Global symmetry anomalies of a quantum field theory (QFT) can be packaged as specific couplings of a higher-dimensional symmetry theory (SymTh). In this work we show that for 5D superconformal field theories (SCFTs) engineered from M-theory backgrounds $X$ a Calabi-Yau cone, this data can be extracted from the path algebra of branes probing $X$. This provides a complementary algebraic approach compared with more geometric computations based on the explicit calculation of triple intersection numbers in a resolved geometry and / or $η$-invariants extracted from the boundary geometry $\partial X$. Our method applies in situations where the counterpart geometric computation is either unknown or combinatorially unwieldy. We illustrate with several toric threefold examples, including orbifolds $\mathbb{C}^{3} / Γ$ and more general non-orbifold Calabi-Yau cones of Sasaki-Einstein five-manifolds.

Are AI agents tools, co-authors, or researchers? We present a quantified case study ($N=1$): a physicist supervising an AI coding agent (Claude Code, Sonnet and Opus models) over 12 work days and 57 sessions to build CLAX-PT, a differentiable one-loop perturbation theory module in JAX. We documented and classified 15 supervision events by intervention level. The agent resolved ten autonomously by iterating against oracle tests. Two more by the physicist's domain knowledge. The three it could not -- all evaded oracle detection -- share a common property: the agent treated symptom reduction as root-cause resolution. It spent 33 of the 57 sessions adjusting coefficients within a code architecture that could not represent the target physics, and could not re-evaluate its CLASS-PT branch choice even when prompted to reconsider; only an injected physics concept (anisotropic BAO damping) triggered the redesign. Separately, the agent committed a calibrated correction that passed all oracle tests but corresponded to no quantity in the theory, predicting wrong values at any other cosmology. The fudge factor was caught and replaced within the same session. Three supervision practices proved critical for catching what oracle tests missed: testing at diverse parameter points beyond the fiducial calibration; shared changelogs that surfaced stalled exploration across sessions; and an explicit rule against unphysical numerical patches. In this case, supervision design, not model capability, determined whether the agent's output was trustworthy. Closing the gap would require agents that propose architectural alternatives rather than optimize within a given structure, and distinguish predictive adequacy from explanatory correctness -- capabilities not exhibited here, not obviously addressed by scaling alone. [Abridged.]

Non-singular gravitational theories are expected to be relevant in the early universe. In this paper, we derive a set of effective Friedmann equations describing the dynamics of matter shells in the presence of a gravitational regulator $\ell$. We find that such a regulator induces a primordial black hole mass gap such that below a certain mass $M_\text{gap}(\ell, R_H)$ no black holes can form. The order of magnitude of this mass gap is set by the regulator $\sim c^2\ell/G$, with subleading dependence on the horizon radius at time of formation $R_H$. Finally, we show that over a wide range of equation of state parameters $ω= 0 \dots 1/3$, the mass gap implies a Carr criterion of the form $δ_H > 2G M_\text{gap}/R_H - 1$. If the horizon size is of the same order of the regulator, $R_H \sim \ell$, this new criterion is stronger than the traditional Carr criterion for primordial black hole formation. This connects the primordial black hole abundance directly to the presence of gravitational regulators.

Electron doped rhombohedral multilayer graphene at high displacement field features an exceptionally flat band minimum with near-ideal quantum geometry. Experiments in this regime observe the formation of a 'quarter metal,' in which the electron liquid condenses into a single spin- and valley flavor. Remarkably, recent experiments have found a zero resistance state in the same region of the density- and displacement-field-tuned parameter space, attributed to the formation of a chiral superconductor characterized by a finite-momentum Cooper pair condensate. Here, we use nanoSQUID-on-tip magnetometry to map the orbital magnetization of electron-doped rhombohedral graphene devices ranging in thickness between 3 and 13 layers. Magnetization within the quarter metal phases peaks at finite density, consistent with concentration of the Berry curvature in a finite-momentum 'ring of fire'. Correlating transport and local magnetometry data in a tetralayer sample reveals that the superconducting state has a finite orbital magnetic moment, providing direct evidence of its chiral nature. We further show that widely observed stochastic switching of the resistivity in the metallic regime arises from a density-tuned sign change in the valley-resolved total magnetic moment. This leads to the formation of metastable magnetic domains under typical gate control sequences and can also be harnessed for electric-field controlled switching of orbital moment across the entire device. Unexpectedly, we find magnetic inhomogeneity specific to the apparent normal state of the chiral superconductor, suggestive of a strain-tuned competition between magnetic and non-magnetic ground states. Our results point to a subtle energetic competition underlying the observation of chiral superconductivity in a narrow range of layer numbers.

