Papers

Changing-look active galactic nuclei (CL AGNs) show the appearance or disappearance of broad emission lines on timescales of years. Among them, the repeating CL (RCL) AGNs may provide a clear clue for our understanding of the CL transitions because the same nucleus crosses some physical boundaries more than once. We search for RCL AGNs in known CL-AGN samples using long-term multi-band light curves, and selected 34 candidates for spectroscopic follow-up. We confirm 25 RCL AGNs, including 22 newly identified cases. Properties of these RCL AGNs are analyzed. The observed rest-frame intervals of the second transitions are mostly 3–4 yr, while the variations of the optical light curves suggest that some transitions might occur on timescales of several months. The latest spectra show that the on/off states correspond to higher/lower Eddington-ratio, in the expected direction relative to the parent CL-AGN samples. As seven RCL AGNs are well covered by nearly continuous single-band light curves, their on/off states can be found to follow multi-year optical excursions, and their Eddington ratios vary consistently with the photometric changes. We also find that the H$\beta$-only transitions occur at higher Eddington ratios than the transitions involving both H$\alpha$ and H$\beta$, suggesting a line-dependent Broad-Line-Region (BLR) visibility threshold. These results support a picture in which different accretion-flow processes drive reversible changes in the central ionizing emissions, while the observed RCL transitions are produced by the BLR breathing across line-dependent visibility thresholds.

Learning a classical description of a quantum state is a fundamental task in quantum computation. Among the most important classes of quantum states are stabilizer states, which play a central role in quantum error correction and fault-tolerant computation. To mitigate the effects of realistic noise, agnostic learning of stabilizer states has emerged as a natural and well-motivated problem. Recently, Chen et al. [STOC'25, p. 429-438] resolved this problem for qubit systems by using a stabilizer bootstrapping framework. However, the agnostic learning of qudit stabilizer states remains largely unexplored, since the qudit setting introduces fundamental structural differences that prevent a direct generalization of existing qubit techniques. In this paper, we successfully generalize the stabilizer bootstrapping framework to qudit systems and present the first efficient quantum algorithm for agnostic learning of qudit stabilizer states. Specifically, given copies of an unknown $n$-qudit pure state $|\psi\rangle$ that has fidelity $\tau$ with some stabilizer state, our algorithm outputs a stabilizer state $|\phi\rangle$ such that $\left| \braket{\phi|\psi} \right|^2 \geq \tau - \varepsilon$ with high probability. The algorithm uses only single-copy and four-copy measurements, and its sample and time complexity scale as $(d/\tau)^{O(d^2 \log(1/\tau))} \cdot \mathrm{poly}(n, 1/\varepsilon)$, where the dimension $d$ is an odd prime. As a direct corollary, our algorithm enables efficient estimation of the magic of a quantum state, as quantified by its stabilizer fidelity. Completing the picture, we also present a streamlined algorithm for the high-fidelity regime $\tau > \cos^2(\pi/8)$, establishing a qudit analogue of the threshold-based approach in prior qubit work.

Testing Lorentz invariance violation (LIV) is notoriously difficult, as its characteristic energy scale, $M_\star$, in modified dispersion relations typically lies beyond current experimental reach. To enable a low-energy probe, we investigate the quantum coherence of an inertial Unruh-DeWitt (UDW) detector interacting with a massless scalar field with a Lorentz-violating dispersion relation $\omega_{|\textbf{k}|}=|\textbf{k}|f(|\textbf{k}|/M_\star)$. Unlike the Lorentz-invariant case where quantum coherence is rapidity-independent, we find for the Lorentz-violating quantum field case the dynamics of detector's quantum coherence exhibits a strong dependence on its rapidity – offering a potential low-energy signature of LIV. Applying this to the polymer-quantized scalar field theory inspired by loop quantum gravity, we show that the detector's quantum coherence exhibits a pronounced dependence on rapidity and undergoes a sharp transition near a critical rapidity $\beta_c \approx 1.3675$, a value within reach of existing facilities like the Relativistic Heavy Ion Collider. We also show how the detector's rapidity and its energy-level spacing enhance the response to the test of LIV. Our results establish quantum coherence as a sensitive and practical probe of LIV, providing a complementary avenue for testing quantum gravity-induced modifications to field theory.

In this work, we establish a structural correspondence between quantum states and matroid theory. This connection demonstrates that key properties of quantum states, including entanglement and measurement, can be characterized in purely combinatorial terms via matroids, despite the apparent conceptual distance between these two fields. Using this framework, we show that a matroid-supported state is genuinely entangled when its underlying matroid is connected. Moreover, a uniform superposition over all bases of a matroid is genuinely entangled if and only if the matroid is connected. We also demonstrate that a local measurement in the $Z$-basis on such a state yields another matroid-supported state, whose underlying matroid is a minor of the original one. Inspired by matroid duality, we further propose a notion of quantum state duality, uncovering a deep structural symmetry in state transformations.

