arXiv subjects

Tao Xiang

Publications and source records attributed to Tao Xiang.

At least 19 recordsLinked to original sources

arXiv2026

LaQX: A JAX Toolkit for Neural Quantum State Simulations of Lattice Quantum Many-Body Models

Neural quantum states provide accurate variational representations for interacting lattice fermions and spins. Turning a new ansatz into a reproducible calculation, however, requires coordinated implementations of the Hamiltonian, sampling, optimization, observables, and accelerator parallelism. We present LaQX (Lattice Quantum simulation based on jaX), an open-source neural quantum state library based on JAX that organizes these components through common functional interfaces. Beyond ground-state variational Monte Carlo, LaQX supports excited-state calculations, Green's Function Monte Carlo refinement, adiabatic evolution, and real-time evolution with time-dependent variational Monte Carlo. It consolidates methods developed in recent high-accuracy neural quantum state studies of the Hubbard model and frustrated magnets, and the package has been used to investigate the triangular-lattice Hofstadter-Hubbard model. We document these capabilities with reproducible calculations for Hubbard, $J_1$-$J_2$ Heisenberg, altermagnetic Hubbard, and interacting Hofstadter models. Function-oriented modules separate the scientific components and limit the source context needed for extensions by human developers and large language models. Source code, documentation, and executable examples are available at https://github.com/bytedance/laqx.

cond-mat.str-el↗

arXiv2026

Charge Pumping Leptogenesis from the Symmetric Mass Generation Phase on a Four-Leg Ladder Lattice

Quantized charge pumping in quantum Hall systems is a bulk topological property, typically manifesting on gapless boundaries as spectral flow through chiral edge modes upon flux insertion. When the boundary is anomaly-free, it can be symmetrically gapped by symmetric mass generation (SMG) interactions. However, the effects on quantized charge pumping at an interacting gapped boundary are unclear. We address this using density-matrix renormalization group simulations of a four-leg ladder quasi-(2+1)d realization of the (1+1)d 3-4-5-0 lattice chiral fermion model with six-fermion interactions at one edge. We find gapless modes near the interacting edge for both weak and strong couplings, but the edge can be in a fully gapped SMG phase at intermediate coupling. We also identify spontaneous breaking of the model's $\mathbb{Z}_2$ symmetry above a critical interaction strength. Across all three boundary phases, we find quantized charge pumping with the same pumped charge per flux quantum. The spatial distribution of the pumped charge varies between phases. Besides charge pumping, we study how a single injected fermion propagates under open boundary conditions. In the SMG phase, an interaction-induced flavor-mixing current develops at the endpoints of the gapped edge, which is absent in the weak- and strong-coupling limits. This current reveals a charge-redistribution mechanism unique to the SMG phase. Additionally, we propose an analogy between charge pumping in this model and a topological leptogenesis scenario in the (3+1)d Standard Model. The anomaly-free chiral U(1) symmetries of the 3-4-5-0 model play a role analogous to the baryon-minus-lepton symmetry of the Standard Model. In both settings, charge pumping can be associated with a continuous deformation along a classically defined but quantum-anomalous direction, while a discrete subgroup remains an exact quantum symmetry.

