Papers

We develop a field-theoretic framework for work statistics in $O(N)$ models driven through criticality. By analyzing the dynamic renormalization group flow of composite power operators, we find the Kibble-Zurek scaling laws as a natural consequence of the flow, and we derive the scaling of work cumulants relevant to Kibble-Zurek scaling of topological defects from first principles, bypassing heuristic freeze-out argument. This yields the universal scaling $c_n \sim \tau_Q^{-\alpha_n}$ for the $n$-th work cumulant density: isolated quantum systems exhibit a scaling of $\alpha_n = p(d+nz)\nu/(1+pz\nu)$, whereas open quantum and classical systems undergo a dimensional collapse to $\alpha_n = pd\nu/(1+pz\nu)$. Validated by exact Gaussian solutions and numerical simulations, our theory establishes a foundation for general work statistics far from equilibrium, thereby bridging stochastic thermodynamics and the renormalization group theory.

We report low-temperature ultrasound measurements on single crystals of the layered van der Waals superconductor 4Hb-TaS$_2$. Specific heat and ac magnetic susceptibility show a sharp bulk superconducting transition at $T_{\rm c}\approx 2.9$~K. Ultrasound measurements reveal an additional anomaly deep inside the superconducting state near $T^{*}\approx 1$~K. The most direct signature is observed in the relative ultrasonic attenuation change $\Delta\alpha$: instead of being rapidly suppressed at $T_{\rm c}$, $\Delta\alpha$ remains large throughout the intermediate superconducting regime and drops strongly only near $T^{*}$. This loss of acoustic dissipation is accompanied by a pronounced anomaly in the relative sound velocity change $\Delta v/v$, indicating strong coupling to the lattice. The low-temperature anomaly is rapidly suppressed by magnetic field and by Se substitution, suggesting a possible superconducting origin of the anomaly. We speculate that this feature may be related to induced superconductivity in the 1T layers.

Conventional materials derive their properties from microscopic composition and arrangement, whereas mechanical metamaterials are defined by mesoscopic structure rather than constituent material. Bridging these paradigms, using macroscopic geometric alterations to orchestrate microscopic degrees of freedom and program mechanics, remains a central challenge. Here, we demonstrate that cuts in anisotropic, responsive solids provide such a connection. Using liquid crystal elastomer (LCE) sheets with kirigami patterns, we reveal that engineering strain through cuts harnesses molecular anisotropy to control emergent mechanics. Similarly, the interplay between cut patterns and the molecular phase transition of LCEs enables soft robotics functionalities such as supersoft grippers with remote actuation and architectures that reversibly morph under temperature variations, behaviors inaccessible to conventional kirigami or LCE sheets alone. LCE kirigami thus establish a new class of multiscale metamaterials in which geometry governs access to microscopic degrees of freedom, to program macroscopic function.

Solving heterogeneous Helmholtz equations at high wavenumbers remains challenging because the discretized operator is indefinite, pollution degrades phase accuracy, and scalar coarse-grid correction can discard the local phase and propagation-direction information carried by oscillatory errors. We propose Multi-channel Multigrid (McMg), a learned phase-space multigrid preconditioner for heterogeneous Helmholtz equations. Rather than predicting the solution directly, McMg maps residuals to corrections within an iterative framework. Its central idea is to coarsen physical space while retaining unresolved local wave information in the channel dimension: each coarse node carries a learned packet of amplitude, phase, direction, and scattering coefficients rather than a single scalar unknown. The architecture combines linear multi-channel transfer operators with locally adaptive stencils, neural PDE operators, and medium-dependent smoothers whose coefficients are generated from the wave speed. For a fixed medium, the V-cycle is linear in the residual; nonlinear physical features are computed once in a setup phase and cached, so each online iteration reduces to convolutions with fixed coefficients. We further study generalization across scales. Models trained on small domains transfer directly to larger domains and higher effective wavenumbers, and a Layer-by-Layer Progressive Finetuning (LLPF) strategy improves large-domain scalability by adding new coarse levels while finetuning only the newly introduced parameters. Numerical experiments on high-frequency, high-contrast, and large-scale three-dimensional problems demonstrate that McMg requires substantially fewer iterations and less wall-clock time than strong classical baselines, while consistently outperforming existing neural preconditioners.