Alternating Current Optimal Power Flow Unit Commitment (AC-OPF-UC) is a difficult mixed-integer nonlinear optimization problem that combines binary generator commitment decisions with nonconvex continuous AC power-flow constraints. In this work, we investigate whether hybrid quantum-classical variational algorithms can improve the solution of single-period AC-OPF-UC relative to classical approaches. To the best of our knowledge, this is the first study to directly evaluate quantum or hybrid quantum-classical algorithms for the full AC-OPF-UC problem. We consider two candidate algorithms for improving AC-OPF-UC solution quality relative to purely classical methods on ideal quantum hardware. The first applies QAOA directly to a fully discretized formulation of the problem, with equality and inequality constraints incorporated through penalty terms and slack variables. Although conceptually straightforward, this approach requires a prohibitively large number of qubits even for small instances. The second, qubit-efficient approach encodes only the binary generator status variables on a quantum computer, while optimizing the continuous power-flow variables classically for each sampled bitstring. We benchmark this method on randomly generated AC-OPF-UC instances with 5 to 13 generators and compare it against SCIP, SMAC, and uniform random sampling. Our simulations show that the qubit-efficient hybrid method does not outperform uniform sampling. These results suggest that in order to establish potential advantage of the variational hybrid strategy considered here over the best classical algorithms, if any, much larger system sizes (25+ generators) need to be tested, which is beyond our computational capacity. Alternatively, different approaches, such as quantum versions of branch-and-bound methods, may be more promising.

Stellar rotation plays a key role in the transfer of angular momentum, and a large sample of Be-type stars with reliable projected rotational velocities is crucial for understanding their formation and evolution. In this work, we derive the projected rotational velocities ($v$\,sin\,$i$) of 479 Be-type stars using the Fourier transform method, based on their LAMOST Medium-resolution Survey (MRS) spectra. Our results suggest that the Fourier transform method can provide reliable $v$\,sin\,$i$ values for Be-type stars by analyzing the \ion{He}{1}\,lines at 4922, 5015, 5047, and 6678 \,Åin their LAMOST MRS spectra. A K-S test indicates that Be-type stars with different H$\alpha$ emission line morphologies exhibit different $v$\,sin\,$i$ distributions, and Be-type stars with double-peaked emission have a higher fraction of rapid rotators than those with single-peak emission. The $v$\,sin\,$i$ distributions of our Be-type stars in the field, OB associations, and clusters show no significant differences. The deconvolved $v$\,sin\,$i$ distribution of our entire Be-type star sample does not exhibit a bimodal distribution but rather a single peak at $v\approx260$\,km$\cdot$s$^{-1}$. Based on the analysis of 105 stars in our sample, we find that the mean equatorial rotational velocity is 0.74 times the critical velocity. Furthermore, we investigate the relationship between $v$\,sin\,$i$ and the H$\alpha$ peak separation velocity for Be-type stars exhibiting double-peak H$\alpha$ emission lines, using Pearson and Spearman rank correlation coefficients.

Organic-inorganic hybrid perovskites (OHPs) deliver certified single-junction power conversion efficiencies (PCEs) exceeding 26% and perovskite-silicon tandem values surpassing 34%, yet a substantial gap with the Shockley-Queisser (S-Q) limit persists-primarily due to grain-boundary (GB) defects that drive non-radiative recombination, ion migration, and degradation. Rational passivation demands a non-contact tool capable of identifying and quantifying specific defect species in device-relevant thin films, a capability absent from conventional probes. This short review demonstrates that terahertz time-domain spectroscopy (THz-TDS, 0.3-3.0 THz) fulfills this role. Across four OHP compositions fabricated by sequential vacuum evaporation (SVE)-MAPbI3, MAPbBr3, FAPbI3, and CsPbI3-the THz spectral window captures both intrinsic phonon modes and GB-localized molecular defect vibrations, enabling species-specific, quantitative characterization at room temperature. Notably, the oscillator strength of the SVE-specific 1.58 THz absorption in MAPbI3 scales linearly with XPS-quantified CH3NH2 defect concentration, establishing THz-TDS as a direct, non-destructive defect meter. Building on these findings, we propose a three-pillar framework for THz-guided defect engineering: (I) quantitative defect measurement via oscillator-strength analysis, (II) material-specific fingerprint identification from a systematically constructed THz library, and (III) fingerprint-guided defect elimination with real-time feedback-together defining a closed-loop quality-control cycle that connects spectroscopic diagnosis to passivation strategy and, ultimately, to enhanced solar-cell efficiency.

