Papers

Current all-sky surveys such as Vera Rubin's Legacy Survey of Space and Time and the Zwicky Transient Facility (ZTF) promise a wealth of scientific gains that are contained in the millions of astrophysical transient candidates produced each night. Kilonovae, one such transient of interest, will be challenging to identify in the alert stream and will require efficient artificial intelligence models to parse the large, real-time influx of data. In order to build these large models, comprehensive multimodal datasets are necessary for training. Due to a lack of numerous kilonova observations, we propose $\texttt{kilonova-multimodal-emulator}$ – a simulation pipeline for realistic, multimodal kilonova observations comprised of photometry, spectra, and images. We demonstrate this pipeline for ZTF-type observations, based on historical cadence and limiting magnitude information from the Bright Transient Survey (BTS) and using the latest radiative transfer kilonova models to produce a comprehensive dataset meant for the training of large artificial intelligence model.

Monopoles have been a subject of much theoretical and experimental research since they were proposed to symmetrize Maxwell's equations. No experimental signature of their existence has been detected. A model generalizing QED with dark massless photons and dark monopoles was proposed not long ago. In this model, by introducing a mixing interaction between the two sectors, leptons and monopoles are transformed into into dyons. This particular behavior of the electrons determines a new path to study the physics of monopoles. Here we generalize the Electroweak theory in the same way by incorporating a dark sector with massive photons and monopoles, and analyze experimental consequences for electrons in both models. The mass of the dark photon plays an important role regarding observability.

Tensor networks are powerful tools for simulating quantum many-body systems, but the growth of spatial entanglement severely limits the direct evolution of pure states under chaotic unitary circuits. Here we consider a more targeted task: the approximate strong simulation of a specified output probability of a 1D chaotic brick-wall circuit at finite relative precision. Given input and output bit strings $\by$ and $\bx$, we evaluate $p(\bx|\by)=|\bra{\bx}U(T)\ket{\by}|^2$ using the Sweeping RTM algorithm, a transverse tensor-network contraction based on reduced transition matrices (RTMs). The algorithm compresses the left and right temporal boundary states jointly, seeking an output probability that converges across spatial cuts and as the bond dimension is increased. For chaotic one-dimensional brick-wall circuits at a fixed relative target precision, we find numerical evidence that the bond dimension required to obtain stable estimates grows subexponentially over the accessible time window. Our findings open a direct route to classical probability queries for chaotic quantum circuits, with potential applications to benchmarking and learning tasks.

There has been a proliferation of sampling algorithms based on Wasserstein gradient flows (WGF) and forward-only diffusion processes (FODP), often accompanied by theoretical guarantees of exponentially fast convergence to the target distribution. These guarantees are frequently interpreted as evidence that such methods can efficiently sample complex multimodal distributions, often supported by empirical results. In this work, we argue that this interpretation is fundamentally misleading. By invoking the Jordan-Kinderlehrer-Otto (JKO) scheme and Otto calculus, we establish that the canonical WGF sampling dynamics and overdamped forward diffusion share the same density evolution and therefore inherit the same metastability and slow-mixing phenomena long understood in nonequilibrium statistical physics. We analyze this family of samplers using two complementary tools – spectral analysis and mean first-passage time (MFPT) analysis – and show that well-separated multimodality can induce exponentially long mixing times associated with small spectral gaps and rare inter-mode transitions. For the commonly adopted log-linear annealing schedule studied here, we find that introducing intermediate distributions does not remove the exponential scaling of the total transport time. The limitation is structural rather than implementation-specific: purely local, gradient-driven transport mechanisms can require exponentially long times to transport probability mass across well-separated modes. We argue that this represents a fundamental limitation of WGF- and FODP-based sampling in their standard forms, and motivates future development of fundamentally nonlocal mechanisms for efficient multimodal sampling.

