring light-matter interactions, but also in interfacing semiconductor spin qubits to photonic links.

cond-mat.mes-hall↗

arXiv2026

Autoregressive One-Step Generative Modeling for Dynamical System Forecasting

Fast surrogate modeling for high-dimensional physical dynamics requires more than low short-term error: useful models must roll out efficiently while preserving the statistical structure of long trajectories. Neural operators provide inexpensive autoregressive forecasts but can drift in turbulent regimes, whereas rolling diffusion and latent generative surrogates can represent stochastic transitions at the cost of multi-step denoising, noise-schedule design, or auxiliary compression models. We propose MeanFlow Long-term Invariant Spatiotemporal Consistency Autoregressive Models (MeLISA), a latent-free autoregressive generative surrogate built on pixel-space MeanFlow. MeLISA defines a blockwise stochastic transition kernel that generates each forecast block with a single model evaluation, avoiding latent encoders and iterative diffusion solvers at inference time. To stabilize long-horizon rollouts, MeLISA combines a Window-Consistency MeanFlow objective that learns conditional spatiotemporal generation from partially observed temporal windows with a Time Increment Consistency loss that constrains multi-lag finite increments and targets temporal-correlation structure. We evaluate MeLISA with compact UNet and scalable DiT backbones on two high-resolution benchmarks, extended 2D Kolmogorov flow at $256 \times 256$ and turbulent channel-flow slice at $192 \times 192$. MeLISA outperforms neural-operator baselines on short-term forecasting accuracy and long-horizon statistical metrics, including energy spectra, turbulent kinetic energy, and mixing-rate-related dynamics, while achieving inference speeds comparable to, and in some cases faster than, neural operators. To our knowledge, this is the first method for high-resolution one-step generation for physical dynamical systems with performance comparable to state-of-the-art deterministic surrogates.

cs.LG↗

arXiv2026

Probing Fundamental Constant Oscillation in the Galactic Center with S-Star Spectroscopy

Astrophysical spectroscopy provides a powerful probe of spacetime variations of fundamental constants, as atomic and ionic emission and absorption lines depend sensitively on the fine-structure constant. In particular, coherent temporal oscillations induced by an ultralight scalar background produce characteristic, time-resolved signatures that can be robustly disentangled from intrinsic variability. In the Galactic Center, such scalar backgrounds can be substantially enhanced, either through the formation of dense scalar clouds powered by black hole rotational energy extraction or as ultralight scalar dark matter forming a soliton-like core. These scalar configurations generically induce oscillations of the fine-structure constant, with periods set by the scalar mass and spatial profiles determined by the scalar wavefunction and its coupling to the electromagnetic sector. We show that precise, time-resolved spectroscopy of S-stars orbiting the supermassive black hole Sgr A$^*$ provides a sensitive test of these effects, enabling constraints on quadratic scalar-photon couplings in the exceptionally high boson-density environment of the Galactic Center.

hep-ph↗

arXiv2026

Explainable quantum-compressed machine learning for complex fluid flows

Machine-learning surrogates of physical systems face a paradox: explainable models facing the challenge of expressivity to capture complex nonlinear flows, whereas expressive deep surrogates match high-fidelity simulations only through massive parameterisations that turn the learned dynamics into a black box. Here, we introduce quantum-compressed machine learning (QCML), which resolves this tension by compressing the latent propagator of a flow surrogate from $524{,}288$ trainable parameters to no more than $8$. This parameter reduction brings the learned dynamical law to the parameter scale of a physical constitutive relation rather than a black-box neural network, making the surrogate directly interpretable and controllable without sacrificing expressivity. The compression is realised by a structured quantum circuit whose unitary propagator constrains the latent spectrum to the unit circle exactly and by construction, replacing exponential error growth with linear accumulation over autoregressive rollouts. Classical regularisation only approximates this constraint: even a quantum-inspired classical baseline penalised towards unitarity collapses within one Lyapunov time on turbulent channel flow, whereas QCML remains stable over the full rollout. Shared phase and coupling angles parameterising the circuit correspond directly to modal frequencies and inter-mode interactions, giving the learned dynamics a physical interpretation in spectral space. On two patient-specific cardiovascular benchmarks, the structured QCML propagator matches the predictive accuracy of its classical counterpart on surface pressure spectra, pressure drop, and wall shear stress. These results establish QCML as a working component of scientific machine learning and a concrete contribution towards practical quantum advantage in real-world prediction.

physics.flu-dyn↗

arXiv2026

GUEST: Gravitational Universe Exploration with Satellite Tracking. A passive satellite laser-ranging mission for the dark gravitational Universe

GUEST is a space mission concept whose central objective is the detection of gravitational waves (GWs) in the microhertz band -- a physics-rich frequency window that no other present or planned detector can reach at a significant level. The concept is simple: two dense, passive spheres, covered with cube-corner retroreflectors, deployed in {highly eccentric} Earth orbits ($e \gtrsim 0.7$, period $P \gtrsim 33$ h), tracked continuously by the global network of satellite laser-ranging stations over a minimum observation time of 10 years, with an expected total duration of 30 years. The orbits themselves act as resonant detectors of the oscillating gravitational perturbations, with the microhertz sensitivity emerging from the selected orbital parameters. From the same data stream, GUEST delivers a programme of fundamental and applied science that cuts across particle physics, gravitational-wave astronomy, cosmology, astrophysics, and geodesy: the first coherent search for GWs from supermassive black-hole binaries in the $μ$Hz band, the exploration of primordial GW backgrounds in the unexplored energy-scale gap between pulsar-timing arrays and LISA, a dedicated probe of ultra-light dark matter in a parameter region untouched by any other experiment, a new way to search for ultra-light bosons, order-of-magnitude-improved tests of new gravitational interactions at astronomical ranges, and a step change in the absolute determination of $GM_\oplus$ that underpins the Global Geodetic Observing System and future navigation and Earth-observation missions. This white paper presents the motivation, scientific reach, and mission concept of GUEST.