Galaxy redshift surveys encode a wealth of information generated by nonlinear gravitational evolution, galaxy bias, and redshift-space distortions, only part of which is accessible through standard two-point statistics. Motivated by the need for flexible and computationally efficient alternatives, we introduce the augmented correlation function, a general framework in which an arbitrary transformation of the galaxy field defines additional ``latent'' dimensions that extend the standard two-point correlation function and isolate clustering properties averaged out in conventional analyses. As a proof of concept, we study a latent variable constructed from the pairwise gradient of the inverse Laplacian of the galaxy density field, showing that the resulting statistics naturally distinguish clustering regimes associated with infalling and outflowing pairs. Using Fisher forecasts based on $z=1$ halo catalogues from the Quijote simulations within $νΛ\mathrm{CDM}$ cosmology, we find that the augmented correlation systematically yields tighter constraints on all cosmological parameters considered. Although these improvements should be regarded as indicative given the exploratory nature of the analysis and the limitations of Fisher forecasts and simulations, our results demonstrate the potential of augmented correlations as a flexible framework for extracting additional information from spectroscopic galaxy surveys.

We develop a straightforward analytical framework for the propagation of spatial light modes through a turbulent atmosphere. Built upon the split-step approach with the mode-based optical field representation, it directly assesses how turbulence-induced phase fluctuations deplete the optical power in the original mode and re-distribute it into neighboring spatial modes. Importantly, this power transfer scales linearly with the propagation distance in a uniform channel, yielding a simple solution for arbitrary distances in the form of a matrix exponential. The transfer rate is determined by the spatial spectral overlap between the turbulence spectrum and the acceptance spectrum for a pair of interacting spatial modes. The model predicts the average power in each spatial mode and is exact when a single mode strongly dominates all others. Our predictions show reasonably good agreement with simulations up to medium-to-strong turbulence levels. The model also confirms the scalings with mode order previously known as empirical observations.

We establish improved sample-complexity bounds for sample-based Lindbladian simulation based on the Wave Matrix Lindbladization (WML) algorithm. For a jump operator $L$ with dimension $d$, we derive an explicit non-asymptotic sample complexity bound $n_d^*(t,\varepsilon) \le \left( \frac{2d+3}{8} \right) \|L\|_\infty^2 \left( \frac{t^2}{\varepsilon} \right)$, holding for simulation time $t$ and error $\varepsilon$. This refines the dimension dependence of the best previously known bound, $O(d^2 t^2/\varepsilon)$, from [Go et al., Quantum Sci. Tech. 10, 045058 (2025)]. Remarkably, we show that this dimensional overhead can be entirely avoided when $\| L\|_\infty^2 = O(1/d)$, a condition satisfied with high probability for random Lindblad operators, yielding a typical-case sample complexity of $O(t^2/\varepsilon)$. On the other hand, in the worst case, we show that WML necessarily requires $Ω(dt^2/\varepsilon)$ samples by constructing an explicit example with a rank-one Lindblad operator. Our results reveal a sharp dichotomy between typical and adversarial sample complexities in Lindbladian simulation, thereby strengthening the theoretical foundations of sample-based quantum algorithms.

We study Bernoulli percolation on $\mathbb Z^d$ in dimensions ${d>6}$. We prove that a classical consequence of the van den Berg-Kesten inequality, often referred to as the Simon-Lieb inequality in the context of the Ising model, admits a partial reversal. As a main application, we show that the quantity $φ_{p_c}(S)$, introduced by Duminil-Copin and Tassion (Comm.\ Math.\ Phys., 2016), is uniformly bounded over all $S\subset \mathbb Z^d$. This partial reversal further yields a short and self-contained route to several key results, including near-critical estimates on the two-point function and sharp bounds on the critical one-arm probability.