In the Hayden–Preskill protocol, the decoder is usually assumed to know the microscopic identity of the collected output qubits. We study what happens when these labels are unavailable and only the relative order of the received qubits is preserved. The resulting order-preserving deletion channel maps an $n$-qubit scrambled register to a subsequence of length $\ell$. For a diary of fixed size $k$, we prove that the optimal entanglement fidelity converges to the no-output value $4^{-k}$ when $\ell=o(n^{2/3})$, uniformly over the scrambling unitary. For a Haar-random scrambling unitary, it converges to one when $\ell=\omega(n^{2/3})$ and $\ell=o(n)$. Monotonicity then gives the fixed-error recovery scale $\ell_{\mathrm{rec}}=\Theta(n^{2/3})$. We also consider partial position information obtained by dividing the register into $B$ consecutive blocks and revealing the block of origin of each received qubit. The recovery scale becomes $\ell_{\mathrm{rec}}(n,B)\asymp n^{2/3}B^{-1/3}$ for $B\leq\sqrt n$ and $\ell_{\mathrm{rec}}(n,B)\asymp n/B$ for $B\geq\sqrt n$. The exponent $2/3$ is traced to rank-aligned coincidences between random subsequences, which control both the converse and the recovery argument. Thus, even purely classical information about the origin of the received subsystems can change the amount of quantum output required for Hayden–Preskill recovery.

Efficient discovery of high-performance materials has been pursued through a variety of data- and AI-driven strategies, among which inverse design, generating materials from desired target properties, has emerged as an important paradigm. Large language models (LLMs) offer a complementary route for inverse materials design because their reasoning and in-context learning capability can be used not only to propose candidates but also to demonstrate interpretable design principles. In this work, we introduce a domain specific language (DSL)-guided strategy to improve the reasoning and design capability of LLM agents by translating natural language design rules into symbolic predicates encoded in a predefined chemistry DSL. These predicates allow the LLM agent to obtain statistical evidence from the accumulated materials data, enabling the agent to evaluate and refine its own reasoning during the design loop. Based on this strategy, we developed a multi-agent materials design framework, called Operational Rule-grounded CHEmical Search Through Reasoning Agents (ORCHESTRA), and applied it to the inverse design of double perovskite oxides under multiple target-property objectives. The results show that symbolic predicates help the LLM identify unsupported rules, validate newly proposed rules and improve the rule store over iterative design cycles. Compared with a strategy relying only on natural language rules, the DSL-guided framework showed the potential to improve materials design performance, particularly for challenging target properties. These findings suggest that mathematical and statistical grounding can enhance the reasoning capability of LLM agents in materials science and that LLM-based inverse design can be performed effectively without large task-specific datasets or additional model training.

A mixed-pitch grating coupler which can couple a wide range of wavelengths is preferred in its application in co-packaged optics (CPO). However, the design and optimization of such grating coupler is complex. In this work, we developed software with integrated deep neural network (DNN) model to automatically design the mixed-pitch grating coupler from user-specified peak wavelengths and full-width half-maximum (FWHM) values. We first trained the DNN model with 10,000 rows of grating parameters-power spectrum datasets, where the power spectrum was simulated using finite-difference time domain (FDTD) technique. Upon training, we tested the model using ~1,000 different combinations of peak wavelengths and FWHM values. Among the combinations, 822 attempts have <15% error, while 351 attempts have <5% error when comparing the user-specified and FDTD-verified spectrum. Meanwhile, comparing the user-specified and FDTD-verified peak wavelengths, 844 attempts have peak wavelengths with absolute error (AE) < 2 nm. For FWHMs, 738 attempts have FWHM values with AE < 10 nm. We have also developed a graphical-user interface (GUI) to ease the usage of this software.