cond-mat.str-el↗

arXiv2026

Fast and accurate neural triage decoding of ultra-high-rate quantum LDPC codes

Quantum error correction is essential for fault-tolerant quantum computing. High-rate quantum low-density parity-check (qLDPC) codes have emerged as a promising approach to reducing the substantial physical qubit overhead of quantum error correction, enabling encoding rates exceeding $1/2$ in blocks of approximately one thousand physical data qubits. However, their practical implementation requires decoders that combine high accuracy with low latency to keep pace with quantum hardware. Existing decoders of high-rate qLDPC codes can achieve high accuracy but often incur substantial computational latency, whereas neural network decoders have yet to be demonstrated for high-rate qLDPC codes. Here, we introduce \emph{neural triage decoding}, a framework that uses syndrome consistency to adaptively allocate neural computation. A local neural message-passing network rapidly predicts error corrections, additional iterations reduce residual syndrome violations, and a rarely invoked diversification stage searches for consistent corrections to the remaining shots. We demonstrate that the \texttt{Triage} decoder can decode ultra-high-rate qLDPC codes encoding a few hundred logical qubits, including lifted-product and Kasai codes, achieving state-of-the-art accuracy comparable to the near optimal algorithmic decoders while reducing decoding latency by approximately one to two orders of magnitude. Our framework is inspired by a rigorous analysis of the exact bitwise marginal decoder, revealing a distance-independent syndrome-inconsistent error floor. In addition, we show the robustness of neural network decoders to mismatched error priors. Our results establish adaptive neural decoding as a promising route towards fast, accurate, and scalable quantum error correction in the near term.

quant-ph↗

arXiv2026

Giant bubbles of Fisher zeros in the quantum XY chain

We demonstrate an alternative approach based on a complex-valued inverse temperature and partition function to probe quantum phases of matter with nontrivial spectra and dynamics. It leverages thermofield dynamics (TFD) to quantitatively characterize quantum and thermal fluctuations, and exploit the correspondence between low-energy excitations and Fisher zeros. Using the quantum XY chain in an external field as a testbed, we show that the oscillatory gap behavior manifests as oscillations in the long-time dynamics of the TFD spectral form factor. We also identify giant bubbles, i.e. large-scale closed lines, of Fisher zeros near the gapless XX limit. They provide a characteristic energy scale that seems to contradict the predictions of the low energy theory of a featureless Luttinger liquid. We identify this energy scale and relate the motion of these giant bubbles with varying external field to the transfer of spectral weight from high to low energies. The deep connection between Fisher zeros, dynamics, and excitations opens up promising avenues for understanding the unconventional gap behaviors in strongly correlated many-body systems.

cond-mat.str-el↗

arXiv2026

Computational and experimental study of multi-grain-size sediment transport and sorting process

Successful prediction of the interaction between turbulent flows and river ecosystems is essential for flood management, development of mitigation strategies, and infrastructure protection. Natural riverbeds consist of bed materials of multiple grain sizes that interact with turbulence differently, leading to spatial classification of particle sizes. Such particle sorting processes can only be captured by models capable of handling multiple grain sizes. We present a numerical framework that couples large-eddy simulation with a multi-grain-size sediment transport module to track multiple grain size classes, their local fractions within the bed-load layer, and bed evolution. A laboratory experiment was conducted in a flume with multi-size bed material to examine sediment particle redistribution and sorting. Measured sediment grain size distributions and bed elevations were used to validate the numerical framework. The results of the multi-grain-size sediment transport model for bed deformation were then compared against those of a uniform sediment transport model, demonstrating that the former reduces the prediction errors relative to the experimental measurements from 37.98 to 3.83 percent for maximum scour depth and from 48.35 to 11.54 percent for maximum deposition height. The validated numerical framework was also applied to large-scale meandering rivers with multi-grain bed material, simulating their bed deformation and sediment sorting. The simulation results revealed the formation of particle sorting patterns in meandering rivers, with coarser and finer sediment particles accumulating near the erosional depression (scour) and depositional (point bar) regions, respectively. These findings support the coupled framework's ability to predict sediment particle sorting and highlight the importance of representing bed material heterogeneity in numerical modeling of meandering rivers.

physics.flu-dyn↗

arXiv2026

Pressure-induced decoupling of the pseudogap and superconductivity in a bilayer cuprate