The bilayer Ruddlesden-Popper nickelate $\mathrm{La_3Ni_2O_7}$ has emerged as a promising platform for exploring and understanding high-temperature superconductivities. While most prior doping studies have focused on hole doping via strontium (Sr) substitution or by tuning oxygen content, electron doping remains largely unexplored. In this work,we systematically investigate electron doping in $\mathrm{La_3Ni_2O_7}$ thin films through tetravalent element substitution, employing first-principles density functional theory calculations. Our results reveal that, unlike in cuprates, $\mathrm{cerium}$ (Ce) doping is difficult to effectively introduce electron carriers into the low-energy bands. In contrast, zirconium (Zr), hafnium (Hf), and thorium (Th) can act as efficient electron dopants. These element substitutions can significantly increase the interlayer hopping $t_{\perp}$ between $d_{z^2}$ orbitals, which may lead to enhanced superexchange coupling $J_{\perp}$ , and thereby potentially elevated superconducting $T_c$. We evaluate the interaction parameters using constrained random phase approximation. Our results identify candidate dopants for achieving electron-doped $\mathrm{La_3Ni_2O_7}$, offering a route to clarify the ongoing debate on pairing mechanisms in this system.

A Kondo insulator (KI) is a prototypical example of a highly entangled phase of matter, where many-body interactions between local moments and delocalized electrons engender the non-magnetic insulating ground state. Conventionally, the local moments arise from atomic multiplet states with a narrow bandwidth, limiting Kondo coherence to low temperatures. Here, we realize a new paradigm for constructing the KI state with hybridized molecular orbitals in FeSb2. Resonant inelastic X-ray scattering (RIXS) at the Fe L-edge reveals distinct signatures of band-like continuum states and localized states. Comparisons with first-principles calculations establish a mixed-configuration ground state with hybridized Fe d-Sb p molecular orbitals as basis states. By systematically investigating the RIXS momentum, temperature, and doping dependences, we find propagating collective modes commensurate with many-body charge and spin excitations. Our results pave the way for understanding the emerging class of unconventional d electron insulators and engineering high temperature Kondo many-body states.

Objectives: We captured a fine-grained dataset of organic socializing with socially meaningful group labels to fill a gap in the study of face-to-face interaction. Prior interaction data from conferences, classrooms, hospitals, and workplaces exhibit network signatures such as heterogeneous contact rates, clustering, and bursty dynamics. However, schedules, room assignments, and authority roles in these settings may obscure organic social group dynamics. Studies on group mixing often rely on demographic proxies like gender, or assigned categories like school classes, rather than relationship-based groups. We aim to test if temporal network signatures from institutionally structured settings generalize to informal social interaction. Data description: We present the first, to our knowledge, public temporal proximity network dataset of a privately hosted social event, with contextual relationship-based group membership. At the outdoor cocktail hour of a wedding, 95 participants wore proximity sensor badges that detected other badge-wearers within approximately 1.5 m in 5 s intervals. The public dataset, coarsened to 10 s temporal bins, contains 7,213 contact events over 2,760 observed dyads. Participants self-reported their relationship category with respect to the wedding couple, enabling group mixing analysis. Beyond testing the generalizability of interaction patterns, this dataset supports modeling of social events for applications such as contact tracing policy and social-space design.

We found solutions to the linear but with complicated kernel and non-homogeneous evolution equations for the cross sections of productions of $n$-cut Balitsky-Fadin-Kuraev-Lipatov (BFKL) Pomerons in the final states of high energy DIS on a nucleus, resumming all multiple rescatterings in the leading logarithmic approximation. For the model leading-twist BFKL kernel, we calculate analytical solutions of these equations by developing the homotopy approach. We also calculate the solution in the large $z=\ln\left(x_{01}^2\,Q_s^2(Y,\mathbf{b})\right)$ and large $n\gtrsim\langle n(z) \rangle$ limits, where $x_{01}$ is the dipole size, $Q_s$ the saturation scale and $\langle n(z) \rangle$ is the average multiplicity of the produced gluons. Having these cross sections we calculate the multiplicity distributions of the produced gluons and describe how the upcoming Electron-Ion Collider (EIC) can test our theoretical formalism.