Epitaxial quantum dots (QDs) are widely recognised as one of the best quantum light sources, given their good stability, brightness, quantum efficiency and coherence. To reach such properties, QDs are protected from potentially detrimental surface states by a relatively thick capping layer, typically exceeding 50nm. This prevents the implementation of near-field effects, like plasmonic-based ones, that can dramatically increase the light-matter interaction, since the control of the spontaneous emission dynamics and directionality of the emission can only occur if the emitter is in close proximity (typically tens of nanometers or less) from the metallic structures. In this work, we report the growth and optical characterisation of InAs/GaAs QDs with GaAs capping layer thickness of 10nm, 20nm, and a more standard 95nm. Remarkably, we observe emission linewidths up to 5 times smaller than previously reported, with unperturbed excitonic lifetimes. These results demonstrate that the epitaxial QD's high optical quality can be maintained even when the emitters are at reduced distances from the surface, opening the path for the exploration of near-field light-matter interactions with coherent and stable emitters, suitable for quantum technology applications.

We study the transverse field Ising model, defined by the Hamiltonian $H =\frac{1}{2}\sum_{i, j\in [n]} J_{ij} Z_i Z_j +\sum_{i=1}^n h_i^z Z_i + \eta\sum_{i} X_i$ where $J $ is the symmetric interaction matrix, and $\eta$ is the transverse field strength. Let $\Delta(J)=\lambda_{\max}(J)-\lambda_{\min}(J)$ be the spectral width of $J.$ When the inverse temperature $\beta\geq0$ satisfies $\Delta(J)\cdot\frac{\tanh(\beta\eta)}{\eta}\leq1$, we give a randomized classical algorithm that approximates the partition function $Z(\beta)=\operatorname{Tr}(e^{-\beta H})$ to a given relative error $\epsilon\in(0,1)$ in time polynomial in $n$, $\beta$, the model parameters, and $\epsilon^{-1}$. When $ \Delta(J) \cdot \frac{\tanh(\beta \eta)}{\eta} > 1 ,$ we show that approximating $ Z(\beta)$ within an $\exp(o(n))$-multiplicative factor is $\textbf{NP}$-hard, and thus unlikely to admit an efficient classical or quantum algorithms under standard complexity theoretic assumptions. Furthermore, in the regime $\Delta (J)\cdot \frac{\tanh(\beta \eta)}{\eta}\leq 1,$ we provide an efficient randomized classical algorithm that approximates Pauli string observables of the Gibbs state $ \rho_\beta = \frac{e^{-\beta H}}{\operatorname{Tr}(e^{-\beta H})}$ within an arbitrarily small additive error. In the special case when the observable is also diagonal in the $X$-basis, i.e. $P \in \{I, X\}^{\otimes n}$, the algorithm further achieves arbitrarily small relative error.

Fermionic non-Gaussianity is a resource for universal quantum computation that can be generated by interactions in quantum many-body systems. Using the magic Rényi entropy (MRE) as a measure of non-Gaussianity, we derive its universal upper bound and Haar mean, proving that typical Haar-random states attain the maximal MRE density of $\ln(4/3)$ per Majorana in the large-system limit. We also show that the MRE can differ extensively between states with identical covariance matrices. To investigate how non-Gaussianity grows toward this maximal density, we evolve a Gaussian state in imaginary and real time under the Sachdev-Ye-Kitaev Hamiltonian. By varying the imaginary- and real-time durations, we identify a first-order transition marked by a kink in the MRE density and spontaneous breaking of the permutation symmetry among the four copies used to evaluate the MRE. This transition represents a qualitative change in the non-Gaussianity of the state that is not reflected in the thermal free energy.

Rare weather regime transitions pose a challenge for data-driven modeling due to class imbalance. In this study, we develop a probabilistic deep learning emulator for a prototypical system with regime transitions, the stochastic Holton–Mass model of stratospheric variability, and analyze the structure of its learned latent space. The Holton–Mass model exhibits two metastable regimes, a strong and a weak polar vortex, maintained by nonlinear wave–mean flow interactions, with weak stochastic forcing intermittently triggering rare transitions between these regimes that qualitatively represent SSW events. We employ a ResNet-inspired Conditional Variational Autoencoder with six-layer encoder and decoder layers and explicit current-state conditioning to model the distribution of the system's state at the next time step (one day). The emulator accurately reproduces short-term dynamics, steady-state probability distributions, regime persistence statistics, rare transition rates, the transition committor function, and the transition expected lead time of the physical model. Beyond emulation fidelity, we interrogate the learned latent representation to understand how the model internalizes the underlying metastable structure of the dynamics. Principal Component Analysis of the 32-dimensional latent space reveals a clear and unsupervised separation into four physically interpretable clusters corresponding to strong versus weak vortex regimes and stable versus transition-prone configurations. Such emergent regime separation in latent space is hard to identify for deep generative models applied to high-dimensional stochastic systems. Our results show that carefully designed probabilistic emulators can uncover physically meaningful manifolds governing extreme-event dynamics, potentially aiding the development of improved operational advanced warning systems.