Herein it is shown spontaneous symmetry breaking in supramolecular aggregates of D4h-symmetric zinc phthalocyanine (ZnPc). Ab initio density functional theory calculations at the M06/DGDZVP level reveal that intermolecular interactions induce a subtle relaxation of the macrocyclic framework, producing a characteristic red shift of the aza-bridge (C-N-C) stretching mode and symmetric stretching of the pyrrole ring. Confocal Raman Optical Activity measurements further reveal a negative Cotton effect at 1505 cm^-1 and 1338 cm^-1, providing evidence of emergent supramolecular chirality. Our findings identify pristine ZnPc as a model system for spontaneous self-assembled chiral symmetry breaking in molecular condensates and suggest new opportunities for chiroptical, spin-selective, and quantum functional materials.

Normalizing Flows excel at modeling a single fixed density, yet many problems across the sciences, such as high energy physics, instead require modeling how that density deforms as a function of continuous parameters: the strength of a physical effect, a calibration constant, or a source of systematic uncertainty. Learning a separate flow for every parameter configuration quickly becomes intractable, since the number of joint settings grows exponentially with the number of parameters. We introduce Factorizable Normalizing Flows (FNFs), which represent the parameter-dependent density as a fixed, high-fidelity flow for a reference configuration composed with a learnable transformation that is polynomial in the parameters and factorized over them. This structure has a practical consequence: each parameter's effect is learned in isolation, from samples in which that parameter alone is varied. The combined response of many parameters is then recovered by summation at inference, without ever sampling their combinatorially large joint space. On a controlled problem with two interpretable deformations applied jointly to the data, the learned transformation reproduces the true deformations and matches the optimal likelihood, while optional interaction terms capture residual correlations when several parameters vary strongly at once. The resulting model is interpretable, scales linearly with the number of parameters, and keeps the likelihood tractable. This provides a general tool for any inference workflow requiring continuous density morphing, and directly enables the next generation of unbinned likelihood fits in high energy physics.

Raman-scattered emission features in symbiotic stars provide a powerful diagnostic of mass-loss and transfer processes, as they uniquely probe both ionized and neutral regions within interacting binaries. When resolved with high-resolution spectroscopy, these features encode detailed information on the physical properties of the neutral hydrogen medium. In this work, we present high-resolution spectroscopic observations of the symbiotic nova RR Telescopii obtained with FEROS in 2004 and GHOST in 2024, providing a $\sim$ 20 yr baseline. We report the clear detection of all three Raman-scattered He II lines at 6545 Å, 4851 Å, and 4332 Å, and constrain the distribution and kinematics of H I through line profile analysis. The three Raman lines exhibit distinct relative velocities, indicating that they trace different depths within the H I region. The Raman conversion efficiencies of the three Raman He II lines in 2024 are significantly lower than those in 2004, indicating substantial changes in the physical properties of the neutral hydrogen region. In addition, radiative transfer modeling implies a larger covering factor (opening angle) of the neutral region in 2004 than in 2024. These results indicate that the neutral hydrogen region cannot be characterized by a single H I column density, emphasizing the need for advanced radiative transfer modeling that accounts for the complex kinematics and geometry of the H I region. Overall, these results establish Raman-scattered He II lines as a powerful tool for spectroscopic tomography, allowing for direct constraints on the structure and kinematics of neutral hydrogen in symbiotic binaries.

Hit-level ML-based particle reconstruction methods have recently shown promising results. However, the reconstruction models are currently provided with targets that are unaware of the detector geometry and its resolution, resulting in training ambiguities. This can introduce a dependence on sample priors and reduce robustness under changes in event topology. We study the effect of a detector-aware target definition in the context of end-to-end Particle Flow reconstruction using a generic GEANT4-based detector simulation. We introduce the concept of detector-aware targets built from calorimeter showers with a hit-based merging algorithm based on cell-wise energy sharing that takes into account the spatial resolution of the detector. This includes a Particle-Flow-aware variant that preserves charged-particle consistency. Using a fixed GNN-based reconstruction model, we show that merged targets improve physics performance on a training-like sample. More importantly, models evaluated on an independent sample with different particle composition and topology show improved momentum response and resolution when trained with PF-aware merged targets. Our results show that removing experimentally non-resolvable target structure enhances not only reconstruction performance, but also improves model robustness against process-dependent variations in event topology.