The recent discovery of altermagnetism has sparked growing interest in compensated magnetic systems as promising platforms for highly scalable spintronics. Altermagnetism is a distinct magnetic order where opposite spin sublattices are connected by rotation, yielding zero net magnetization but momentum-dependent spin splitting. To date, experimental verification of altermagnetic order has been achieved predominantly through bulk-sensitive techniques, including spin-dependent electronic spectra and transport responses. However, direct atomic-scale evidence that explicitly correlates crystal symmetry, local structural distortions, and magnetic ordering has remained unexplored. Here, we report the direct atomic-scale observation of coexisting polar distortions and altermagnetic order in MnTe, combining atomic resolution scanning transmission electron microscopy (STEM) imaging with electron magnetic chiral dichroism (EMCD) measurements. We reveal that MnTe is not an ideal uniform P63/mmc g-wave altermagnet at the atomic scale. Instead, it hosts ubiquitous inversion-symmetry-breaking distortions that lower the spin-space-group (SSG) symmetry, admits d-wave altermagnetic components, and in lower-symmetry regimes, even allow s-wave spin splitting (net magnetization). The coexistence of ferroelectric signatures and altermagnetic order establishes local lattice symmetry in MnTe as a control knob for altermagnetic spin splitting, spin current generation, and multiferroic memory applications.

We introduce a geometric formulation of quantum indeterminacy from which the standard uncertainty inequalities emerge as necessary consequences. Our approach is based on convex geometry in phase space and on methods from symplectic topology, and does not rely on statistical descriptors such as variances or covariances. Instead, we associate to empirical position and momentum data with convex bodies whose mutual relations encode the fundamental constraints of quantum mechanics. The central tools are h-polar duality and symplectic capacities, which provide intrinsic, coordinate-free bounds on admissible phase-space configurations. Within this framework, the Robertson-Schrodinger inequalities arise naturally as manifestations of deeper geometric and topological principles. This perspective suggests that quantum indeterminacy is not primarily a statistical phenomenon, but rather a structural property of phase space governed by symplectic covariance. The results thus provide a unified and conceptually transparent foundation for the uncertainty principle.

The recent, super-exponential scaling of autonomous Large Language Model (LLM) agents signals a broader, fundamental paradigm shift from machines primarily replacing the human hands (manual labor and mechanical processing) to machines delegating for the human minds (cognition, reasoning, and intention). The uncontrolled offloading and scaling of "thinking" itself, beyond human's limited but efficient biological capacity, has profound consequences for humanity's heat balance sheet, since thinking, or intelligence, carries thermodynamic consequences. The Earth has already surpassed the heat dissipation threshold required for long-term ecological stability, and projecting based on empirical data reveal a concerning trajectory: without radical structural intervention, anthropogenic heat accumulation will breach critical planetary ecological thresholds in less than 6.5 years, even under the most ideal scenario where Earth Energy Imbalance (EEI) holds constant. In this work, we identify six factors from artificial intelligence that influence the global heat dissipation rate and delineate how their interplay drives society toward one of four broad macroscopic trajectories. We propose that the integration of artificial intelligence and its heat dissipation into the planetary system constitute the tenth planetary boundary (9+1). The core empirical measurement of this boundary is the net-new waste heat generated by exponential AI growth, balanced against its impact on reducing economic and societal inefficiencies and thus baseline anthropogenic waste heat emissions. We demonstrate that managing AI scaling lacks a moderate middle ground: it will either accelerate the breach of critical planetary thermodynamic thresholds, or it will serve as the single most effective lever on stabilizing the other nine planetary boundaries and through which safeguarding human civilization's survival.