Knowledge distillation for jet taggers is usually cast as compression toward a near-oracle teacher. We instead train a small reliability-gated correction layer jointly over two frozen teachers — a GN2-type ATLAS flavour tagger (3 classes) and CMS ParT (10 classes) — reading only their frozen penultimate embeddings and logits, so it adds negligible marginal cost. A single per-sample reliability signal reweights the distillation loss, drives a prototype-based repair of the teachers' errors, and anchors the inference-time gate that mixes teacher and student logits. Zero-initialised, the 82\,k-parameter student starts as a bit-exact copy of the teacher — a provable invariant on the training distribution — and departs only where the data support it. Under a statistically-grounded evaluation, with binomial, Poisson and paired uncertainties on every table, it reproduces both teachers at $\geq\!99\%$ agreement and matches their overall accuracy while redistributing it toward their weakest classes: the JetClass per-class accuracy of $H\!\to\!4q$, QCD and $H\!\to\!bb$ rises by $+0.8$, $+0.4$ and $+0.2$ percentage points ($|z|>7$), offset by comparable drops elsewhere. Oracle heads on the frozen embedding recover a budget consistent with zero in both domains, and a conformal-risk-control analysis certifies the false-accept rate. This full re-evaluation supersedes an earlier, incorrectly-evaluated set of numbers; code and models are available at https://github.com/sysu17363020/JetCoRD.

We propose and numerically demonstrate doubly resonant third-harmonic generation (THG) in a diamond photonic crystal (PhC) slab, in which the fundamental harmonic (FH) and the third harmonic (TH) modes are simultaneously resonant within the same membrane. A hexagonal-lattice slab with triangular air holes is designed so that a K-point band-edge FH mode and a $\Gamma$-point quasi-bound-state-in-the-continuum (quasi-BIC) TH mode satisfy the frequency-tripling condition $3\omega_1\approx\omega_3$. Modifying the hole shape from circular to equilateral triangular breaks the in-plane symmetry that otherwise forces the nonlinear coupling to vanish, thereby converting a TH mode with negligible overlap into one with finite while simultaneously reducing the required slab thickness. Guided by a closed-form expression for THG efficiency derived from coupled-mode theory, we design the unit cell and a PhC heterostructure cavity. Three-dimensional simulations of the designed cavity yield a normalized THG efficiency $\eta=2.7\times10^{-7}~\mathrm{W}^{-2}$ under moderate quality factors, which is projected to reach ~$0.034~\mathrm{W}^{-2}$ at the fabrication-limited quality factor (Q = 200,000). Because the operating wavelength is set by the lattice constant, this design, combined with the ultra-wide transparency window of diamond, can map a single geometry across various fabricable wavelengths, spanning from telecommunication bands to color-center-resonant visible and deep-UV outputs. These results establish a robust route toward efficient, monolithic on-chip frequency conversion in an all-diamond platform for quantum and nonlinear photonics.

We present the results of a survey program with JWST's MIRI LRS that obtained 5$-$14 $\mu$m R~100 spectra of twenty-three L0$-$T6 dwarfs. This spectral type range spans the formation, growth, and sedimentation of silicate condensate clouds, as well as the appearance of methane and ammonia in the atmospheres of brown dwarfs. The initial onset of silicate absorption at spectral type L0 as well as the evolution of this feature with spectral type are both examined in unprecedented detail. We detect 7.5$-$9.5 $\mu$m gas-phase SiO absorption for the first time in dwarf stars, with detections in M5.5$-$L2.5 dwarfs from combined Spitzer IRS and JWST spectra. This feature has previously been detected in K and M giants, with stronger absorption in these giants than in higher-gravity M dwarfs. In contrast, low-gravity L dwarfs show weaker SiO absorption than field-gravity L dwarfs. We also confirm water, ammonia, and methane absorption trends with spectral type previously seen in Spitzer IRS observations. We report the possible detection of CS2 in a T3.5 dwarf, although confirmation would require higher-dispersion spectroscopy. We find two newly resolved binaries, confirm two near-equal flux unresolved candidate binaries, and identify two additional possible non-equal flux binaries. After accounting for contamination by resolved and candidate binaries, we find that single field dwarfs are ~0.15$-$0.20 mag underluminous compared to existing empirical relations. The presented spectral library offers a spectrophotometrically calibrated basis for future interpretation of JWST MIRI spectra of brown dwarfs or of directly imaged or transiting exoplanets.

Correlations among the degrees of vertices in random graphs often occur when clustering is present. In this paper we define a joint-degree correlation function for vertices in the giant component of clustered configuration model networks which are composed of clique subgraphs. We use this model to investigate, in detail, the organization among nearest-neighbor subgraphs for random graphs as a function of subgraph topology as well as clustering. We find an expression for the average joint degree of a neighbor in the giant component at the critical point for these networks. Finally, we introduce a novel edge-disjoint clique decomposition algorithm and investigate the correlations between the subgraphs of empirical networks.