The physical origin of the pseudogap in cuprate superconductors, whether a precursor to superconductivity or a distinct intertwined order, remains an open question. Although hydrostatic pressure provides a clean tuning parameter, probing the relevant energy scales under compression has remained experimentally elusive. Here, we employ ultrafast optical spectroscopy to track the non-equilibrium quasiparticle dynamics of slightly underdoped Bi$_2$Sr$_2$CaCu$_2$O$_{8+δ}$ up to 37 GPa. By disentangling the pseudogap and superconducting relaxation channels in the time domain, we uncover a contrasting pressure evolution: despite an increase in carrier density, the pseudogap thermal onset (T$^*$) rises monotonically, whereas its energy gap ($Δ_{PG}$) is continuously suppressed. Conversely, the critical temperature (T$_C$) and the superconducting gap ($Δ_{SC}$) exhibit a synchronized dome-like trajectory. The effective coupling ratio 2$Δ_{SC}$/k$_B$T$_C$ collapses abruptly from ~10 toward the weak-coupling d-wave limit, signifying a crossover from a phase-fluctuating two-dimensional regime to a globally coherent three-dimensional state. Upon compression to 37 GPa, the superconducting condensate is completely quenched into an insulating-like state, yet the pseudogap persists. The extended phase diagram reveals an unambiguous decoupling between the pseudogap and superconductivity, imposing stringent constraints on microscopic theories of high-temperature superconductivity.

cond-mat.supr-con↗

arXiv2026

Pressure-induced Lifshitz and quantum phase transitions in electron-doped cuprate superconductor

We report the first observations of a pressure-induced Lifshitz transition coupled with a quantum phase transition in electron-doped cuprate superconductor Pr0.87LaCe0.13CuO4 (PLCCO), by combining high-pressure electrical resistance, Hall coefficient and synchrotron X-ray diffraction (XRD) measurements at low temperatures. Our low-temperature Hall coefficient measurements reveal that the Hall coefficient decreases continuously and reaches zero at about 10 GPa (critical pressure of Pc1). Upon further compression beyond Pc1, Hall coefficient unexpectedly changes its sign from negative to positive, signaling a reconstruction of the Fermi surface from electron-dominated to hole-dominated topology. Concurrently, the superconducting transition temperature (Tc) exhibits a monotonic suppression, vanishing completely at 17.6 GPa (critical pressure of Pc2), where the system enters a non-superconducting metallic state. Our low temperature XRD measurements unequivocally demonstrate the absence of any structural phase transition across Pc1 and Pc2. Therefore, the sign change in RH at Pc1 is associated with a Lifshitz transition, which is never found in the compressed bulk electron- or hole-doped cuprate superconductors. Moreover, the quantum phase transition observed at Pc2 contrasts sharply with known high-pressure behavior of hole-doped cuprates, uncovering a fundamental difference on how pressure tunes the ground states of electron- versus hole-doped systems. These findings provide crucial insights into the different pressure responses on the interplay among Fermi surface topology, electronic correlations, and superconductivity between these two kinds of cuprate superconductors.

cond-mat.supr-con↗

arXiv2026

Native Action-Prior Learning from Videos for World Action Models

World action models integrate future visual dynamics with robot action prediction, but their scalability remains limited by the need for action-annotated robot trajectories. Observation-only videos contain rich evidence about interaction dynamics, but existing approaches typically use them either to pretrain visual representations that must later be adapted for control, or to infer latent actions that are subsequently grounded to robot commands. We present NAVA-WAM, which introduces native action-prior learning by directly pretraining the action policy from observation-only videos, avoiding indirect representation-to-control transfer or a separate latent-action model. Our training consists of two stages. First, we pretrain on observation-only videos, where future-video flow-matching supervision over visual transitions is propagated through transition-structured joint attention to optimize the Action-DiT and learn action-relevant priors. Second, we use action-labeled demonstrations to post-train the Action-DiT for robot control through joint video--action flow matching, while asymmetric attention decouples the visual branch from iterative action denoising and enables efficient action-only inference. Extensive experiments show that NAVA-WAM consistently outperforms prior approaches under both in-distribution and out-of-distribution settings, while demonstrating strong action-label efficiency and effective real-robot generalization. These results establish native action-prior learning as an effective approach to directly pretrain action policies from observation-only videos, providing a scalable path beyond action-labeled robot data.

