Introducing electric fields into density functional theory (DFT) calculations is essential for understanding electrochemical processes, interfacial phenomena, and the behavior of materials under applied bias. However, applying user-defined electrostatic potentials in DFT is nontrivial and often requires direct modification to the specific DFT code. In this work, we present an implementation for supercell DFT calculations under arbitrary electric fields and discuss the required corrections to the energies and forces. The implementation is realized through the recently released VASP-Python interface, enabling the application of user-defined fields directly within the standard VASP software and providing great flexibility and control. We demonstrate the application of this approach with diverse case studies, including molecular adsorption on electrified surfaces, field ion microscopy, electrochemical solid-water interfaces, and implicit solvent models.

We prove that for every countable string graph $S$, there is a planar graph $G$ with $V(G)=V(S)$ such that \[ \frac{1}{23660800}d_S(u,v) \le d_G(u,v) \le 162 d_S(u,v) \] for all $u,v\in V(S)$, where $d_S(u,v)$, $d_G(u,v)$ denotes the distance between $u$ and $v$ in $S$ and $G$ respectively. In other words, string graphs are quasi-isometric to planar graphs. This theorem lifts a number of theorems from planar graphs to string graphs, we give some examples. String graphs have Assouad-Nagata (and asymptotic dimension) at most 2. Connected, locally finite, quasi-transitive string graphs are accessible. A finitely generated group $Γ$ is virtually a free product of free and surface groups if and only if $Γ$ is quasi-isometric to a string graph. Two further corollaries are that countable planar metric graphs and complete Riemannian planes are also quasi-isometric to planar graphs, which answers a question of Georgakopoulos and Papasoglu. For finite string graphs and planar metric graphs, our proofs yield polynomial time (for string graphs, this is in terms of the size of a representation given in the input) algorithms for generating such quasi-isometric planar graphs. We further extend our techniques to show that every complete Riemannian surfaces $Σ$ of bounded Euler genus has a triangulation $G\subset Σ$ such that $G^{(1)} \hookrightarrow Σ$ is a quasi-isometry, where $G^{(1)}$ is the simplicial 1-skeleton of $G$.

We demonstrate that Liouvillian exceptional points (LEPs), previously explored only in continuous Lindbladian dynamics, also emerge in discrete brickwork completely positive trace-preserving (CPTP) circuits. By analytically solving a minimal two-qubit brickwork model, we identify the conditions under which discrete-time LEPs arise and show that they retain the hallmark square-root eigenvalue splitting and linear-in-time sensitivity enhancement. These results establish a direct bridge between continuous non-Hermitian physics and discrete quantum-circuit architectures, opening a path toward the realization of exceptional-point-based sensing on near-term quantum processors.

In computable analysis typically topological spaces with countable bases are considered. The Theorem of Kreitz-Weihrauch implies that the subbase representation of a second-countable $T_0$ space is admissible with respect to the topology that the subbase generates. We consider generalizations of this setting to bases that are representable, but not necessarily countable. We introduce the notions of a computable presubbase and a computable prebase. We prove a generalization of the Theorem of Kreitz-Weihrauch for the presubbase representation that shows that any such representation is admissible with respect to the topology generated by compact intersections of the presubbase elements. For computable prebases we obtain representations that are admissible with respect to the topology that they generate. These concepts provide a natural way to investigate many topological spaces that have been studied in computable analysis. The benefit of this approach is that topologies can be described by their usual subbases and standard constructions for such subbases can be applied. Finally we discuss a Galois connection between presubbases and representations of $T_0$ spaces that indicates that presubbases and representations offer particular views on the same mathematical structure from different perspectives.