How complex can the responses of a quantum device become as it runs longer with a fixed internal memory? We quantify this complexity through sequential response capacity: how many adaptive testing stages, each using a fresh run, can continue to separate possible processes by a prescribed gap in response probabilities. For fixed system and memory sizes, we establish a tight law relating this capacity to run length and probability resolution. At fixed resolution, the capacity grows on the order of $K\log K$, where $K$ is the number of time steps in each run. Our construction attains this growth using time-dependent phase rotations on a single visible qubit with no additional internal memory; its tests give response probabilities exactly zero or one. Under the same tests, classical stochastic processes that measure in a fixed basis at every step have only linear capacity at fixed sizes and resolution. For phase sequences selected by a stored classical label, we then quantify how known independent Pauli noise changes this logarithmic enhancement. With ideal controls and weak residual phase noise after correction, we prove matching capacity bounds at a fixed small probability gap. These bounds identify the inverse residual phase-flip probability as the coherence timescale that limits the extra logarithmic growth.

The critical Ising model admits several natural scaling limits. For an Ising model on a lattice approximation $D^a$ of a planar domain $D$, the rescaled spin field converges, as $a\to0$, to the critical Ising magnetization field (IMF), a rough random distribution on $D$, while the spin interfaces converge to a nested $CLE_3$ and the FK–Ising representation converges to a colored $CLE_{16/3}$. In this work, we prove that the IMF can be directly reconstructed from the nested $CLE_3$. This complements the previously known construction of the IMF from the colored $CLE_{16/3}$ [CGN15]. Our result also implies that %We further show that if one constructs a colored $CLE_{16/3}$ from the nested $CLE_3$ through CLE percolation [MSW17], the resulting construction recovers the same magnetization field. A main difficulty in this present reconstruction comes from the fact that the renormalization exponent of the IMF, $15/8$, is smaller than the Hausdorff dimension of the $CLE_3$ carpet, $187/96$. Consequently, the field cannot be recovered by a direct Minkowski-content construction from the nested $CLE_3$ as is the case for $CLE_{16/3}$. We conjecture that the converse measurability also holds, namely that the nested $CLE_3$ can itself be reconstructed from the IMF.

An outstanding question in holographic superconductivity is how rotation and non-relativistic scaling symmetries compete to modify the onset of condensation. We address this by comparing the probe superconductor sectors of two exact rotating axionic black holes: a four-dimensional asymptotically anti-de Sitter solution dressed by a non-minimally coupled scalar, and a five-dimensional asymptotically Lifshitz solution with $z \in (2,3)$. Writing both metrics in a common stationary form yields a master scalar equation in which rotation enters through the co-rotating potential $A_t-N^xA_x$. This equation explains why the temporal and spatial components of the probe gauge field cannot be treated independently. The numerical profiles reported in [1,2] indicate that the correlated deformation associated with rotation reduces the condensate amplitude in both backgrounds, whereas increasing the dynamical exponent at fixed $J$ enhances the Lifshitz condensate. Since the parameters $\alpha$ and $J$ also modify the matter fields supporting the corresponding exact geometries, these trends should be interpreted as properties of complete correlated families rather than as universal effects of mechanical rotation alone.