The artificial intelligence industry is not an isolated economic phenomenon; it is the current physical substrate for a broader, multi-billion-year process: the evolution of an abstract intelligence on Earth. As the scale of computation accelerates planetary-wide, the academic and popular discourse must address the physics aspect of this phase transition, beyond algorithmic architectures, alignment, and silicon supply chains. From first principles, it is clear that if the current exponential trajectory of computation holds, the ultimate bottleneck of the coming decades will be neither data nor capital, but the laws of thermodynamics and the finite heat capacity of the Earth. The evolution of intelligence is fundamentally a problem of non-equilibrium thermodynamics, bound by strict hardware limitations, and ultimately, an absolute ecological boundary condition. Civilization itself is an exceedingly rare and highly expensive thermodynamic algorithm actively fighting the default settings of the cosmos. To better understand AI's future and our future, we must rigorously examine the physical laws governing computation, complexity theory, and the narrow thermodynamic tightrope our civilization must walk to survive it

In recent years, diffusion-based models have demonstrated exceptional performance in searching for simultaneously stable, unique, and novel (S.U.N.) crystalline materials. However, most of these models don't have the ability to change the number of atoms in the crystal during the generation process, which limits the variability of model sampling trajectories. In this paper, we demonstrate the severity of this restriction and introduce a simple yet powerful technique, mirage infusion, which enables diffusion models to change the state of the atoms that make up the crystal from existent to non-existent (mirage) and vice versa. We show that this technique improves model quality by up to x2.5 compared to the same model without this modification. The resulting model, Mirage Atom Diffusion (MiAD), is an equivariant joint diffusion model for de novo crystal generation that is capable of altering the number of atoms during the generation process. MiAD achieves an 8.2% S.U.N. rate on the MP-20 dataset, which substantially exceeds existing state-of-the-art approaches. Code: https://github.com/andrey-okhotin/miad.git

Recent angle-dependent magnetoresistance measurements in underdoped cuprates have revealed compelling evidence for small hole pockets in the pseudogap regime, including observation of the Yamaji effect in HgBa$_2$CuO$_{4+δ}$ (Chan et al., Nature Physics 10.1038/s41567-025-03032-2 (2025)). A key distinction between theories is their predicted Fermi volumes, measured as fractions of the square lattice Brillouin zone: $p/4$ per pocket for spin density wave (SDW) versus $p/8$ for fractionalized Fermi liquid (FL*), where $p$ is the hole doping. We calculate the $c$-axis magnetoresistance $ρ_{zz}(θ, ϕ)$ within the semiclassical Boltzmann formalism for both states, and using the ancilla layer model (ALM) for FL* in a single-band Hamiltonian. The results from the $\text{FL}^*$ phase show good consistency with current experimental data. Conversely, the results for the SDW phase are highly sensitive to the ordering momentum along the $z$-direction. An ordering vector of $Q = (π, π, π)$ yields predictions that starkly disagree with the experiment. The only possibility for agreement within the SDW scenario is to assume an ordering momentum of $Q = (π, π, 0)$. However, even in this specific case, the SDW scenario predicts a marginally smaller Yamaji angle at $ϕ=0$ than the FL* theory, and a second Yamaji peak near in-plane angle $ϕ= 45^\circ$, which was not observed in the experiment. In reality, the Néel ordering vector is likely uncorrelated between adjacent layers, so that there is no coherent interlayer transport of hole-pocket quasiparticles in the SDW scenario, and consequently no Yamaji effect. Our results support the FL* interpretation of Fermi arcs in the pseudogap phase, and establish Yamaji angle measurements as a discriminatory tool between theoretical models.