How galaxies drive reionization and what governs its geometry remain fundamental questions. We present JWST/NIRCam wide-field slitless spectroscopy (WFSS) observations toward two of the most Ly$α$-transmissive QSO sightlines near the end of reionization. We find that regions at $z \sim 5.7$ along both sightlines previously found to be low-density in Ly$α$ emitters are also underdense in [O III] emitters, with densities less than half the cosmic mean. Other transmissive regions, however, are found to coincide with average-density environments, indicating that multiple pathways may produce high IGM transmission. For the first time, we measure the two-dimensional cross-correlation between IGM transmission and galaxy positions, revealing evidence for anisotropic ionization geometry. Specifically, we detect enhanced transmission at transverse distances of $Δr \sim 0.8$ times the mean free path, consistent with ionizing photons escaping preferentially along large-scale structures that are aligned with, but offset from, the line of sight. This anisotropic escape may contribute to the observed patchiness of reionization and challenges the assumption of isotropic ionized bubble growth in current models.

Limits on the dark matter fraction of small mass primordial black holes from Hawking radiation are predominantly derived from the assumption of a Schwarzschild black hole evaporating. However, astrophysical black holes are usually much more realistically modelled by the rotating Kerr black hole solution. Meanwhile, electromagnetically charged black holes are astrophysically of little importance due to their fast neutralisation in the present universe. Dark matter is not just a possible solution to issues of astrophysics and cosmology, but also to issues of the standard model of particle physics. Extensions of this model thus can lead to charges present in the early universe which remain preserved in the charge of primordial black holes - even when the corresponding particles have disappeared from the particle content of the present epoch of the universe. Here, we report on a thorough proof-of-concept that such charges can greatly change evaporation limits for primordial black hole dark matter. Special emphasis is placed on (near-)extremal black holes, for which this effect is especially pronounced.

We show that, contrary to some recent claims, relativistic effects cannot mimic dark matter in the galactic rotation curves and gravitational lensing.

In a recent paper we discussed when it is possible to define reference frames nonrotating with respect to distant inertial reference objects (extension of the IAU reference systems to exact general relativity), and how to construct them. We briefly review the construction, illustrating it with further examples, and caution against the recent misuse of zero angular momentum observers (ZAMOs).

Predicting the superconducting transition temperature ($T_c$) from crystal structure and composition remains a central challenge in condensed-matter physics, reflecting the absence of a broadly predictive framework connecting microscopic bonding to macroscopic quantum behavior. Here, we introduce a structure- and chemistry-aware approach implemented in an interpretable Gaussian process model, which we call GP-$T_c$ (Gaussian Process $T_c$), that enables uncertainty-quantified prediction of superconductivity from experimentally accessible inputs. By encoding local bonding environments and geometry as graphlet histograms and learning within a probabilistic framework, we find that the predictive space collapses to a compact set of descriptors: the distribution of electron-affinity differences between neighboring atoms, together with simple elemental features and interatomic distances, provides an informative basis for predicting $T_c$ across disparate superconducting families. This result identifies an overlooked chemical control parameter while emphasizing the essential role of local structure beyond composition-only approaches. We demonstrate the framework through two complementary tests: validation against a recently established superconducting family and discovery of a previously unknown material. GP-$T_c$ reproduces the experimentally reported $T_c$ range of the infinite-layer nickelate Nd0.8Sr0.2NiO2. We further predict superconductivity in stoichiometric PtPb$_3$Bi and experimentally confirm it through synthesis and bulk measurements, establishing PtPb$_3$Bi as a new superconductor with $T_c$~3 K. GP-$T_c$ identifies additional high-priority superconducting candidates -- including SrNiO2, K(PRh)2, and Ho2C3 -- that provide concrete targets for ongoing and future experimental exploration.