Opposition control reduces skin-friction drag by opposing the wall-normal velocity on a near-wall detection plane, but the detection-plane velocity it requires is not available from wall-mounted sensors. Wall data can reconstruct inner-flow quantities accurately when assessed offline on a fixed flow state, and we ask whether such a reconstructed field can instead serve as a live surrogate sensor inside the feedback loop. We train a recurrent estimator to infer the detection-plane velocity from the two wall-shear-stress components in opposition-controlled turbulence. Offline it performs extremely well, reaching a correlation of 0.99 and near-unity coherence across the energetic scales; yet the same estimator fails in closed loop, decorrelating from the true field within a few viscous time units as the control collapses. The failure is not one of accuracy but of distribution shift induced by the controller itself: small closed-loop errors carry the flow off the attractor represented in the training data, while unresolved high-wavenumber errors enter through the wall boundary condition and return as out-of-distribution inputs. Standard remedies such as low-pass filtering and exponential averaging only delay numerical breakdown while accelerating decorrelation. Stable wall-only control is recovered by imposing spectral consistency on the deployed actuation and retraining the estimator on its own closed-loop data, giving a controller that holds much of the drag reduction of ideal opposition control from wall quantities alone. The obstacle is not whether the near-wall flow can be reconstructed offline, but whether that reconstruction stays dynamically consistent when allowed to modify the flow it senses.

We consider a junction consisting of an extended one-dimensional Kitaev chain which incorporates both time-reversal symmetry (TRS) breaking and long-range interaction, sandwiched between two metallic leads from two sides. In this hybrid device, we study electrical transport under voltage bias for varying strength of the TRS breaking phase. We compare the transport characteristics of long-range type Kitaev chain with that of the short-range Kitaev chain as the strength of the TRS breaking phase varies. We find that the TRS breaking modifies the density of states and localisation/delocalisation property of the eigenstates which in turn affect the transport characteristics. Moreover, we find that the impact of the TRS breaking is not identical for the long-range Kitaev chain and its short-range counterpart. Therefore, noticeable differences in the transport properties can be observed due to the interplay between the TRS breaking and the range of interaction.

Graphene auto-kirigami describes the thermodynamically self-driven tearing, sliding and folding of graphene sheets to form micrometre-scale, folded ribbons. However, this process typically requires specialised multi-axial nanoindentation systems or highly laborious AFM-based scratching methods. We here introduce a novel, scalable, wholly AFM-based method to nucleate high yields of ribbons in comparable timeframes to previous multi-axial indentation methods, by AFM-based indentation and "hard tapping", whereby high setpoint AFM imaging can nucleate, manipulate and dynamically image the auto-kirigami ribbons. This can be performed with any conventional AFM, enabling extensional growth, rotation and reversal of ribbons towards potential applications as NEMS devices.

We report on the first experimental verification of mode-independent entanglement. Commonly, the entanglement of a state is firmly based on pre-defined parties that are correlated, and the state might be disentangled when the definition of the parties is changed. Exceeding this party-dependent concept, we realize a type of quantum entanglement that persists even if the parties, in our case modes, are transformed. This safeguards the performance of entanglement in real-world applications, such as quantum communication settings involving noise and untrusted parties. For the state generation, we present an experimental scheme based on a fully reconfigurable temporally multiplexed interferometer with measurement-induced nonlinearities, which generates heralded two-photon states in two modes that are entangled for all choices of orthonormal mode basis. For the certification process, we utilize a tailored quantum-state tomography, achieving fidelities that validate the presence of mode-independent entanglement as a resilient and operationally advantageous quantum correlation.

It is well-known in integral geometry that a maximally symmetric Riemannian manifold, such as a static slice of vacuum AdS spacetime, can be perfectly covered by the geodesics in the Kinematic space, which we call the partial-entanglement-entropy (PEE) threads. In this context, the area of a codimension-one surface in the manifold can be computed by counting its intersections with the PEE threads, which is the celebrated Crofton formula. In this paper, we analyze the Kinematic space for a generic subregion in vacuum AdS space, and propose that the PEE threads emanate from a co-dimension one surface can perfectly cover a subregion in the manifold. Furthermore, we build holographic tensor network models on the network of the PEE threads confined in a subregion, thereby providing a concrete framework that realizes the surface-state correspondence and the generalized entanglement wedges for gravitational subregions.