Consider a non-relativistic quantum particle with wave function $ψ$ in a bounded $C^2$ region $Ω\subset \mathbb{R}^n$, and suppose detectors are placed along the boundary $\partial Ω$. Assume the detection process is irreversible, its mechanism is time independent and also hard, i.e., detections occur only along the boundary $\partial Ω$. Under these conditions Tumulka informally argued that the dynamics of $ψ$ must be governed by a $C_0$ contraction semigroup that weakly solves the Schrödinger equation and proposed modeling the detector by a time-independent local absorbing boundary condition at $\partial Ω$. In this paper, we apply the newly discovered theory of boundary quadruples to parameterize all $C_0$ contraction semigroups whose generators extend the Schrödinger Hamiltonian, and prove a variant of Tumulka's claim: all such evolutions are generated by the placement of a linear absorbing boundary condition on $ψ$ along $\partial Ω$. We combine this result with the work of Werner to show that each $C_0$ contraction semigroup naturally admits a Born rule for the time of detection along $\partial Ω$, and we prove that a detection will almost surely occur in finite time if detectors have been placed everywhere along $\partial Ω$.

Skipper Charge-Coupled Devices (Skipper-CCDs) are a leading technology in the search for sub-GeV dark matter and coherent elastic neutrino-nucleus scattering. A key background for rare-event searches with these detectors arises from "spurious charge" -- single-electron events generated when charges are transferred through the active region to the serial register, and across the serial register to the readout stage. We present a characterization of spurious charge in both the active region and the serial register of SENSEI Skipper-CCDs, and show that, in a well-shielded low-background environment, the dominant contribution originates in the serial register during Skipper readout, when horizontal clocks are held at constant voltage between pixel transfers. Motivated by this finding, we develop a "tri-level" clocking scheme in which the held-low phase is raised to an intermediate voltage during readout to suppress trap-mediated charge generation. Using the SENSEI detector near the MINOS cavern, we measure a serial-register single-electron density of $(2.9 \pm 0.1) \times 10^{-5}$ electrons/pixel/image under standard SENSEI readout conditions, reduced to $(4.0 \pm 0.4) \times 10^{-6}$ electrons/pixel/image with tri-level clocking -- a factor of $\sim$7 improvement. This technique offers a promising path to lower backgrounds in current and future Skipper-CCD experiments.

Real-time thermal-hydraulic simulation is essential for digital twin (DT) technology that supports the safe and efficient operation of small modular reactors (SMRs). Computational fluid dynamics (CFD) provides high-fidelity flow analysis, but its computational cost prevents direct use in DT applications. AI-based surrogate modeling has been actively investigated to address this limitation, yet neural operator--based surrogates for CFD-level transient analysis of SMR-specific geometries have not been reported. This study presents an integrated framework that combines a reduced-order model (ROM) with neural operators, applied to the helical coil steam generator (HCSG) of the System-integrated Modular Advanced Reactor (SMART). Two ROM strategies tailored to each CFD data type were compared, an MLP-based autoencoder (AE) for unstructured mesh data and a convolutional autoencoder (CAE) for structured mesh data, and each was coupled with the deep operator network (DeepONet) to construct the latent DeepONet (L-DeepONet). The Fourier neural operator (FNO) was additionally adopted for comparison. A multi-scale technique was incorporated into both frameworks to mitigate spectral bias and improve the prediction of Kármán vortex streets developing inside the HCSG. The multi-scale L-DeepONet captured the instantaneous periodic vortex dynamics in both velocity and pressure fields, while the FNO and its multi-scale variant predicted the time-averaged mean flow and provided reliable pressure drop estimates. These complementary characteristics provide a practical model-selection guideline that links each architecture to specific DT objectives based on CFD data type and the required level of flow resolution.

Self-dual theories, being UV-finite, should have their own AdS/CFT dualities. Higher-spin extensions of self-dual theories are attractive to simplify the CFT duals. The maximal self-dual theory is Chiral higher-spin gravity, which should be dual to a subsector of Chern--Simons matter theories. As a step toward establishing self-dual holography, we develop the Fefferman--Graham expansion and holographic dictionary for arbitrary spin self-dual theories. As an application, we derive bulk-to-bulk propagators and compute three- and four-point AdS/CFT correlators in a contraction of Chiral higher-spin gravity, which is a higher-spin extension of self-dual Yang--Mills theory.