We propose a scheme to implement Heisenberg-limited spin squeezing in a hybrid cavity optomechanical-spin system. In our system, $N$ two-level systems are coupled via Tavis-Cummings interactions to a mechanical resonator (MR) in a standard optomechanical setup. Within the dispersive coupling regime, adiabatic elimination of the optical mode induces a squeezing effect on the MR, which, in the squeezed representation, effectively transforms the collective spin operators into a Bogoliubov form. Under large detuning conditions, the phonon mode mediates interactions among the Bogoliubov collective spins, thereby enabling a two-axis twisting squeezing protocol through appropriate parameter tuning. Both theoretical analysis and numerical simulations show that in the presence of dephasing and phonon dissipation, the maximum squeezing degree asymptotically converges to a constant as $N$ increases, which implies the metrological precision asymptotically approaches the standard quantum limit without parameter optimization. Nevertheless, in parameter optimization we extract a scaling relation of the optimal squeezing which surpasses existing schemes in the literature. Moreover, the optimization also leads to a considerable reduction of the preparation time for the optimal squeezing. Our work may provide insights into dissipation effects in spin squeezing and offer a potential route for high-precision quantum metrology in many-body systems.

Let $μ$ be a probability distribution on a multi-state spin system on a set $V$ of sites; equivalently, a $d$-partite simplicial complex with distribution $μ$ on maximal faces. For any pair of vertices $u,v\in V$, define the pairwise spectral influence $\mathcal{I}_{u,v}$ as follows. Let $σ$ be a choice of spins $s_w\in S_w$ for every $w\in V\setminus\{u,v\}$, and construct a matrix in $\mathbb{R}^{(S_u\cup S_v)\times (S_u\cup S_v)}$ where for any $s_u\in S_u, s_v\in S_v$, the $(us_u,vs_v)$-entry is the probability that $s_v$ is the spin of $v$ conditioned on $s_u$ being the spin of $u$ and on $σ$. Then $\mathcal{I}_{u,v}$ is the maximal second eigenvalue of this matrix, over all choices of spins for all $w\in V\setminus\{u,v\}$. Equivalently, $\mathcal{I}_{u,v}$ is the maximum local spectral expansion of links of codimension $2$ that include a spin for every $w \in V \setminus \{u,v\}$. We show that if the largest eigenvalue of the pairwise spectral influence matrix with entries $\mathcal{I}_{u,v}$ is bounded away from 1, i.e. $λ_{\max}(\mathcal{I})\leq 1-ε$ (and $X$ is connected), then the Glauber dynamics mixes rapidly and generate samples from $μ$. This improves/generalizes the classical Dobrushin's influence matrix as the $\mathcal{I}_{u,v}$ lower-bounds the classical influence of $u\to v$. As an application, we prove that the Glauber dynamics mixes rapidly up to (approximately) the phase transition for the multi-state hardcore model--a widely studied model in telecommunication networks and statistical physics (generalizing the hardcore model) introduced by Mazel and Suhov. As a by-product of our results, we also prove improved/almost optimal trickle-down theorems for partite simplicial complexes. Our proof builds on the trickle-down theorems via $\mathcal{C}$-Lorentzian polynomials machinery recently developed by the authors and Lindberg.

Can sustained open-ended technological progress preserve natural resources in a finite planet? We address this question on the basis of a stylized model with genuine open-ended technological innovation, where an innovation event corresponds to a random draw of a technology in the space of the parameters that define how it impacts the environment and how it interacts with the population. Technological innovation is endogenous because an innovation may invade if it satisfies constraints which depend on the state of the environment and of the population. We find that open-ended innovation leads either to a sustainable future where global population saturates and the environment is preserved, or to exploding population and a vanishing environment. What drives the transition between these two phases is not the level of environmental impact of technologies, but rather the demographic effects of technologies and labor productivity. Low demographic impact and high labor productivity (as in several western countries today) result in a Schumpeterian dynamics where new "greener" technologies displace older ones, thereby reducing the overall environmental impact. In this scenario, global population saturates to a finite value, imposing strong selective pressure on technological innovation. When technologies contribute significantly to demographic growth and/or labor productivity is low, technological innovation runs unrestrained, population grows unbounded, while the environment collapses. As such, our model captures subtle feedback effects between technological progress, demography and sustainability that rationalize and align with empirical observations of a demographic transition and the environmental Kuznets curve, without deriving it from profit maximization based on individual incentives.