We study heat rectification through a quantum two-level system asymmetrically coupled to two thermal baths, as described by the Ohmic spin-boson model. We evaluate the steady-state heat current using a tensor-network approach, which enables us to access the strongly correlated regime, and benchmark the results against analytical formulas in several limiting regimes, including the weak-coupling and incoherent-tunneling regimes. We identify a scaling regime where the studied system flows from an ultraviolet regime, at temperatures larger than the Kondo temperature, to an infrared regime, at temperatures lower than the Kondo temperature. By applying perturbation theory near the infrared fixed point, we find that the rectification ratio follows a universal power law. Our numerical results agree well with this analytical prediction. Our results provide a fundamental understanding of how dissipation-induced many-body physics affects heat transport.

Neural-network quantum states (NQS) provide expressive variational representations for strongly correlated quantum many-body systems, but their practical accuracy depends sensitively on architecture-level hyperparameters and optimization schedules. Here we develop NQS-Agent, an implemented open-source software framework for health-aware hyperparameter optimization (HPO) in NQS calculations. Its workflow monitors energy trajectories, detects destructive optimization events, stops unstable calculations, modifies the learning-rate schedule, resumes optimization from safe checkpoints, and ranks candidates with an anomaly-aware score. We demonstrate the approach on a residual convolutional NQS for the square-lattice Heisenberg $J_1$-$J_2$ model, using architectures with parameter counts comparable to aCNN, a convolutional NQS architecture used here as a reference. The results show that NQS-Agent improves over the reported human-tuned aCNN baseline for the aCNN reference architecture and identifies a structurally distinct wide-and-shallow competitive candidate within the parameter-count-matched residual-CNN search space. These results show that the stability and recovery history of an optimization trajectory should be considered when assessing an NQS result. Health-aware HPO therefore provides a reproducible tuning protocol that goes beyond selecting a single lowest-energy calculation.

Stable readout statistics are often taken as evidence for a well-defined physical response, but stability alone need not identify which state quantity has changed. We analyze this issue for finite collections of qubit states measured by binary readouts, focusing on changes in computational-basis population. The central question is when reproducible response data certify the sign or range of an underlying population change. We show that the answer is controlled by the calibrated measurement directions, not by loop consistency alone. For a fully calibrated finite readout family, we derive an exact closed-form interval of all compatible population changes. We also construct a same-record, jointly measurable example in which identical probabilities and accepted loop checks admit positive, zero, and negative population interpretations. When only a diagonal readout gain and a bound on coherence sensitivity are trusted, we obtain the sharp minimax interval and the necessary-and-sufficient sign condition $g>2\chi$. These results separate implementation stability from population identifiability and provide analytic benchmarks for qubit readout calibration.

Galaxy clusters provide constraints on cosmology through their abundance as a function of mass and redshift. Parameter inference from cluster counts requires modelling the covariance entering the likelihood, including contributions from Poisson shot noise and super-sample covariance (SSC) induced by long-wavelength density fluctuations. Since evaluating the full covariance during parameter inference can be computationally expensive, many analyses compute it at a fiducial cosmology and keep it fixed. In this work, we investigate the impact of covariance misspecification on the estimation of $\Omega_c$, $\sigma_8$, and $w$. We perform a systematic analysis in which the covariance is either varied consistently with the sampled cosmology or fixed at displaced cosmological models, including intermediate strategies where only selected components, such as SSC, are held fixed. Our analysis incorporates observational effects relevant for LSST-like optical surveys, including mass-proxy scatter and photometric redshift uncertainties. We find that the estimators of $\Omega_c$, $\sigma_8$, and $w$ remain unbiased even when the covariance is evaluated at an incorrect cosmology. However, fixing the covariance can significantly over- or underestimate confidence regions. The magnitude and sign of this effect are primarily driven by parameters controlling the amplitude of matter fluctuations. For LSST-like surveys, an inconsistent covariance specification can artificially modify the apparent $S_8$ tension inferred from cluster counts. We further show that a single covariance update evaluated at the recovered best-fit cosmology is sufficient to restore the correct uncertainty normalization. These results indicate that fixed-covariance approximations may be adequate for some single-probe analyses, but a fully cosmology-dependent treatment is required for consistent multi-probe studies. ABRIDGED