X-ray observatories introduce unwanted instrumental effects into photon count data that must be accurately accounted for. In particular, the spatially variant point spread function (PSF) and shot noise pose a non-trivial inverse problem. We apply Bayesian inference grounded in information field theory (IFT) combined with a fast spatially variant PSF representation to infer the posterior mean and uncertainty of the X-ray flux under a minimal set of physically motivated prior assumptions, thereby removing or reducing these instrumental effects. First, ignoring spatial PSF variability, the IFT-based deconvolution is benchmarked on a synthetic example against three variants of the Richardson-Lucy (RL) algorithm using SSIM, RMSE, and NLL as evaluation metrics. Second, the ability of an interpolated patch-based convolution scheme to accurately represent the complex, spatially variant Chandra PSF is assessed on simulated PSF data. Finally, deblurring and spatially variant PSF representation are combined and applied to real Chandra observations of the supernova remnant Cassiopeia A. The IFT-based deconvolution outperforms all tested RL variants on the synthetic benchmark. The patch-based convolution scheme accurately recovers the complex structure of the Chandra PSF. Applied to Cassiopeia A, the reconstructed image appears visually sharper than the exposure-corrected data, and the data residuals are consistent with pure noise, indicating an accurate model of both the instrument and the sky. A comparison with a highly resolved reference dataset confirms this impression. The presented framework enables the removal of spatially variant PSFs from noisy data and opens avenues for future work on cross-calibration between multiple observations or different X-ray instruments, building on the principled uncertainty quantification inherent to the Bayesian framework.

The eight-component relativistic wave equation for spin-$\frac{1}{2}$ particles of Robson and Staudte (FV$\frac{1}{2}$), a Feshbach–Villars linearization of the Feynman–Gell-Mann equation, reproduces the Dirac hydrogenic spectrum with a doubled solution space and an indefinite inner product. Its exact Stark shifts must equal the Dirac ones; whether its indefinite-metric perturbation theory delivers them is not guaranteed. We compute the first- and second-order Stark shifts of the $n=1$–3 levels of hydrogenic ions from H to U$^{91+}$ in the Schrödinger, Dirac and FV$\frac{1}{2}$ theories, evaluating all spectral sums, continuum included, by the Dalgarno–Lewis method. The FV$\frac{1}{2}$ and Dirac shifts coincide level by level, to working precision at first order (closed-form roots) and to 45 significant figures at second order. Both ingredients are needed: without the explicit spin–field coupling the first-order roots become complex, and without the negative-norm states the $(Z\alpha)^2$ coefficient is wrong. Norm sign and parity are locked in the doubled space: a potential lifting the $2s_{1/2}$–$2p_{1/2}$ degeneracy needs its own spin–gradient term, and shifts placed by hand on the $\kappa$-labeled states give a spurious imaginary linear Stark shift. The relativistic linear Stark effect is smaller by finite factors, but a second-moment sum rule shows the non-relativistic linear Stark strength is conserved, the missing part reappearing in singular $(Z\alpha)^{-2}$ second-order terms. For levels with a fine-structure partner, an exact intra-shell/regular split of the second-order shift gives the $(Z\alpha)^0$ coefficients analytically and relates the $\pi^2$ terms of the $(Z\alpha)^2$ coefficients to the singular ones. The Dirac weak/strong-field crossover for $n=2$ of hydrogen lies near 2.8~kV cm$^{-1}$, and the perturbative window set by the fine structure grows as $Z^5$.

A new method of analyzing grid, or aliasing, instabilities is described, in particle-in-cell (PIC) codes for plasma kinetic theory. It starts with a meshfree approach with $N_{p}$ macroparticles, with $N_{p}<\infty$. Linearization of these equations is done about a uniform density non-drifting equilibrium prescribed by macroparticles on a uniform lattice. Macroparticle positions perturbations are induced about this lattice. The resulting linear equations are analyzed for stability and dispersion relations. The linearized equations are then discretized on a grid of $N_{g}$ points with $N_{g} \le N_{p}$, in both momentum conserving (MCP) and energy conserving (ECP) formulations. For a cold stationary plasma, this leads a dynamical matrix. For ECP, the matrix is symmetric and positive definite (SPD) and immediately shows stability. For MCP, the dynamical matrix is neither symmetric nor positive definite, preventing immediate conclusions on stability. For both MCP and ECP and for a cold stationary plasma, the resulting matrix elements vary relative to the meshfree value, a result of aliasing. This deviation is periodic in the displacement of the particle lattice relative to the grid. In the MCP discretization, eigenvalues occur in complex conjugate pairs for general placement of the particle lattice relative to the grid. These conjugate pairs indicate nonnegative growth rates. The nature of the aliasing is studied and shown to be related to the trapezoidal rule error over the grid. The scaling of the linear growth rates with respect to $N_{g}$ and especially with respect to $N_{ppc}$ is studied. These analytical results are compared with PIC simulations and found to be in excellent agreement. The connection with a cold drifting beam is discussed briefly, specifically, grid-induced instabilities for both the MCP and ECP.