Synchronization, a ubiquitous phenomenon in classical systems, has recently been extended to the quantum domain. Here, we show quantum synchronization of a bosonic mode exhibiting a Fock state-like limit cycle, manifesting as a steady state with a negative Wigner function. We demonstrate that this non-classical state can be phase-locked to an external drive, achieving synchronization within an Arnold tongue regime. We argue that synchronization is a dynamical property and fundamentally tied to the suppression of phase slips, which we show to occur with exponentially decreasing probability. We introduce a novel method to extract the phase slip rate from the Lindblad time evolution of the system. This work opens new avenues for understanding and manipulating non-classical synchronization dynamics.

We examine the intrinsic physical-chemical properties of the conjugated ladder-type polymer poly(benzimidazobenzophenanthroline) (BBL) in response to electron transfer. We aim at explaining the origin of the anti-ambipolar behavior behind the observed BBL nonlinear response associated with specific device architectures. To elucidate this point, we use theory and computation based on first principles, including density functional theory optimizations, ab initio molecular dynamics, time-dependent DFT, and Marcus-theory analysis. Our results reveal that this redox response is not simply monotonic but follows an alternating odd/even pattern in which gap narrowing and reopening occur sequentially before near-gapless behavior emerges at high charging. Converging theoretical evidence in this work demonstrates that bell shaped conductivity in BBL originates in its fundamental electronic structure and supramolecular organization.

We analyze the effect of microscopic heterogeneity on the Lorenz curve of macroscopic observables. Lorenz curve of a response function being a cumulative and bounded quantity, is often a more stable function than the corresponding probability density. We show here that by doing an exponent spectrum analysis of the complementary Lorenz curve, it is possible to obtain a reflection of the underlying heterogeneity that causes the response function to depart from a power law behavior. We demonstrate this framework first by synthetic data and then by analyzing the avalanche statistics of a two dimensional, Random Field Ising Model (RFIM) at zero temperature. This method can lead to possible use in estimating microscopic heterogeneity of a system from analysis of an estimated Lorenz curve, particularly in socio-economic and physical contexts where the full probability distribution function is unavailable.

In this work, we compare three qubit-mapping strategies to study the structure of the nuclear ground state within the shell model description employing the Variational Quantum Eigensolver (VQE) approach. Although the initial point for different mappings is a Hamiltonian matrix in many-body particle basis or Slater determinant (SD) basis, the structure of the trial wavefunction and resource counts are different for each mapping. These three mappings are tested for a mid $p$-shell nucleus $^{10}$B and compared the quantum resources required to find the ground state for each mapping. Further, we extend the qubit-efficient mapping to study the ground state of one more mid $p$-shell nucleus $^{12}$C. We run circuits up to 26-qubits representing their ground states on a noisy simulator (IBM's FakeFez backend) and quantum hardware ($ibm\_fez$). The best post-error mitigated results from the hardware for $^{10}$B ground state is obtained following SD to qubit mapping with a percent error of 0.21 \%. The percent errors for the same state following cSD and pnSD mapping are 3.37 and 8.88 \%, respectively. On the other hand, following the cSD mapping, the post-error mitigated ground state energy of $^{12}$C is 6.82 \% away from the exact result. We further evaluate the fidelity of the VQE wavefunctions obtained from hardware with respect to the shell model wavefunctions for the cSD mapping. This cSD mapping can be useful for scaling the VQE algorithm for complex nuclei across different mass regions in terms of qubit efficiency.