Large language models (LLMs) are increasingly used as agents in simulations of social systems, yet it remains unclear when their behavior can be interpreted as a faithful proxy for human decision-making. Here we test LLM agents against a direct empirical benchmark: a large-scale networked Prisoner's Dilemma experiment with human participants. Using the same interaction protocol, payoff structure, and network topologies, we compare nine open-weight LLMs with the human data. The selected model reproduces several macro-level features of cooperation dynamics, including the early decline and later stabilization of cooperation. This aggregate agreement, however, does not extend uniformly to finer levels of behavior. LLM populations underestimate individual-level heterogeneity and generate conditional cooperation patterns that differ from those observed in humans. Adding a fraction of random agents improves some aspects of micro-level agreement, but does not remove the mismatch in decision rules. These findings reveal a macro–micro dissociation in LLM-based social agents: collective outcomes can appear human-like even when the underlying behavioral distributions and mechanisms are not. They suggest that validating LLM agents as human surrogates requires comparisons across aggregate dynamics, individual heterogeneity, and context-dependent decision rules, rather than outcome-level agreement alone.

For a process in which a system is driven irreversibly from equilibrium state $A$ toward equilibrium state $B$, the free energy difference $\Delta F = F_B-F_A$ can be estimated using the work fluctuation theorem $\langle e^{-W/T}\rangle = e^{-\Delta F/T}$, where $W$ and $T$ denote work and temperature. The estimate often suffers from poor convergence with the number of trajectories used to calculate the average. Borrowing ideas from escorted free energy estimation, and from diffusion models of machine learning, we show how to construct infinitely many work-like quantities, $W_\theta$, that satisfy $\langle e^{-W_\theta/T}\rangle = e^{-\Delta F/T}$, for the same underlying dynamics. Our method involves a virtual control field ${\boldsymbol u}_\theta$ that does not modify these dynamics. We show how to choose parameter values $\theta$ to optimize convergence of the free energy estimate, for a fixed set of trajectories. We identify conditions under which our method provides a zero-variance estimator of $\Delta F$. We use numerical simulations of model systems to illustrate the gains in convergence that our method can achieve.

CARMApy is a new open-source python code that performs bin-scheme microphysical modeling of clouds in exoplanet atmospheres. It models key cloud properties such as particle size distributions and microphysical rates from first principles. The code is a wrapper of ExoCARMA, a well tested Fortran code with an almost half century long heritage. CARMApy includes the microphysical processes of homogeneous and heterogeneous nucleation, condensational growth, evaporation, coagulation, and vertical transport. CARMApy has 10 built-in default condensates and allows the user to specify additional condensates. In this work we describe CARMApy and the data products that it can generate, along with the history of its code heritage. We additionally compile a complete description of the theory and methods used in CARMA. Lastly we benchmark CARMApy and show that its results are consistent with previous versions of CARMA, while executing the code ~1.9 times faster single threaded ~3.8 times faster multithreaded.

We study the spatially homogeneous, massless Boltzmann equation in Minkowski spacetime for a certain range of hard and soft interactions. For hard interactions, we derive a Povzner-type inequality for massless particles and show that solutions exist for all time into the future. For soft interactions, we employ singular weights to control singularities at $ p = 0 $, which arise from the masslessness of particles, to obtain local existence. These results, which are among rather few proofs of existence for the massless Boltzmann equation, are motivated by our earlier work on the massless Einstein–Boltzmann system in certain cosmological settings.