Deciding whether an unknown quantum state either belongs to, or is far from, a given family of states is a natural question of quantum information theory. In many such cases, there is a natural 2-copy test which always accepts whenever the state indeed belongs to the target family; it is often much more difficult, however, to bound the probability of acceptance for states that are far from the family. Here we develop a simple new technique for upper bounding this probability for general families, and therefore upper bounding the sample-complexity of the decision problem itself. As a particularly striking example of this framework, we show that deciding whether an unknown pure $n$-mode state is fermionic Gaussian, or at least $\varepsilon$-far in trace distance from all Gaussian states, can be accomplished using the optimal number $\Theta(\varepsilon^{-2})$ of copies. Remarkably, our analysis applies to the well-known Bell sampling procedure for testing fermionic Gaussianity, which we therefore show to be optimal, even compared to protocols making arbitrary collective and adaptive measurements. As a second example, we show that the analogous decision problem for the family of Slater determinant states can also be solved with mode-independent sample-complexity.

Consider the following optimization problem over stabilizer product states: given an $n$-qubit stabilizer state $\left|\psi\right\rangle$ and a set of single-qubit stabilizer states $S$, maximize $\left|\langle \psi | \phi_1, \ldots, \phi_n \rangle\right|^2$ over single-qubit stabilizer states $\left|\phi_i\right\rangle \in S$. By varying the set $S$, we show that solutions to this problem can be useful in a variety of settings: tighter runtime bounds for certain classical simulation algorithms; measures of entanglement; and the complexity of low-rank matrix completion. Moreover, we give a complete complexity classification of this nearest stabilizer product state problem. After accounting for the symmetries in the Clifford group, there are $9$ distinct possible sets $S$, and we show that all but the two simplest of these are $\NP$-complete.

Let $G$ be a simply connected semisimple algebraic group. On the (undecorated) braid variety associated with a power of a Coxeter element, there is a finite-order autoequivalence, called the Zamolodchikov transformation, induced by cyclic rotation of words. In this paper, we show that the coarse moduli space of the fixed-point stack of this autoequivalence is isomorphic to the spectrum of the fusion ring of the corresponding affine Lie algebra. The above results also extend to twisted Coxeter elements, and the fusion rings of all affine Lie algebras are obtained in this way.

We show that a pair of two-qubit product measurements can be perfectly distinguished when accessed jointly, yet cannot be perfectly distinguished when their local components are distributed between distant parties restricted to local quantum operations and classical communication (LOCC). This establishes a measurement analogue of nonlocality without entanglement that, unlike its state-based counterpart, emerges in a remarkably simple setting. We further uncover a hierarchy of LOCC discrimination strategies: adaptive protocols, in which the probe used at one site may depend on classical information obtained from another, can strictly outperform restricted non-adaptive protocols, as demonstrated by a set of six bipartite qutrit product measurements. We also show that LOCC distinguishability of bipartite measurements can be intrinsically directional, depending on which party initiates the protocol. More generally, we construct pairs of $n$-qubit product measurements that are globally perfectly distinguishable but cannot be perfectly distinguished across any bipartition, even when all parties on either side of the bipartition are allowed arbitrary joint quantum operations. Finally, we show how quantum data hiding based on quantum-process nonlocality without entanglement can be realized using elementary optical components.