astro-ph.CO↗

arXiv2026

Population statistics of nanohertz gravitational wave sources

The recent detection of a nanohertz gravitational wave (GW) background by pulsar timing arrays (PTA) has sparked extensive discussions regarding its origin-whether it arises from astrophysical supermassive black hole binaries (SMBHBs) or from primordial GWs generated by various early universe processes. Previous studies suggest that a key discriminant between these two origins is the non-Gaussianity of the GW background prior to the detection of any individual source. In this Letter, we introduce a hierarchical Bayesian inference framework for inferring population properties of GW sources. This approach enables not only the measurement of evidence for different GW origins using PTA data but also the inference of population properties of astrophysical SMBHBs, by optimally leveraging non-Gaussian information in individual bright sources and in power spectrum fluctuations of the GW background.

astro-ph.HE↗

arXiv2026

The SKAO Pulsar Timing Array

Pulsar timing arrays (PTAs) are ensembles of millisecond pulsars observed for years to decades. The primary goal of PTAs is to study gravitational-wave astronomy at nanohertz frequencies, with secondary goals of undertaking other fundamental tests of physics and astronomy. Recently, compelling evidence has emerged in established PTA experiments for the presence of a gravitational-wave background. To accelerate a confident detection of such a signal and then study gravitational-wave emitting sources, it is necessary to observe a larger number of millisecond pulsars to greater timing precision. The SKAO telescopes, which will be a factor of three to four greater in sensitivity compared to any other southern hemisphere facility, are poised to make such an impact. In this chapter, we motivate an SKAO pulsar timing array (SKAO PTA) experiment. We discuss the classes of gravitational waves present in PTA observations and how an SKAO PTA can detect and study them. We then describe the sources that can produce these signals. We discuss the astrophysical noise sources that must be mitigated to undertake the most sensitive searches. We then describe a realistic PTA experiment implemented with the SKA and place it in context alongside other PTA experiments likely ongoing in the 2030s. We describe the techniques necessary to search for gravitational waves in the SKAO PTA and motivate how very long baseline interferometry can improve the sensitivity of an SKAO PTA. The SKAO PTA will provide a view of the Universe complementary to those of the other large facilities of the 2030s.

astro-ph.IM↗

arXiv2026

MENO: MeanFlow-Enhanced Neural Operators for Dynamical Systems

Neural operators have emerged as powerful surrogates for dynamical systems due to their grid-invariant properties and computational efficiency. However, Fourier-based variants inherently truncate high-frequency components in spectral space, resulting in the loss of small-scale structures and degraded prediction quality at high resolutions when trained on low-resolution data. While diffusion-based enhancement methods can recover multi-scale features, they introduce substantial inference overhead that undermines the efficiency advantage of neural operators. In this work, we introduce MeanFlow-Enhanced Neural Operators (MENO), a novel framework that achieves accurate all-scale predictions with minimal inference cost. By leveraging the improved MeanFlow method, MENO restores both small-scale details and large-scale dynamics with superior physical fidelity and statistical accuracy. We evaluate MENO on three challenging dynamical systems, including phase-field dynamics, 2D Kolmogorov flow, and active matter dynamics, at resolutions up to 256$\times$256. Across all benchmarks, MENO improves the power spectrum density accuracy by up to a factor of 2 compared to baseline neural operators while achieving up to $14\times$ faster inference than the state-of-the-art Denoising Diffusion Implicit Model (DDIM)-enhanced counterparts, effectively bridging the gap between accuracy and efficiency. The flexibility and efficiency of MENO position it as an efficient surrogate model for scientific machine learning applications where both statistical integrity and computational efficiency are paramount.

cs.LG↗

arXiv2026

The MeerKAT Thousand-Pulsar Polarisation Array II: Searches for Ultralight Axion-Like Dark Matter

We construct Pulsar Polarisation Arrays (PPA), using regular pulsars monitored in MeerKAT's Thousand Pulsar Array (TPA) Programme, to search for Axion-like Dark Matter (ALDM) within Milky Way. Specifically, from a catalogue of 1237 regular pulsars, we select the 50 ones with the highest signal-to-noise ratio and set upper limits on the ALDM Chern-Simons coupling. We find no signals with statistical significance over the mass range of $[10^{-23},10^{-20}]\,{\rm eV}$ in the six-year MeerKAT's data. By combining the high-quality TPA pulsars and the accurate ionospheric subtraction of spinifex, we establish the most sensitive upper limits to the date on the ALDM Chern-Simons coupling, namely $\lesssim 10^{-14} - 3\times 10^{-13}\,{\rm GeV}^{-1}$, for the mass range of $[10^{-23},10^{-21}]\,{\rm eV}$ except at $m_a \sim 1.3 \times 10^{-22}\,$eV. This study underscores the great potential of constructing regular-pulsar PPAs for scientific tasks.

astro-ph.HE↗