Large thermal fluctuations of the liquid phase obscure the weak macroscopic electric field that drives electrochemical reactions, rendering the extraction of reliable interfacial charge distributions from ab initio molecular dynamics extremely challenging. We introduce SMILE-CP (Scalar Macro-dipole Integrated LEarning - Charge Partitioning), a macro-dipole-constrained scheme that infers atomic charges using only the instantaneous atomic coordinates and the total dipole moment of the simulation cell - quantities routinely available from standard density-functional theory calculations. SMILE-CP preserves both the global electrostatic field and the local potential without invoking any explicit charge-partitioning scheme. Benchmarks on three representative electrochemical interfaces - nanoconfined water, Mg2+ dissolution in water, and a kinked Mg vicinal surface under anodic bias - show that SMILE-CP eliminates the qualitative errors observed for unconstrained charge decompositions. The method is computationally inexpensive and data-efficient, opening the door to charge-aware machine-learning potentials capable of bias-controlled, nanosecond-scale simulations of realistic electrochemical systems.

We study a class of nonlocal conformal field theories in two dimensions which are obtained as deformations of the Virasoro minimal models. The construction proceeds by coupling a relevant primary operator $ϕ_{r,s}$ of the $m$-th minimal model to a generalized free field, in such a way that the interaction term has scaling dimension $2-δ$. Flowing to the infrared, we reach a new class of CFTs that we call long-range minimal models. In the case $r=s=2$, the resulting line of fixed points, parametrized by $δ$, can be studied using two perturbative expansions with different regimes of validity, one near the mean-field theory end, and one close to the long-range to short-range crossover. This is due to a straightforward generalization of an infrared duality which was proposed for the long-range Ising model ($m = 3$) in 2017. We find that the large-$m$ limit is problematic in both perturbative regimes, hence nonperturbative methods will be required in the intermediate range for all values of $m$. For the models based on $ϕ_{1,2}$, the situation is rather different. In this case, only one perturbative expansion is known but it is well behaved at large $m$. We confirm this with a computation of infinitely many anomalous dimensions at two loops. Their large-$m$ limits are obtained from both numerical extrapolations and a method we develop which carries out conformal perturbation theory using Mellin amplitudes. For minimal models, these can be accessed from the Coulomb gas representations of the correlators. This method reveals analytic expressions for some integrals in conformal perturbation theory which were previously only known numerically.

We introduce a variational quantum computing approach for quantum state reconstruction within a discretized logical framework, using experimental measurement data as input. By mapping the reconstruction cost function onto an Ising model, the problem can be solved using a variational eigensolver on present-day quantum hardware identifying dominant logical elements of the density matrix. As a proof of concept, we demonstrate the method on quantum structured light, in particular, entangled photons carrying orbital angular momentum and show that the reconstruction procedure can yield reliable performance even on noisy devices. Our results highlight the potential of variational algorithms as a complementary approach to quantum state tomography, particularly for high-dimensional structured light, where classical approaches can face bottlenecks.

We study the notion of semistability for principal bundles over curves with possibly disconnected reductive structure group. We establish a new characterization of the behavior of semistability under change of group, novel even in the connected case, and prove that all existing notions of semistability are equivalent, thus settling a question by Biswas-Gomez. The key ingredients for our results include a study of cocharacters and characters of disconnected linear algebraic groups, and an extension of the recursive description of Kirwan stratifications in Geometric Invariant Theory to the case of disconnected groups.

We present sharp estimates for the extremal eigenvalues of the Schur complements arising in saddle point problems. These estimates are derived using the auxiliary space theory, in which a given iterative method is interpreted as an equivalent but more elementary iterative method on an auxiliary space, enabling us to obtain sharp convergence estimates. The proposed framework improves or refines several existing results, which can be recovered as corollaries of our results. To demonstrate the versatility of the framework, we present various applications from scientific computing: the augmented Lagrangian method, mixed finite element methods, and nonoverlapping domain decomposition methods. In all these applications, the condition numbers of the corresponding Schur complements can be estimated in a straightforward manner using the proposed framework.