Stabilizer states are central to quantum computing, underlying quantum error correction, benchmarking, and efficient classical simulation. Yet their learnability exhibits a striking gap: an $n$-qubit stabilizer state can be learned from $\Theta(n)$ copies using two-copy Bell measurements, whereas non-adaptive single-copy measurements require $\Omega(n^2)$ copies. Here we show that adaptivity completely closes this gap. We give a polynomial-time adaptive algorithm that learns an arbitrary $n$-qubit stabilizer state from $\Theta(n)$ single-copy Clifford measurements, matching the optimal sample complexity of Bell sampling without any multi-copy measurements. The same ideas yield a sample-optimal single-copy tolerant tester and, with $k$ qubits of quantum memory, the optimal testing tradeoff $\Theta(n-k+1/\varepsilon)$ at infidelity $\varepsilon$. Finally, we show that this adaptive mechanism extends beyond exact stabilizer states: states of stabilizer nullity at most $r$, including states prepared by Clifford circuits with a bounded number of $T$ gates, can be learned using $O(n2^{r})$ single-copy measurements.

We give a quantum algorithm for simulating a $d$-sparse Hermitian Hamiltonian $H$, assuming a known upper bound $\Lambda$ on its maximum column Euclidean norm $\|H\|_{1\to2}$. For $t\Lambda\ge1/2$, simulation with operator-norm error $\epsilon$ uses \[ O\!\left(t\Lambda\sqrt d+\sqrt d\log(2/\epsilon)\right) \] sparse-oracle queries. This removes the subpolynomial overhead in Low's algorithm [STOC 2019], replacing it with an additive logarithmic precision term. For $d>1$ and $t\Lambda\ge\log(2/\epsilon)$, the bound matches the worst-case lower bound. A known spectral-norm upper bound may also be used in place of $\Lambda$. The number of 1- and 2-qubit gates is linear in the query scale, up to oracle costs and polynomial overhead in the input bit lengths and logarithmic precision parameters. As applications, we obtain $O(\kappa\sqrt d\,\mathrm{polylog}(\kappa/\epsilon))$ queries for solving $d$-sparse quantum linear systems with $\|A\|\le1$ and $\|A^{-1}\|\le\kappa$, under standard sparse and state-preparation access. We also give a gate-efficient implementation of black-box unitaries with at most $d$ nonzero entries per row and column using $O(\sqrt d\log(2/\epsilon))$ queries, given sparse access to the unitary and its adjoint. At constant error, the query bound is optimal and yields $\Theta(\sqrt N)$ queries for arbitrary $N\times N$ unitaries, resolving the open question on black-box unitary implementation posed by Berry and Childs [QIC 2012].

Optimal circuit synthesis is the problem of finding the shortest-depth circuit representation of a given functionality with respect to a pre-specified elementary gate set. This is a central problem in both quantum and classical hardware design. While the classical version is very well understood – both in terms of heuristics and rigorous hardness assertions – much less is known about optimal quantum circuit synthesis. We focus on optimal Clifford circuit synthesis, for which various heuristics are known, e.g. via reduction to 3-SAT. Our main result supplies a matching hardness result: a variant of optimal Clifford synthesis is NP-hard. The proof proceeds in two parts: (i) reduce 3-edge colorability on 3-regular graphs to a circuit synthesis problem that only involves CZ gates, (ii) prove that the availability of additional elementary Clifford gates – most notably: Hadamard, phase and CNOT – cannot lead to further improvements of the optimal circuit depth. Our work sharpens the complexity-theoretic understanding of Clifford circuits: simulation and equivalence checking are in P, whereas deciding whether a Clifford unitary admits an implementation within a prescribed depth is NP-complete.

Blind extragalactic radio continuum surveys are increasingly conducted at sub-arcsecond angular resolutions. However, these surveys contain substantially more spurious sources than expected from purely Gaussian noise statistics. We investigate the origin of these false detections using the 10GHz GOODS-N mosaic obtained with the Karl G. Jansky Very Large Array (VLA), which reaches a native angular resolution of 0.22arcsec and exhibits a nearly Gaussian pixel-noise distribution. We show that part of the increase in the spurious-source fraction at high angular resolution arises naturally from the larger number of independent resolution elements, while residual wide-field imaging effects associated with the primary-beam response make only a modest contribution. Nevertheless, even after accounting for these effects, the observed fraction of false detections remains several times higher than predicted for Gaussian noise. An analysis of the noise autocorrelation function reveals that images with relatively sparse uv coverage retain significant dirty-beam sidelobes that introduce correlated noise fluctuations, substantially increasing the probability of false detections. We conclude that the excess of spurious sources is an intrinsic consequence of incomplete uv sampling rather than the imaging methodology itself, highlighting that future deep radio continuum surveys require not only high angular resolution but also sufficiently dense uv coverage to minimize spurious detections.

