Papers

The first evidence of electroweak (EW) production of pairs of Z bosons in association with two jets (jj) in the final state ZZjj $\to$ $\ell\ell\nu\nu$jj, where $\ell$ = e, $\mu$, is reported by the CMS experiment. The analysis is based on a data sample of proton-proton (pp) collisions at $\sqrt{s}$ = 13 TeV, corresponding to an integrated luminosity of 138 fb$^{-1}$. Events are selected by requiring exactly two leptons of same flavor and opposite charge, large missing transverse momentum, and two jets with a large rapidity separation and large invariant mass. The EW production cross section in a fiducial volume is $\sigma_{\mathrm{EW}}$(pp $\to$ ZZjj $\to$ $\ell\ell\nu\nu$jj) = 0.37 $^{+0.14}_{-0.12}$ (stat) $^{+0.06}_{-0.06}$ (syst) fb, in agreement with the standard model prediction of 0.39 $\pm$ 0.06 fb. The observed (expected) significance of the signal is 3.1 (2.8) standard deviations. Limits on anomalous quartic gauge couplings are derived in terms of dimension-8 effective field theory operators. A combination with the previously reported result from the ZZ decay channel with four charged leptons yields an observed (expected) significance of 5.0 (4.5) standard deviations for the EW production of Z boson pairs.

Modeling of complex microlensing events suffers from many difficult-to-disentangle degeneracies. This is especially the case for orbital motion of the source in a binary system, the so-called xallarap effect. To address the degeneracies inherent in xallarap modeling, we developed a novel approach that directly samples the physical parameters of the source stars (initial mass, evolutionary phase, metallicity, distance, and reddening) during MCMC fitting. In our approach the physical parameters of the source are estimated using MIST stellar evolution models. This parametrization imposes astrophysical constraints that help identify the physically most probable solutions. We test our method on the complex microlensing event OGLE-2017-BLG-0114, which exhibits signatures that can be traced to the complexity of the source system. We successfully constrained the microlensing models, achieving improvements in the Einstein ring radius estimates by up to an order of magnitude in the case of binary source models.

We study the statistical field-theoretic renormalization of active flocks via the Martin-Siggia-Rose-De Dominicis-Janssen (MSRDJ) formulation of stochastic dynamics, focusing on the Toner-Tu theory of Malthusian flocks, or polar-ordered, momentum non-conserving active fluids in which density fluctuations relax sufficiently rapidly to be eliminated as hydrodynamic degrees of freedom. Working in the limit of isotropic diffusion in two spatial dimensions, we perform an all orders analysis of the perturbative renormalization of propagators and interaction vertices. We show that a nonlinearly realized diagonal rotation/shift symmetry gives rise to Ward identities that protect the trigonometric structure of the convective derivative, ensuring that the sine and cosine interaction potentials retain their functional form under renormalization up to a rescaling of their overall couplings. We demonstrate that the freedom to renormalize the dynamical critical exponent in this boost non-invariant theory prevents the determination of the precise values of the critical exponents and anomalous dimensions in the theory, highlighting parallels and crucial differences with sine-Gordon and XY models. We express all anomalous dimensions as functions of the dynamical critical exponent, which remains undetermined due to the absence of a relation ordinarily forced upon boost invariant systems. We revisit and contextualize several claims and counterclaims in the recent literature in light of these results.

We compare the performance of representative cooling, adiabatic, and optimization algorithms for ground-state preparation in the presence of noise. Using an exactly solvable family of quadratic fermionic Hamiltonians subject to depolarizing noise, we derive the scaling of the achievable relative energy as a function of the noise rate and support these results with numerical simulations. The Hamiltonian exhibits two phases, separated by a quantum phase transition. As expected, the performance of the different algorithms depends on the phase: adiabatic evolution is favorable in the trivial phase, while a multi-frequency cooling algorithm, as proposed in [1], becomes competitive or superior in the topological phase, where gap-closing limits adiabatic protocols. We further present numerical results for the quantum approximate optimization algorithm [2], showing that it performs competitively with cooling in the trivial phase but is typically outperformed in the topological regime. Finally, we show that for this model the cooling protocol exhibits enhanced robustness to parameter imperfections, highlighting its potential advantage for realistic implementations of noisy quantum state preparation. The analytical approach developed here, in conjunction with numerical validation, establishes an extendable approach to benchmarking ground-state preparation algorithms.