We study the shipper-side design of large-scale inbound transportation networks, motivated by the global supply chain of the carmaker Renault. We formalize the Shipper Transportation Planning Problem (STPP), which integrates discrete flow consolidation via explicit bin-packing, time-expanded routing, and operational regularity constraints. To solve this high-complexity combinatorial problem at an industrial scale, we propose a tailored Iterated Local Search (ILS) metaheuristic. The algorithm combines large-neighborhood search with MILP-based perturbations and leverages bundle-specific decompositions to obtain scalable lower bounds and effective search guidance. Computational experiments on real industrial data involving more than 700,000 commodities and 1.2 million arcs demonstrate that the ILS achieves an average gap of 7.9% to the best available lower bound. The results reveal a 23.2% cost-reduction potential compared to legacy planning benchmarks. Most significantly, the proposed framework is currently deployed in production at Renault, where it supports weekly strategic decisions and generates realized cost savings estimated at approximately 20 million euros per year. Our analysis yields key managerial insights: we demonstrate that explicit 1D bin-packing is a critical step forward for realistic consolidation modeling, that transport regularity offers a robust balance between cost and stability, and that high-volume global networks benefit significantly from in-house strategic planning over third-party outsourcing. To the best of our knowledge, this is the first work to successfully solve a shipper-side transportation design problem at this magnitude.

We introduce a sharpness functional for probabilistic models that quantifies sharpness as an intrinsic property of the probability distribution. The measure is derived based on a rank-based concentration principle that tracks upward transfers of probability mass along the rearranged profile of the predictive distribution. For finite outcome spaces, this yields a normalized sharpness measure with transparent mass--length representation and equivalent formulations as a Gini-type coefficient on the probability vector and a scaled 1-Wasserstein distance from the uniform distribution in rearranged space. We extend the functional to bounded continuous and multidimensional domains for predictive distributions with finite first moment, and establish normalization, symmetry, continuity, and monotonicity properties. The diagnostic application of the measure is illustrated with real and simulated data, and a relationship to the multivariate energy score is discussed.

Background and context: Debugging is a significant and often frustrating challenge for beginner programmers. Understanding students' debugging behaviours and strategies can help to identify common difficulties and inform approaches for alleviating these. Currently, there are limited studies of school students' debugging behaviour in a text-based programming language, a medium through which millions are learning to program. Objectives: In this paper, we investigate the debugging behaviour of 12-14-year-old students learning Python through a lesson-long classroom study. Method: We collected program snapshots from 73 students' attempts at a set of Python debugging exercises in an online code editor. Through qualitative content analysis of these snapshots, we developed a granular categorisation of the changes students made when debugging. Findings: While most students were able to resolve some errors, most also frequently exhibited ineffective debugging behaviours. Many students added errors through small-scale changes, reverted corrective changes, and repeatedly ran identical programs in quick succession. From the results, we identify four barriers to successful and reliable debugging for students learning a text-based programming language: fragile knowledge, a lack of systematicity and reflection, the syntax barrier, and dynamics of emotions and attitudes. Implications: This paper highlights some of the difficulties that secondary school students have when debugging in Python. We recommend that school teachers explicitly teach a systematic approach to debugging and discourage the use of ineffective debugging behaviours, and that programming environments should contain features that facilitate successful debugging.

By generalizing the notion of linearization, a concept originally arising from microlocal analysis and symbolic calculus, to diffeological spaces, we make a first proposal setting for optimization problems in this category. We show how linearizations allow the construction of smooth paths and variational flows without requiring canonical charts or gradients. With these constructions, we introduce a general optimization algorithm adapted to diffeological spaces under weakened assumptions. The method applies to spaces of mappings with low regularity. Our results show that weak convergence toward minima or critical values can still be achieved under diffeological conditions. The approach extends classical variational methods into a flexible, non-linear infinite-dimensional framework. Preliminary steps to the search for fixed points of diffeological mappings are discussed.