Layered viscoelastic interiors control the frequency dependence of planetary tidal dissipation, but repeated layered Love-number calculations are expensive in long-term orbital and spin integrations. Equivalent homogeneous rheologies provide a cheaper representation of the same response, but their effective parameters do not by themselves identify the interior layers they represent. We develop a local interpretation of such parameters by differentiating the layered-to-homogeneous construction about a fixed-geometry baseline, perturbing only selected layer rigidities and viscosities. We use the pole-residue representation of the degree-two Love number to construct a physical-to-effective transfer matrix. We then use its singular values, resolution matrix, and mode-dominance matrix to determine which physical perturbations are visible in the Love-number response and how they appear in effective-rheology coordinates. Applied to a five-layer lunar model, the full degree-two response is sensitive to seven of eight solid-layer rheological parameters. The single near-null direction is the inner-core rigidity, consistent with shielding by the fluid outer core and with the weak dependence of Love numbers on core elastic structure. Over the LLR frequency interval, fewer physical directions are visible. To first order, the elastic spring, isolated dashpot, and two Voigt elements reproduce the nine-pole response over this interval. Finite perturbations confirm the tangent prediction to within a few percent, with the validity radius limited by a nearly degenerate pair of long-timescale modes.

Typically, multiple copies of a quantum state are described by a tensor power of that state. However, from an operational point of view, an experimentalist cannot, in general, rule out imperfections at unknown positions or correlations between copies, so assuming independent and identically distributed (iid) copies is not justified a priori. In the following, we relax the iid assumption by considering a whole set of resource states that are fundamentally indistinguishable from a tensor power state, and in that sense look like many copies of the quantum state to the experimentalist. Using a de Finetti-type argument, we show that this operational set of states can asymptotically be represented by almost-iid states. As an application, we introduce operational analogues of asymptotic transformation rates, such as entanglement cost and distillation. This raises the question of whether these rates coincide with their iid counterparts.

Nonlinear nanophotonics enables the engineering of optical functions using light-matter interactions in nanoscale structures with carefully designed geometries. Phase matching in nonlinear optics is typically essential for realizing high efficiency for desired processes and engineering of sophisticated functions, but it can be challenging to reliably achieve in nanophotonic waveguides. Conventionally, phase matching in nanophotonics is achieved through nanofabrication, e.g., by poling a device's nonlinear material using electrodes with fixed geometry. This approach has two drawbacks: the optical functions a device can perform are fixed at the time of fabrication, and the device's performance can be degraded by fabrication imperfections. Here, we circumvent these limitations by engineering programmable $\chi^{(2)}$ holograms inside nanophotonic waveguides, enabling reconfigurable quasi-phase matching that can be updated after device fabrication in approximately one second. By projecting structured optical illumination onto photoconductive electrodes, we generated spatial patterns of electric-field-induced $\chi^{(2)}$ to exert spectral, modal, and polarization control over three-wave mixing processes. This programmability also allowed us to employ closed-loop in situ optimization to compensate for phase mismatch in complex waveguide structures, such as width-modulated waveguides and spiral waveguides exceeding 10 cm in length. Furthermore, we show that programmable nonlinearity enables powerful in situ diagnostics, including direct dispersion measurements of various transverse modes and on-chip wavefront phase tomography. Our work opens up the possibility of building cascaded nonlinear nanophotonic systems where yield issues due to fabrication variations can be sidestepped while retaining the benefits of etched waveguides such as confinement and dispersion engineering.