Learning a computational method has always meant learning to operate a tool – pencil, slide rule, calculator, or programming language. Agentic artificial intelligence, which writes, executes, and revises simulation code from natural-language specifications, is the latest and largest step in a centuries-long migration of mechanical work from human to tool. I argue that what a learner must know has remained remarkably stable: the inputs and outputs of a method, the concept of what it does, the terminology to communicate about it, the judgment to evaluate its results, and the skill of operating the current tool. This paper organizes these requirements into a tool-invariant framework spanning single-digit addition to agent-orchestrated molecular dynamics, argues that verification – not code authorship – is now the load-bearing skill, and draws the consequence for assessment: when artifacts can be generated on demand, the artifact no longer certifies the student. I describe a practical response, designed for the small classes where the subject lives – AI-free in-class coding quizzes paired with oral defenses of comment-stripped, AI-assisted work – and argue that the real product of a computational physics course is the student's ability to explain and defend computational artifacts in the language of the discipline.

We analyze 405 deep JWST/NIRSpec spectra of star-forming galaxies at $z=4.5$–$9.8$ from multiple JWST lensing-cluster surveys, including DREAMS, SPURS, GLIMPSE-D, UNCOVER, CANUCS, GLASS, and ERO, to study chemical enrichment in intrinsically faint, low-mass galaxies. The sample covers $M_{\rm UV}\simeq-12$ to $-22$ and reaches $M_\star\simeq10^{5.7}\,M_\odot$, with 50% of the sources at $M_{\rm UV}>-17.5$ magnified by $\mu>3$. From individual spectra and mass-binned stacks, we derive the gas-phase metallicity together with nitrogen and carbon abundances using the rest-frame UV and optical lines. We derive new empirical strong-line metallicity calibrations using direct-method measurements from stellar-mass-binned stacks, reaching a representative stellar mass of $M_\star\simeq10^{6.6}\,M_\odot$. Applying these calibrations, we trace the $z\sim6$ mass–metallicity relation down to $M_\star\simeq10^{6.6}\,M_\odot$, where it reaches $12+\log({\rm O/H})\simeq7.2$. A two-parameter linear fit gives a slope of $\alpha=0.318\pm0.037$, only slightly steeper than the local low-mass slope and broadly consistent with hydrodynamical simulations. In the $M_\star\simeq10^{7.7}\,M_\odot$ stack, N/O from N\,iv]\,$\lambda\lambda\,1483,1486$ exceeds that from [N\,ii]\,$\lambda6583$ by $\simeq1.4$ dex. The UV–optical difference could indicate a localized, highly ionized N-rich component whose high N/O and subsolar C/O resemble nitrogen-rich globular-cluster populations with $M_\star\lesssim10^6\,M_\odot$. The combination of these abundance patterns and a He\,ii\,\W4686/H$\beta$ ratio of $\simeq0.03$, well above BPASS predictions, suggests that WR stars may contribute both prompt CNO-cycle enrichment and hard ionizing radiation, with the inferred WR population potentially supplying enough nitrogen to account for the excess on globular-cluster scales.

Coherent Hamiltonian drifts and systematic errors can pass through quantum error correction differently than stochastic Pauli noise: they generate phase components of the logical channel that are lost under full Pauli twirling. We consider the Lindbladian dynamics of \(n\) physical qubits propagated through an effective logical channel of a fixed encoding, recovery, and decoding scheme. The work is methodological in character: standard ideas of observability and minimal realization from linear systems theory are applied to the channel \[ \omega \mapsto \Phi_t(\omega) = \cD e^{t\cL}\cE(\omega), \] where \(\cE\) encodes the logical state and \(\cD\) is the chosen recovery map with final decoding. The minimal code-visible space \(\cK_{\rm code}\) is defined as the Krylov closure of the decodable logical observables with respect to \(\cL^\dagger\); its observable factorization yields a minimal realization of the logical channel. In this form, the Knill–Laflamme conditions serve as a short-time consistency check, Pauli noise on stabilizer codes reduces to a chain on syndrome-logical classes, and the choice of recovery map acts as a spectral filter on the visible modes. For quantum memory, the same language yields an estimate of the storage time in terms of the dominant visible eigenvalues of the logical channel of the correction cycle. Particular attention is paid to the spectral signatures of coherent noise and to how these signatures depend on the chosen decoder.