The existence of a landscape of metastable vacua raises the possibility that our Universe may have undergone quantum vacuum decay at late times. This work explores how such a transition can be tested with cosmological observables, focusing on precision distance measurements and cosmic microwave background (CMB) anisotropies. A set of phenomenological models is constructed in which late-time quantum tunneling changes the vacuum energy and may convert a subcomponent of dark matter into dark radiation, possibly accompanied by domain-wall production. The resulting expansion histories are compared with DESI DR2 baryon acoustic oscillation data; supernova distance measurements from DES-Dovekie, Pantheon+, and Union3; and a compressed CMB likelihood. For quantum-tunneling models, current cosmological distance measurements still allow a 50% decrease in the total vacuum energy for a transition redshift $z_t<1$. The model with dark-matter conversion and domain-wall production provides a good fit to resolve the tension between cosmological observables and the $Λ$CDM model, with a preferred transition around $z_t \sim 7$ and about 10% of dark matter participating in the transition. Additionally, CMB anisotropy constraints from bubble nucleation and the associated domain-wall network are derived and shown to strongly restrict slow or sparse late transitions. Applied to the minimal quantum-tunneling model, these constraints allow an $\mathcal{O}(10\%)$ decrease in the total vacuum energy for a transition redshift $z_t$ of order unity. For nonminimal models, dark-matter-density-dependent tunneling can proceed rapidly enough to evade such bounds. These results demonstrate that late-time quantum vacuum decay is a testable cosmological phenomenon and provide a concrete observational handle on metastable-vacuum physics motivated by landscape scenarios.

In this letter, we establish a complete correspondence between the Newman--Penrose and 1+1+2 semitetrad covariant formalisms by expressing all Newman--Penrose spin coefficients, Ricci scalars, and Weyl scalars in terms of the scalar, vector, and tensor variables of the 1+1+2 decomposition. This provides a direct dictionary between two widely used approaches to general relativity and gives a geometrical interpretation of Newman--Penrose quantities in terms of covariantly defined 1+1+2 variables. As an application, we derive necessary conditions for the existence of future outer trapping horizons in locally rotationally symmetric class II spacetimes, expressed in terms of the Ricci and Weyl Newman--Penrose scalars and the cosmological constant.

The increasing complexity of industrial scheduling and transport routing problems motivates the study of alternative optimization formulations and computational paradigms. In this work, we study how higher-order unconstrained binary optimization (HUBO) formulations of such problems map onto quantum optimization workflows in both noisy and fault-tolerant regimes. We consider three representative logistics and manufacturing use cases and formulate each as a HUBO problem. This captures process intricacies, such as highly correlated assembly-line scheduling rules, which are difficult to express faithfully with the standard quadratic (QUBO) form, while at the same time reducing the number of binary variables required in the quantum mapping, thus lowering qubit demand. We compare the HUBO formulations with corresponding QUBO encodings, highlighting a key trade-off: while HUBO reduces qubit requirements through compact binary encoding, it introduces higher-order interaction terms that increase circuit depth, limiting feasibility on current quantum hardware. The proposed formulations are validated using classical solvers across several problem instances and benchmark small routing problem instances using bias-field digitized counterdiabatic quantum optimization in classical simulation. We complement these results with a resource and scalability analysis, focusing on the capacitated vehicle routing problem as a representative large-scale industrial use case. Our analysis indicates that while HUBO formulations offer advantages in qubit scaling compared to QUBO encodings, their practical implementation is constrained by gate fidelity, coherence, and circuit depth, making hybrid quantum-classical workflows and early fault-tolerant quantum hardware the most plausible settings for their practical use.

Efficient computation of the angular bispectrum is an essential part of modelling large-scale structure observations, but it still remains an extremely challenging task. In this work, we compute the tree-level, unequal-time angular bispectrum in both real and redshift space. By deriving full-sky results, we show that the bispectrum can be expressed as a sum of products of two angular power spectra, enabling the use of our recently developed flat-sky approximation to enhance computational efficiency significantly. This flat-sky formalism preserves key line-of-sight mode information while discarding extraneous full-sky contributions. We validate our approach by comparing it with direct full-sky integration, finding excellent agreement across a wide range of scales and redshifts for all bispectrum configurations. At redshift $z = 1$, we achieve sub-percent agreement (for multipoles $\ell \gtrsim 5$) between full-sky and flat-sky results for equilateral, squeezed, and folded configurations, using narrow Gaussian radial window functions ($σ_z = 0.01$) in both equal-time and unequal-time scenarios. On small scales, where direct full-sky integration becomes computationally prohibitive, our results align with the Limber approximation (where applicable), confirming the robustness and accuracy of our implementation. To facilitate future studies, we provide a \texttt{Python} implementation of our results, which is publicly available on \texttt{GitHub}.