Equivariant machine learning interatomic potentials (MLIPs) have revolutionized atomistic modeling, but accurate treatment of complex materials and molecular systems demands expensive models. This limits simulation length- and time-scales, with tensor products a key computational bottleneck. The recent emergence of foundation-scale MLIPs further exacerbates this challenge. We present Branch Interatomic Potential (BranchIP), a single-model framework for learned adaptive tensor product computation, trained with a novel distillation loss. In our experiments on two systems of physical interest, a heterogeneous catalysis system and a proton-conducting solid acid electrolyte, BranchIP accelerates MLIPs across model sizes by up to $2.4\times$ while reducing memory usage by up to $2.6\times$. This is achieved while maintaining physical fidelity. Furthermore, the learned adaptive computation provides model interpretability by revealing which interactions demand deeper computation and showing how computational depth relates to chemical complexity and dynamics.

The layered honeycomb $\alpha$-RuCl$_3$ has been much explored as a candidate to display unconventional cooperative magnetism between spin-orbit entangled $j_{\rm eff}=1/2$ Ru$^{3+}$ magnetic moments stabilized by strongly-frustrated, Kitaev and other bond-dependent anisotropic interactions. Most studies so far have focused on relatively large single crystals that undergo a structural phase transition from monoclinic to rhombohedral upon cooling below 150 K and a magnetic transition to zigzag order around 7 K. Here we study the magnetism of high-quality, very small (sub 100 $\mu$m diameter) single crystals, which remain monoclinic upon cooling to low temperatures and display a sharp magnetic transition at 14 K. Using highly-sensitive piezo-cantilever magnetic torque measurements in fields up to 16 T, we report a comprehensive study of the magnetic phase diagrams for magnetic field rotated in three orthogonal crystallographic planes. Our extensive torque data collected upon varying almost continuously the angular orientation of the field as well as the field magnitude, allows clear detection of phase transitions via anomalies in the raw torque data and its higher order derivatives with respect to both angle and field magnitude. The obtained magnetic phase diagrams show many differences compared to rhombohedral samples, displaying reduced rotational symmetry, substantially higher fields required to suppress the spontaneous magnetic order, and distinct field-induced phases. We provide a direct comparison of the torque data with mean-field calculations for magnetic Hamiltonians appropriate for $\alpha$-RuCl$_3$ allowing for the monoclinic lattice symmetry and propose minimal magnetic structure models for all explored regions of the magnetic phase diagrams.

We turn metric reconstruction into an analytic solver for rotating black holes in modified gravity, bypassing the direct solution of coupled field equations. As an example, in parity-even cubic gravity, we construct the first analytic spinning black hole metric at linear order in the coupling, without spin or weak-field expansions. We stringently validate the field equations and reproduce independent analytic predictions for horizon properties. This establishes a new route to construct spinning black holes beyond Einstein gravity and lays the foundation for strong-field tests.

We show that a two-dimensional chiral fluid supports an antisymmetric contribution to the pressure, and determine its microscopic expression. Its origin lies outside the standard kinetic and collisional channels of the normal pressure tensor, stemming instead from the average of a parity-breaking pseudo-scalar. This chiral pressure is set by the rotational (vortex) viscosity of micropolar hydrodynamics, whose kinetic value for rough disks we compute here, to Gaussian order. Crucially, both the chiral pressure and the rotational viscosity survive at zeroth order in the gradients, meaning they persist even in the fully homogeneous fluid – which we show is not only well defined but steady (the Quiescent Chiral State, QCS). Since this averaged pseudo-scalar does not vanish in a chiral fluid, any expansion of its distribution function must contain terms in this pseudo-scalar variable.

An ideal tournament is both efficacious and attractive: it selects the best player as the winner, but only at the end of the tournament in order to maintain suspense as long as possible. We identify and explore the trade-off between these criteria in Swiss-system chess team tournaments via simulations. Organising more rounds is shown to improve efficacy but increase the probability of a stakeless last round. The 2008 reform in the Chess Olympiad ranking is found to be a questionable move, as the previous rule based on board points determines the efficacy-competitiveness frontier, regardless of whether two or three points are awarded for winning a match. Combining board points and match points is better than using match points only. Our results can help optimise the design of the Swiss system, which, besides chess, is also widely used in e-sports.