We use the Banerjee–Mandal–Sahoo dipole-current Ward identity for the one-loop logarithmic soft-photon theorem as input and determine its finite-energy action on Mellin-difference hard currents. The commutator with such hard currents has a scheme-independent hard-hard residue that survives every one-particle redefinition. With the meromorphic continuation stated explicitly below, a two-particle Plancherel transform identifies this residue with an analytic two-particle primary module, and the coefficient map is a hard-current one-cocycle. The cocycle defines a minimal filtered abelian extension. It has a canonical two-particle primitive and integrates to an affine action. For scalar hard legs, the fixed-leg operator agrees coefficient by coefficient with the symmetry-governed long-range logarithmic tower of Choi, Kadhe, and Puhm. Applied to a tree-level scalar-QED photon-exchange block, the finite-energy analysis determines the logarithmic two-particle coefficient functional from the ordinary hard amplitude and the Banerjee–Mandal–Sahoo ordered-pair soft kernel. This gives a finite-energy relation between the Banerjee–Mandal–Sahoo dipole-current Ward identity and the exponentiated long-range celestial OPE.

We provide a re-formulation of the light-matter interaction of trapped-atom systems in terms of classical modulation theory. We introduce commuting “mean” quadrature operators together with “deviation” operators that describe the quantum fluctuations resulting from the uncertainty principle. From the “mean” position operator stems an accurate approximate expression for the internal transition coupling strengths in terms of Bessel functions which matches that of classical modulation theory. The error of the approximation is a direct result of quantum fluctuations. We also show that this result can also be obtained with WKB theory. The validity of our approach is numerically verified and supported by an expansion of the exact expression using a recurrence relation between orthogonal polynomials. Compared to the exact solution, our result is analytically more tractable, numerically more stable, and admits a transparent physical interpretation which connects the classical and quantum pictures.

In this comment we discuss some properties of the novel spacetime, recently found in [L. Ma, H. Lü, arXiv:2606.23782]. In particular, we draw attention to the background of this solution that the authors claim to be a new spacetime. We show that this is not the case because this background belongs to the special class of electrovacuum Kundt spacetimes of type D with either electric or magnetic charge. We show this by first analyzing the algebraic properties of this spacetime, and then by finding the explicit coordinate transformations. We hope that this analysis of the background allows for a better understanding of the structure of the general class of this type, namely as the Kerr-like black holes in the background generated by an accelerating electric or magnetic charge.

Quantum entanglement is one of the most intriguing features of quantum mechanics. To investigate the entanglement between two excitons in a biexciton, an experimental technique called density matrix spectroscopy (DMS) has recently been developed. DMS combines stimulated emission tomography and pump-probe techniques to obtain a time-resolved density matrix of the polarization state of a photon pair emitted from the biexciton. The reconstructed density matrix is expected to encode information about the biexciton state and its entanglement dynamics, but the precise nature of this connection has remained unclear. In this paper, we derive an analytical relationship between the density matrix obtained by DMS and the biexciton state. In addition, we perform numerical simulations to compare the entanglement dynamics obtained by DMS with the biexciton's entanglement dynamics in a two-dimensional electron-hole system using an extended ionic Hubbard model. We find that DMS can partially capture the entanglement in the biexciton, in particular, the dynamics of the difference $S_{\mathrm{bi}} - S_k$, where $S_{\mathrm{bi}}$ is the entanglement entropy of the biexciton and $S_k$ is the entanglement in terms of the wavevectors of the excitons that constitute the biexciton. These results demonstrate the validity of DMS for obtaining information about the entanglement dynamics of the biexciton.

Over the past decades, the operations research community has developed numerous effective optimization algorithms, yet quantum computing is emerging as a new computational paradigm with the potential to approach optimization problems more efficiently. Grover's algorithm offers a provable speedup for combinatorial optimization, but its circuit depth places it beyond current noisy intermediate-scale quantum (NISQ) devices. A more accessible alternative is to reformulate the optimization problem as a quadratic unconstrained binary optimization (QUBO) problem and apply quantum annealing; however, practical problem instances remain out of reach for existing hardware. We introduce AtomTreeSearch, a hybrid classical-quantum algorithm that integrates a quantum subroutine natively implementable on neutral-atom quantum computers within a Monte Carlo Tree Search framework. At each expansion step, a maximal weighted independent set of candidate actions provided by the quantum processor is selected, and these collective actions are performed to obtain a child node. We benchmark our method on the Traveling Salesman Problem, with instances of up to 60 cities on random Euclidean instances and up to 100 cities on TSPLIB instances. Our hybrid algorithm generally matches or outperforms both OR-Tools and simulated annealing on these instances, and we find that the quantum subroutine produces more diverse and higher-quality branches compared to classical alternate subroutines. These results suggest that carefully scoped quantum subroutines embedded in classical search frameworks represent a promising path toward near-term quantum utility in combinatorial optimization.