In single-mode fiber (SMF) the Kerr effect ties every channel's quality of transmission to its neighbors' launch powers through cross-phase modulation (XPM), four-wave mixing (FWM) and inter-channel stimulated Raman scattering (ISRS), forcing a jointly planned launch profile. Hollow-core fiber (HCF), with a Kerr coefficient three to four orders of magnitude below silica, turns the per-channel powers into nearly independent knobs limited only by the shared amplifier budget. We build a per-channel generalized signal-to-noise ratio (GSNR) budget for amplified HCF links, including amplified spontaneous emission (ASE) with a wavelength- and output-power-dependent erbium-doped fiber amplifier (EDFA) noise figure (NF), inter-modal interference (IMI), nonlinearity in amplifier pigtails, CO2 gas-line loss and a flat transceiver (TRx) noise ceiling, and derive a sensitivity law that predicts when power shaping pays: its gain is bounded by the ASE noise share, canceled by self-phase modulation (SPM) at the SMF single-channel optimum, and positive in HCF. Across 80x64-GBaud C-band links over 400-3200 km, per-channel optimization buys up to 1.0 dB of worst-channel GSNR over a flat launch as the EDFA NF spread grows to 4 dB, cuts cross-channel power sensitivity by more than two orders of magnitude relative to SMF, and reaches a given GSNR at about 3 dB lower aggregate amplifier output. At a fixed amplifier budget this becomes a 1.26 to 1.41 times worst-channel reach extension, against at most 15% on the nonlinearity-capped SMF link.

Urban low-altitude uncrewed aerial vehicle (UAV) operations are shaped by building-modified wind and constrained terrain-building clearance. Spatially heterogeneous wind can perturb short-horizon motion, but its clearance consequence depends on local airspace: similar perturbations may remain benign in open areas yet become clearance-sensitive near building walls, roof edges, or elevated terrain. This study develops a common-basis method for early clearance assessment before route buffers or corridor alignments are fixed. For each position-height state, nominal and wind-affected vehicle-sized trajectory envelopes are generated with a shared heading and sampling design, separating fixed-support wind loading, initial clearance exposure, and wind-induced clearance change. Applied to Shanghai and Beijing, the method uses lattice-Boltzmann method (LBM) wind fields co-registered with 20 m terrain-building scene grids, height layers of 60-340 m, and a representative light UAV. Results show distinct spatial and vertical patterns in baseline wind loading and clearance exposure. Wind-affected envelopes produce little domain-wide median shift in clearance exposure but generate spatially concentrated upper-tail responses in lower-altitude layers on the normalized clearance-exposure scale. Kilometer-Tile-level analysis links these responses mainly to baseline clearance sensitivity and horizontal wind-gradient magnitude, with fixed-support wind loading and mean wind speed playing secondary roles. Low-altitude planning should distinguish wind-loaded locations from clearance-sensitive locations. As an upstream diagnostic, the method identifies units where nominal trajectory envelopes approach terrain-building margins and local wind variation may further alter clearance exposure, with increases concentrated in the upper tail.

High-fidelity entangling gates are useful for circuit-based quantum computing, yet their realization in a scalable platform is challenging. Optically trapped ultracold polar molecules offer a potentially scalable platform. Here, we introduce a two-qubit molecular entangling gate via a novel ancilla-assisted dark-state mechanism, where high fidelity is achievable. In particular, we find that by using appropriate qubit states for the ancilla and the data qubits, dark states can appear, and entanglement can emerge by driving the ancilla only. It is also feasible to extend the two-qubit gate to a multi-qubit gate via the same dark-state mechanism.

The sign problem is the non-positive path-integral weight that makes a quantum system hard to simulate classically. It shows up in problems that look unrelated, from fermion antisymmetry and geometric frustration to real-time evolution, topological angles, and finite density. We study it here for compact continuous variables, modeled as networks of quantum rotors, $\hat H=\sum_i 4E_C(\hat n_i-n_{g,i})^2-\sum_{\langle ij\rangle} E_{ij}\cos(\hat\phi_i-\hat\phi_j-\theta_{ij})$. For these, a single gauge-invariant quantity controls the obstruction, the frustration flux through the independent loops of the interaction graph. We prove a sign rule, the rotor analogue of Marshall's. The charge-basis Hamiltonian is sign-free if and only if every loop flux vanishes modulo $2\pi$. Exact diagonalization and density-matrix renormalization then show that the sign cost is a gauge-invariant function of the flux. For a single loop it vanishes at zero flux, peaks at $\pi$, and is exponentially small in the loop's perimeter. Summed over many loops it is extensive, growing with system size. The same loop flux generates the sign of three problems usually treated apart, namely the Mott transition (through the slave-rotor mapping), compact $U(1)$ lattice gauge theory, and frustrated continuous optimization. A superconducting rotor array realizes both the model and its sign natively.