cs.CV↗

arXiv2026

Selective suppression of electronic orders via interlayer coupling in superconducting bilayer nickelate thin films

The discovery of spin-density-wave (SDW) order in bilayer nickelates has intensified interest in its interplay with superconductivity. Unlike cuprates, where doping rapidly suppresses the Néel temperature, the SDW transition temperature ($T_{\mathrm{SDW}}$) in bilayer nickelates is robust against oxygen annealing and even increases under pressure. Here, we combine oxygen annealing with isovalent rare-earth ($A$-site) substitution to effectively apply $c$-axis uniaxial pressure, realizing superconducting bilayer nickelate films with $T_{\mathrm{SDW}}$ suppressed from 150 K to 70 K. Notably, while SDW order is weakened but remains, a second charge-like anisotropy order is completely eliminated in the superconducting state. Polarization-resolved O $K$-edge X-ray absorption and electronic structure calculations show that strengthened interlayer coupling reconstructs the Fermi surface and weakens the SDW. These findings, consistent with a spin-spinless stripe ground state, provide new insight into the mechanism of density wave formation and their interplay with superconductivity.

cond-mat.supr-con↗

arXiv2026

In-Flight KV Cache with Clean Anchors for Faster Autoregressive Video Diffusion

Few-step autoregressive video diffusion generates a long video by splitting the video into temporal chunks and generating chunk-by-chunk, each through a short sequence of denoising stages. To memorize chunks that are already generated, previous methods reconstruct a clean or less-noisy key--value (KV) cache by additional forwards to build the cache without advancing an output latent. However, every denoising forward itself already computes the in-flight KV of the current chunk. We introduce FlashForward, which directly reuses this cache to avoid the heavy cache-update-only model forwards. After the current chunk completes one denoising stage, its stage-specific cache is already available for the next chunk. Assigning one GPU to each stage therefore lets different chunks occupy different stages concurrently. This early availability has a quality cost: the resulting stage-matched history is noisy, causing appearance and motion drift among chunks. To complement it, FlashForward produces sparse auxiliary clean anchor latents before the corresponding region is generated so the generation trajectories can be stabilized by this two-sided conditioning. The two memories operate at different temporal scales: sparse clean anchor KV supplies coarse, long-range two-sided structural guidance, while dense stage-matched history preserves fine, recent evolution. With up to four GPUs, FlashForward runs $1.16$--$1.69\times$ faster than HiAR and $1.42$--$2.92\times$ faster than Self-Forcing for 16 FPS videos of 20 seconds or longer across 1.3B and 14B backbone scales at 480p and 720p. On VBench, for the 1.3B model at 480p, it achieves higher scores and remains stable at longer durations, demonstrating that FlashForward generates high-quality and temporally consistent videos across durations of 20s, 35s and 65s at a much faster generation speed.

cs.CV↗

arXiv2026

Microscopic Origin of Pressure-Enhanced and Robust Superconductivity in Infinite-Layer La$_{0.8}$Sr$_{0.2}$NiO$_2$

Recent transport measurements on freestanding La$_{0.8}$Sr$_{0.2}$NiO$_2$ membranes revealed a broad superconducting dome extending from ambient pressure to 210 GPa, with an onset transition temperature reaching 74.5 K near 146 GPa. Using first-principles calculations, a pressure-dependent two-orbital model, and self-consistent FLEX calculations combined with the linearized Eliashberg equation, we determine how compression modifies the pairing tendency. Pressure increases the kinetic-energy scale, reduces $U_x/t_1$, strengthens interlayer hybridization, and transfers holes from the La/Sr-derived charge reservoir to the correlated Ni sector. Within the present low-energy description, the increasing kinetic scale and the approach to optimal intermediate coupling account for the initial enhancement of pairing, whereas pressure-induced self-doping into the overdoped regime is primarily responsible for its high-pressure suppression. Despite a pronounced three-dimensionalization of the Fermi surface, the pairing-relevant spin susceptibility remains weakly dependent on $q_z$ and peaked near $(π,π)$. Consequently, the Ni-$d_{x^2-y^2}$-dominated $d$-wave pairing state remains stable over the calculated pressure range. These results provide a unified microscopic interpretation of both the superconducting dome and its unusual robustness under megabar compression.