Minor mergers can induce sloshing motions in the intracluster medium, leaving characteristic signatures in the thermodynamic structure and gas kinematics of cluster cores. Abell 3571 is an X-ray-bright, apparently relaxed cluster at $z \sim 0.04$. We observed the central $\sim 300$ kpc region of Abell 3571 with four partially overlapping XRISM Resolve pointings, covering three contiguous Resolve fields to the north, south and east with a total exposure time of approximately 575 ks. The velocity dispersions are subsonic and are at the level of $\sim 100$–$150 \, \mathrm{km~ \, s^{-1}}$ across most regions. The cooler region associated with the northern surface-brightness excess is blueshifted by up to $\sim -60 \, \mathrm{km \, s^{-1}}$ relative to the brightest cluster galaxy (BCG), while the hotter region in the southern and eastern surface-brightness deficit regions is redshifted by up to $\sim 170 \, \mathrm{km \, s^{-1}}$. Numerical simulations suggest that this large-scale thermodynamic and kinematic asymmetry is broadly consistent with early-phase sloshing induced by an off-axis minor merger. Abell 3572, an X-ray-faint gas-poor cluster located 1.6 Mpc to the south, is a promising candidate for the perturber. Given the lack of clear signatures of prominent AGN feedback in Abell 3571, these results suggest that sloshing-driven gas redistribution may contribute to delaying the re-establishment of a strong cool core in Abell 3571.

Molten salts such as FLiBe (2LiF–BeF$_2$) are leading blanket materials for breeding and recovering tritium in fusion reactors. Predicting tritium speciation requires accurate electronic ground-state energies for representative molten-salt clusters, a demanding task for correlated electronic-structure methods. Here we report the first application of heterogeneous quantum–classical computing to tritium binding in FLiBe. Clusters drawn from ab initio molecular dynamics are partitioned by an embedded-wavefunction (EWF) method into atom-centered fragments, and the largest fragments are solved on IBM quantum hardware using extended sample-based quantum diagonalization (ext-SQD). Across nine clusters, the heterogeneous quantum–classical workflow reproduces fragment ground-state energies with agreement to full configuration interaction within 0.7~kcal/mol and a mean absolute deviation of 0.3~kcal/mol. In contrast, fragmented and unfragmented conformational energy differences and tritium binding energies differ by 12~kcal/mol and 110~kcal/mol on average, respectively, identifying fragment construction rather than fragment solution as the dominant source of algorithmic bias. To the best of our knowledge, this is the first such demonstration for a charged ionic system and in particular an inorganic molten salt, where electrostatic and polarization effects make the accurate treatment of electronic correlation particularly challenging. These results also identify areas of future research towards an accurate and scalable quantum–classical workflow to compute free-energy estimates of tritium speciation in fusion blankets.

Mechanical stresses are fundamental regulators in biological tissues, where the vertex model (VM) is pivotal for theoretical and force-inference studies. Yet, no uniform expression for the stress tensor exists for the VM. Here we provide a microscopic derivation of it, linking mesoscopic stresses to the VM forces. The stress field presents a freedom on how tensions are distributed across cells, which allows previous expressions to emerge as particular realizations of the field and suggests a link between mesoscopic stresses and cytoskeletal force-transmission architectures in real cells.

The universal scaling of critical behavior in phase transitions is a cornerstone of physics. Dynamical quantum phase transitions (DQPTs) are their nonequilibrium analogues: abrupt nonanalyticities that emerge as a quantum system evolves in time. Yet the hardness and cost of detecting this phenomenon remain largely unexplored. We prove that estimating DQPT to a certain precision is intractable even for quantum computers, whereas deciding a subsystem variant of DQPT is as hard as simulating generic quantum circuits, implying a provable exponential quantum advantage. Furthermore, to search for critical times of local DQPTs, we show a quadratically faster quantum algorithm that estimates observables of Hamiltonian dynamics at multiple time points with Heisenberg-limited precision and sublinear scaling in the number of time points. Moreover, through encoding classical evolution into quantum dynamics, our framework enables broader quantum speedups for detecting anomalous phenomena in classical systems.