In this work, we investigate a CPL-type $Om(z)$ parametrization given by $Om(z)=l+m\left(\frac{z}{1+z}\right)$ to study the evolution of dark energy and possible deviations from the standard $\Lambda$CDM cosmology. The model reduces to the flat $\Lambda$CDM scenario for $m=0$, while $m\neq0$ indicates dynamical evolution. The free parameters are constrained using Pantheon+SH0ES (PPS), Cosmic Chronometer (CC), and DESI BAO DR2 observations through Markov Chain Monte Carlo analysis. The obtained constraints yield $72.48\lesssim H_0\lesssim72.97~\mathrm{km\,s^{-1}\,Mpc^{-1}}$, while the evolution parameter remains negative for all dataset combinations, leading to decreasing $Om(z)$ evolution and favoring quintessence-like behaviour. The combined PPS+CC+DR2 analysis shifts the evolution closer to the $\Lambda$CDM limit. The statistical analysis based on $\chi^2$, AIC, and BIC indicates that the proposed model remains statistically competitive with $\Lambda$CDM. The deceleration parameter and total equation of state are further analyzed to examine the cosmic evolution. The transition from deceleration to acceleration occurs within $z_{tr}\approx0.66-1.01$, while the present values vary within $-0.459\lesssim q_0\lesssim-0.325$ and $-0.639\lesssim\omega_0\lesssim-0.550$ depending on the datasets. Overall, the proposed parametrization provides a flexible framework for studying late-time cosmic evolution.

The aim of this paper is to introduce and disseminate to a wider readership of mathematicians, mathematical physicists and philosophers of mathematics and physics alike the basic theoretical, both conceptual and technical, tenets of Mallios's Abstract Differential Geometry, as well as to present and to summarise the main results from its applications in the last quarter of a century towards formulating an entirely homological algebraic, purely gauge-theoretic, finitistic, quantal and manifestly background geometrical smooth spacetime manifold independent vacuum Einstein gravity. New, previously unpublished, constructions and results are included in the paper.

Accurate sunspot classification is essential for assessing the eruptive potential of solar active regions and forecasting space weather. We present JW-SSD, a high-quality multimodal benchmark dataset for fine-grained magnetic-type classification of sunspots. Constructed from SDO/HMI SHARP 720s data (2010-2023, Solar Cycles 24 and 25), JW-SSD comprises 36,553 co-registered magnetogram-continuum pairs from 2,507 active regions. Unlike conventional three-class schemes, JW-SSD refines the Mount Wilson classification into five physically meaningful categories ({\alpha}, e̱ta}, e̱ta}-{\delta}, e̱ta}-{\gamma}, e̱ta}-{\gamma}-{\delta}), enabling finer characterization of magnetic complexity. Rigorous quality control-including central meridian distance restriction, saturation filtering, and sharpness screening-ensures high data validity. The dataset is provided in both FITS and PNG formats, with standard training (29,243) and test (7,310) splits. Benchmark experiments with four representative architectures (U-Net, ResNet-50, EfficientNet-B0, and ViT-Small) yield high accuracy across all models (89.43%-94.78% on the three-class task), confirming that the dataset is reliably learnable across diverse modeling paradigms. JW-SSD has further been employed to train JW-SunSpot, a multimodal large language model that achieves the highest classification accuracy, demonstrating the dataset's broad applicability to both conventional networks and large-language-model-based approaches.

A conformation tensor is positive definite for every physically realizable polymer microstructural state. Numerical discretization can move the conformation tensor outside the positive-definite domain. This raises a question: can the least eigenvalue alone identify the first unstable direction? We answer it by linearizing Oldroyd-B, equilibrium-normalized FENE-P and Giesekus models about uniform frozen states. All include solvent viscosity and stress diffusion. We examine every non-zero planar Fourier mode, assuming each model's uncoupled constitutive tangent is strictly stable. The margin $1+r\chi$ measures the balance between solvent damping and the zero-frequency polymer response. The complete velocity–conformation system is stable if and only if this margin is positive. At zero margin, a simple stationary root appears; finite inertia changes growth rates but not the neutral boundary. At fixed wavenumber and other parameters, decreasing $\lambda_1$ identifies the first neutral direction. Oldroyd-B and equilibrium-normalized FENE-P first become neutral along principal directions; Giesekus mobility can instead make an oblique direction neutral first. For the reference case, onset is $\lambda_{1,c}=-1.933$ at $\theta_c=23.94^\circ$, before the principal-axis prediction. Along this family, the all-direction threshold approaches $-2.319$ as the other principal stretch grows, whereas the formal principal-axis extrapolation tends to negative infinity. A rational-parameter counterexample, matrix spectra and uniform forced-base calculations test the neutral boundary and both sides. These results concern one linear, uniform, planar Fourier mode, not nonlinear or inhomogeneous-flow stability. Within this scope, onset depends not on indefiniteness alone but also on constitutive-tangent geometry and wavevector direction.