The kagome lattice, with its inherent frustration, hosts a plethora of exotic phenomena, including the emergence of $3\mathbf{q}$ charge density wave order. The high rotational symmetry, required to realize such an unconventional charge order, is broken in many kagome materials by orthorhombic distortions at high temperature, the origin of which is much less discussed despite their ubiquity. In this study, synchrotron X-ray diffraction reveals a structural phase transition from a parent hexagonal phase to an orthorhombic ground state, mediated by a critical regime of diffuse scattering in the prototypical kagome metals $R$Ru$_3$Si$_2$ ($R$=rare-earth). Structural analysis uncovers an interlayer dimerization of kagome atoms in the low-temperature phase. Accordingly, a dimer model with one-dimensional disorder on kagome layers successfully reproduces the diffuse scattering. The observations point to molecular orbital formation between kagome $4d_{z^2}$ orbitals as the driving force behind the transition, consistent with \textit{ab initio} calculations. A framework based on electronegativity and atomic radii is proposed to evaluate the stability of the hexagonal phase in kagome metals, guiding the design of highly symmetric materials.

We elucidate the profound connection between physics and computation by proposing and examining the model of the non-Hermitian quantum computer (NQC). In addition to conventional quantum gates such as the Hadamard, phase, and CNOT gates, this model incorporates a non-unitary quantum gate $G$. We show that NQC is extraordinarily powerful, capable of solving not only all NP problems but also all problems within the complexity class $\text{P}^{\sharp\text{P}}$ in polynomial time. We investigate two physical schemes for implementing the non-unitary gate $G$ and find that the remarkable computational power of NQC originates from the exponentially large physical resources required.

For a $d$-uniform random hypergraph on $n$ vertices in which hyperedges are included i.i.d.\ so that the average degree in the hypergraph is $n^{δ+o(1)}$, the projection of such a hypergraph is a graph on the same $n$ vertices where an edge connects two vertices if and only if they belong to a same hyperedge. In this work, we study the inference problem where the observation is a \emph{noisy} version of the graph projection where each edge in the projection is kept with probability $p=n^{-1+α+o(1)}$ and each edge not in the projection is added with probability $q=n^{-1+β+o(1)}$. For all constant $d$, we establish sharp thresholds for both detection (distinguishing the noisy projection from an Erdős-Rényi random graph with edge density $q$) and reconstruction (estimating the original hypergraph). Notably, our results reveal a \emph{detection-reconstruction gap} phenomenon in this problem. Our work also answers a problem raised in \cite{BGPY25+}.

This study systematically investigates two multi-fidelity strategies used to train machine-learned force fields (MLFFs) -- pre-training/fine-tuning and multi-headed training -- and elucidates the mechanisms underpinning their success. For pre-training and fine-tuning, we uncover a log-log linear relationship between pre-trained and fine-tuned accuracies that holds across model architectures, model sizes, and quantum-chemical methods. The success of this approach hinges on the quantity and quality of available pre-training data, and, critically, the inclusion of force labels. We demonstrate that pre-trained representations are inherently method-specific, requiring adaptation of the model backbone during fine-tuning. In contrast, multi-headed models learn method-independent backbone representations, where again the heads' accuracies are log-log linearly related. Relative to pre-training and fine-tuning, these shared representations marginally reduce model performance in most cases. However, this trade-off is offset by practical advantages: multi-headed training extends naturally to multiple labelling methods and enables partial replacement of expensive labels with cheaper alternatives, paving the way towards cost-efficient universal MLFFs.