cond-mat.supr-con↗

arXiv2026

Symmetry-Preserving Phase Transitions in $AM_2$Al$_9$ Materials under Pressure

External parameters such as temperature, pressure, and chemical doping can induce structural phase transitions in materials. Although such transitions usually involve a change in symmetry, an uncommon exception is the isostructural phase transition, which is first order yet preserves the symmetry of the parent structure. Using first-principles calculations, we show that $AM_2$Al$_9$ compounds ($A$ = Ba, Ca, Sr, or Eu; $M$ = Fe, Co, or Ni) undergo pressure-induced isostructural phase transitions. At the transition pressure, these systems exhibit a pronounced volume collapse while retaining the same crystal symmetry and space group ($P6/mmm$). Bonding analysis based on the integrated crystal orbital Hamilton population (ICOHP) shows that the transition is driven by a redistribution of bonding character between intralayer and interlayer atomic bonds. Because isostructural transitions are rare in single crystals, $AM_2$Al$_9$ provides a promising platform for investigating critical phenomena under pressure and for deepening our understanding of symmetry-preserving structural transitions.

cond-mat.mtrl-sci↗

arXiv2026

Bound states in doped charge transfer insulators

Understanding the physics of doped charge transfer insulators is the most important problem in high-temperature superconductivity. In this work, we show that an in-gap bound state emerges from the localized hole of the doped charge transfer insulator. We propose an approximate ground state wavefunction based on one localized Zhang-Rice singlet and the Neel state. By calculating the excitation states with one hole added and removed from this ground state, we successfully identify the existence of bound states inside the charge transfer gap. This feature is further confirmed by a Lanczos calculation based on matrix product states (MPS) for a system of $4\times4$ CuO$_2$ unit cells. How these bound states evolve into metallic states is further discussed. Our findings identify the key component of recent STM results on lightly doped Ca$_2$CuO$_2$Cl$_2$ and provide a new understanding of hole-doped charge transfer insulators.

cond-mat.supr-con↗

arXiv2026

Screening phonon-mediated superconductors from static orbital Hamiltonians

The first-principles search for superconductors is severely limited by the high cost of electron-phonon coupling (EPC) calculations. Here we develop a low-cost, physically transparent framework that identifies strong-EPC materials directly from static orbital-based Hamiltonians without explicit phonon perturbation calculations. Verification using density functional perturbation theory (DFPT) for representative superconductors shows that the framework captures semi-quantitatively the EPC scale at substantially lower computational cost. Applied to more than 36,000 compounds in the MattKeyBond database, it identifies 34 dynamically stable superconducting candidates with calculated $T_c > 10$ K after DFPT verification. These candidates reveal two distinct routes to relatively high-$T_c$ superconductivity: a metallized covalent $σ$-bond route that is more favorable for achieving high-$T_c$ superconductors, and a Fermi-level density-of-states accumulation route that can enhance $T_c$ but usually to a more limited extent.

cond-mat.supr-con↗

arXiv2026

Bridging Crystal Structure and Material Properties via Bond-Centric Descriptors

Although chemical bonding is the fundamental mechanistic bridge connecting atomic structure to macroscopic material properties, current data-driven materials science largely treats it as an implicit "black box". Existing machine learning (ML) models rely predominantly on geometric coordinates, forcing them to implicitly relearn complex quantum mechanics from scratch. This lack of intermediate physical features limits model interpretability and generalizability, particularly when training data is scarce. To solve this problem, we introduce MattKeyBond, a bond-centric materials database that explicitly maps the local electronic landscape and bonding interactions of materials. Building on this, we propose Bonding Attractivity (BA), a novel element-specific descriptor that quantifies the intrinsic capability of atoms to form covalent networks. By providing pre-calculated, energy-dimensional bonding descriptors, MattKeyBond transforms the implicit "black box" into physically interpretable features. This strategy relieves ML models from the burden of deducing physical laws from pure geometry, enabling accurate predictions even with limited data and seamlessly integrating electronic structure theory into modern AI workflows.