Human behavior shapes epidemic dynamics, yet most models represent it by rescaling transmission, conflating behavior with biology and removing the memory carried by sustained protective behavior. We develop and analyze a susceptible-infected-recovered-aware model in which prevalence-driven awareness, eroded by fatigue, moves susceptible and infectious individuals between accessible and inaccessible states rather than modifying transmission probabilities. The basic reproduction number is $\mathcal{R}_0=\beta/(\gamma+\mu)$, independent of behavioral parameters, and a unique endemic equilibrium exists exactly when $\mathcal{R}_0>1$, excluding backward bifurcation. Infection and awareness are uniformly persistent whenever $\mathcal{R}_0>1$. We derive an exact decomposition of the reduction in endemic burden into susceptible withdrawal and infectious sequestration, establish the effect of each behavioral parameter, and show that the endemic state depends on awareness response and fatigue only through their ratio, with prevalence vanishing linearly as fatigue vanishes. In the instantaneous-relaxation limit, the model reduces to a transmission-modulating formulation whose endemic equilibrium is locally asymptotically stable. At finite relaxation rates, however, numerical stability and bifurcation analyses reveal a Hopf transition and self-sustained epidemic cycles without an imposed delay. Relative to the SIR benchmark, awareness lengthens transient inter-epidemic periods and slows damping rather than creating the underlying transient oscillations.

Ultralight bosonic dark matter (UBDM) that couples to electromagnetism can generate an oscillating magnetic-field signal at the Earth's surface. This is referred to as the “Earth transducer" effect, as the Earth converts UBDM into a detectable magnetic field. Similar DM-induced fields in laboratory experiments typically scale with the size $L$ of the experiment. Because the Earth transducer signal instead scales with the large radius of the Earth, $R$, it is one of the most powerful direct probes of UBDM with masses $m_\mathrm{DM}\lesssim1/R\sim3\times10^{-14}\,\mathrm{eV}$. It has many other favorable properties, such as high spatial and temporal coherence and robustness to atmospheric modeling. In this review, we derive the Earth transducer effect and its properties for multiple UBDM models, and discuss current and future prospects to detect it.

We use perturbation theory to study kinks in nonlinear Klein–Gordon ($\phi^4$ and sine-Gordon) chains and in a discrete series-connected Josephson transmission line. The expansion parameter is the ratio of the lattice period to the kink width. The next-to-leading-order approximation modifies the kink profiles obtained previously in the leading-order approximation.

We study accretion and thermal diagnostics motivated by neutrino processes for a static black hole reconstructed from a three-parameter generalized entropy. The construction is thermodynamic by design: the entropy deformation is not mapped to a radial-coordinate redefinition or to a prescribed Reissner–Nordström-like correction. Instead, the generalized entropy fixes the horizon response factor $\Xi_h=dS_G/dS|_{S_h}$. The effective exterior geometry is then reconstructed by requiring its surface-gravity temperature to reproduce the generalized thermodynamic temperature. As a result, the metric preserves the horizon area and the Schwarzschild asymptotics, and reduces smoothly to Schwarzschild when $\Xi_h\to1$. The entropy response is encoded in $\lambda_G=1/\Xi_{\rm h}-1$, whereas the exterior radial dependence requires an additional localization prescription. The detailed accretion analysis is performed for the power-law family $h(x)=x^{-p}$, with $p\geq2$. Within this reconstruction family, positive $\lambda_G$ moves the photon sphere and ISCO inward, decreases the critical shadow scale, raises the radiative efficiency, and concentrates the energy release toward the inner disk, while negative $\lambda_G$ produces the opposite trend. A complementary comparison with exponential and rational localization functions shows that this qualitative ordering is preserved for the profiles tested, although the magnitude of the strong-field shifts remains profile-dependent. Finally, the neutrino-motivated thermal sector ...

We study the vector Dyson equation $$-\frac{1}{m(z)}=z\mathbf{1}-\mathbf{a}+Sm(z),$$ with parameter $z$ in the complex upper half-plane $\mathbb{C}_+$, where $\mathbf{a}\in\mathbb R^d$ and $S$ is a nonnegative matrix, not necessarily symmetric. This equation has a unique vector solution $m(z)\in\mathbb{C}_+^d$, for which we establish a complete measure decomposition and prove regularity. We then develop a graph-theoretic approach to the singularity and stability problem for non-symmetric matrices $S$. The graph structure of $S$ identifies the possible degeneracies of the stability operator as $z$ approaches the real axis. In particular, for non-backtracking matrices, we prove square-root growth at regular edges, cubic-root growth at regular cusps, and complete stability estimates. We also obtain the corresponding estimates for symmetric matrices in the periodic setting.