Multiple cosmological observations hint at neutrino self-interactions beyond the Standard Model, yet such interactions face severe constraints from terrestrial experiments. We resolve this tension by introducing a model where active neutrinos resonantly convert to self-interacting dark radiation after BBN but before CMB epoch. This exploits the fact that cosmological observables cannot distinguish between neutrinos and dark radiation with the same abundance and free-streaming properties. Our mechanism, based on a simple type-I seesaw framework along with a keV-scale scalar mediator, achieves two objectives: (i) it produces strongly self-interacting dark radiation that imitates neutrino self-interactions favored by cosmological data, and (ii) it depletes the active neutrino energy density, relaxing cosmological neutrino mass bounds and easing the tension with neutrino oscillation data. The model naturally evades laboratory constraints through suppression of the neutrino-mediator coupling by the squared mass ratio of active and sterile neutrinos. We show that this scenario is favored over $Λ$CDM by the combined Planck and DESI data, while being consistent with all other constraints. Our mechanism is testable in future laboratory probes of absolute neutrino mass and searches for sterile neutrinos.

We give a rigorous mathematical result, supported by numerical simulations, of the aggregation of a concentrated vortex blob with an underlying non-constant vorticity field: the blob moves in the direction of the gradient of the field. It is a unique example of a Lagrangian explanation of aggregation of vortex structures of the same sign in 2D inviscid fluids. The result is also extended to almost vertical vortex filaments in a (possibly thin) three-dimensional domain.

In this paper we complete the study of the Lan-Sheng-Zuo conjecture proposed in arXiv:1210.8280 for the curve case. Precisely, we prove that every semistable Higgs bundle is strongly semistable for curves of genus $g\leq 1$, and over any curves of genus $g\ge2$ construct explicit examples of semistable Higgs bundles of arbitrary big rank (the first example is $p=2,r=3$) which are not strongly semistable. These results are complementary to the strongly semistability theorem of Lan-Sheng-Yang-Zuo and Langer for semistable Higgs bundles of small rank.

As often emerges in various basic quantum properties such as Rényi and Tsallis entropies, the trace of quantum state powers $\text{tr}(ρ^q)$ has attracted a lot of attention. The recent work of Liu and Wang (SODA 2025) showed that, even for (possibly) non-integer $q>1$, $\text{tr}(ρ^q)$ can be estimated to within additive error $ε$ using a dimension-independent (and also rank-independent) sample complexity of $\widetilde O(1/ε^{3+\frac2{q-1}})$, together with a lower bound of $Ω(1/ε)$. In addition, combining this result with subsequent work of Liu (STACS 2026) shows that the corresponding promise problem is ${\sf BQP}$-complete. In this paper, we significantly improve and extend the sample complexity bounds for this problem. Furthermore, we show that for $0<q<1$, the problem does not admit an efficient estimator unless ${\sf BQP}={\sf NIQSZK}$, which is considered highly unlikely. In particular, we have the following results. - For $q>2$, we settle the sample complexity with matching upper and lower bounds $\widetildeΘ(1/ε^2)$. - For $1<q<2$, we obtain an upper bound of $\widetilde O(1/ε^{\frac2{q-1}})$, with a lower bound of $Ω(1/ε^{\max\{\frac1{q-1},2\}})$ for dimension-independent (in fact, rank-independent) estimators. - For $0<q<1$, we obtain an upper bound of $O((d/ε)^{\frac2{q}})$, with a lower bound of $Ω((d/ε)^{\frac1{q}})$ for $d$-dimensional states (in fact, both bounds can be naturally refined to depend on the rank rather than the dimension). Accordingly, the corresponding promise problem is ${\sf NIQSZK}$-hard, which is in sharp contrast to the case of $q>1$. Technically, our upper bounds are obtained by (non-plug-in) quantum estimators based on weak Schur sampling, in sharp contrast to the prior approach based on quantum singular value transformation and samplizer.

We rederive the Arnowitt-Deser-Misner equations in a framework in which the zeros of the lapse are innocuous, whether with or without changes of sign. We further develop and analyse a covariantized version of the Anderson-York equations, which provide a well posed system of tensorial evolution equations with freely prescribable shift vector and densitised lapse. The causality properties of the resulting equations are explored. We show how to relate solutions of the Anderson-York equations to the maximal globally hyperbolic development of the initial data.