cond-mat.mtrl-sci↗

arXiv2026

Exact Neural-Network Representations of the Motzkin States

Motzkin spin chains are paradigmatic frustration-free one-dimensional quantum systems whose ground states feature exactly solvable combinatorial structures and exotic, area-law-violating entanglement scaling. Specifically, colorless Motzkin states exhibit critical logarithmic entanglement divergence $\log N$ with system size $N$, while their colorful counterparts host supercritical sublinear $\sqrt{N}$ entanglement growth. Such unconventional entanglement behaviors place these states well beyond the expressive capability of standard matrix product states, which are fundamentally constrained by the entanglement area law. Here, we systematically construct exact, training-free neural-network representations for both colorless and colorful Motzkin states across four mainstream architectures, including recurrent, feedforward, convolutional, and transformer networks. Our core design leverages a causal prefix-sum module, implementable via recurrent updates, feedforward mappings, or masked attention layers, combined with position-selective rectified linear gates that enforce the Motzkin height constraints. For the colorful states, we further introduce a dedicated causal stack module that explicitly encodes the last-in-first-out color-matching rule. Our results demonstrate that neural architectures can accurately capture highly non-trivial entanglement features inaccessible to conventional tensor networks, providing prototypic examples for benchmarking and a constructive design framework for future neural-network quantum state developments targeting strongly entangled quantum systems.

cond-mat.str-el↗

arXiv2026

Efficient classical simulation of two-dimensional long-range systems: Rydberg arrays and beyond

In variational Monte Carlo (VMC) calculations of $N$-site quantum systems with arbitrary all-to-all two-body interactions, evaluating the local energy generally costs $O(N^3)$. We introduce a new framework that reduces this cost to $O(N)$ for tensor network states, capable of scalable and accurate computation of real-time dynamics and ground states. As a result, we obtain accurate simulations of the adiabatic real-time protocol of a $10\times10$ dipolar XY model realized in a Rydberg simulator [C. Chen et al., Nature 616, 691 (2023)], which was previously beyond the reach of classical simulation. Going beyond quantum experiments, we also directly perform ground state VMC to compare with the adiabatic state preparation. Our work demonstrates tensor network VMC as a powerful classical simulator for long-range quantum platforms such as Rydberg and ion-trap simulators, which are currently in urgent need of scalable classical benchmarking tools. As a separate technical contribution, we resolve the pathology of evolving from product states within of tensor network VMC.

quant-ph↗

arXiv2026

Absence of poor local minima in matrix product states

Quantum circuits suffer from severe trainability issues: even shallow circuits are swamped with poor local minima. Yet matrix product states (MPS), which can be prepared by sequential circuits, are remarkably trainable in practice -- as demonstrated by decades of successful density matrix renormalization group calculations. In this work, we resolve this apparent paradox by proving that the energy landscapes of MPS are free from poor local minima, under the same setting where brickwork circuits are not. The key insight is that the gauge freedom of MPS creates an effective local overparametrization that causes local minima to concentrate near the global minimum, analogous to overparametrized classical neural networks. We rigorously prove that the local minimum distribution is invariant under moves of the orthogonality center of MPS representations. Numerical experiments further confirm that the optimization of sequential circuits converges to near-optimal solutions even for random Hamiltonians, in stark contrast to brickwork circuits. Our findings establish a theoretical understanding of the trainability of MPS, providing a valuable guide for designing variational quantum circuits and algorithms with better trainability in the future.

quant-ph↗