Translation invariance underlies all algebraic constructions of topological codes with geometrical locality. It has remained an open question whether codes that generically break this invariance can still be topological and simultaneously possess geometrical locality. Resolving this question is important both fundamentally—deepening our understanding of topological phases—and practically, as relaxing translation invariance could vastly expand the design space and potentially reduce resource overhead in fault-tolerant architectures. Here we introduce space-group codes, in which crystallographic point-group operations enter the bulk stabilizer algebra; bivariate bicycle (BB) codes arise as the translation-only limit. The key insight is that the point-group orbit resolves topology and locality together: it yields a computable algebraic criterion for topological order and a folded geometry in which point-group operations become local. We identify space-group codes whose code parameters exceed the reported same-blocklength, same-check-weight BB benchmarks. In five parameter-matched neutral-atom comparisons, reflection codes reduce the optimized movement cost in every case, by up to $60\%$, while folded placements also enable lower-overhead multilayer superconducting layouts. Treating spatial operations as a code-design variable therefore opens a route to topological codes jointly optimized for information protection and hardware geometry.

The tunability and control of topological edge/surface states are crucial for the development of new device applications. In this work, by combining first-principles calculations and Wannier-based tight-binding methods, we show the emergence of quantum spin Hall phases in van der Waals heterostructures formed by stacking two trivial quintuple layers from the Bi$_2$Te$_3$ family. We demonstrate the tunability of the edge states under interlayer strain and external electric field effects, suggesting the possibility of switching topological edge states on/off by external control. Additionally, the quantum spin Hall edge channels remain robust against interlayer twist, highlighting their stability against external perturbations. Our results provide a new way to create and manipulate two-dimensional topological phases in systems based on Bi$_2$Te$_3$ family, which can be valuable for practical applications, such as topological field effect transistors and spintronic devices.

We show that higher-curvature Lovelock terms do not restore local cosmic censorship in spherical dust collapse, but instead promote the local visibility of central shell-focusing singularities. On the collapse branch with positive highest-order Lovelock coefficient \(c_N\), the highest nonvanishing Lovelock order \(N\) controls both the near-singularity collapse and the formation of trapped surfaces. In noncritical dimensions, \(D-1-2N>0\), the apparent-horizon curve approaches the singularity curve with trapping exponent \(\beta_N=(D-1)/(D-1-2N)\). Comparing this scale with the first nonvanishing correction \(r^\ell\) to the singularity curve gives the local-visibility condition \(\ell<\beta_N\), provided the singularity curve opens outward. Thus increasing \(N\) enlarges the class of inhomogeneous initial data producing outgoing radial null rays from the central singularity. In the critical odd-dimensional branch, \(D=2N+1\), no apparent horizon forms sufficiently close to the center, so any outward opening of the singularity curve gives local visibility. The locally visible singularities are Królak-strong along the emerging null rays, with Tipler strength reached at threshold. For bound and unbound collapse, the noncritical exponents are unchanged: the energy function modifies the opening of the singularity curve, while in the critical branch it enters the leading terminal collapse velocity.

We study the problem of learning local Lindbladians from black-box access to the physical evolution, where the goal is to estimate all Hamiltonian and dissipative coefficients. For local Lindbladians with low-intersection dissipators and local dynamical strength at most $\Lambda$, we design an algorithm that learns the coefficients to target accuracy $\varepsilon$, whose experiment count and total evolution time scale as $\widetilde{O}(\Lambda^2/\varepsilon^2)$ and $\widetilde{O}(\Lambda/\varepsilon^2)$, respectively, with only logarithmic dependence on the number of qubits. The algorithm is built directly from finite-time channel probes. It runs the unknown evolution for short times, estimates the corresponding Pauli transfer matrices from classical shadows, and converts these estimates into Lindbladian coefficients by stable local Fourier inversions. The algorithm is non-adaptive, uses no ancillas, and requires only random product states as inputs followed by random Pauli measurements. The method does not require knowing the structure of the Lindbladian in advance. We prove matching lower bounds that establish the near-optimality of the algorithm in both resources. By constructing a family of single-qubit dephasing Lindbladians, we show that any algorithm, even an adaptive one with arbitrary ancillas and measurements, requires $\Omega(\Lambda^2/\varepsilon^2)$ experiments and $\Omega(\Lambda/\varepsilon^2)$ total evolution time. In particular, the lower bounds imply that the Heisenberg-limited scaling achievable for Hamiltonian learning is information-theoretically impossible once dissipative coefficients must be estimated.

We investigate the magnetic ground states and transverse spin excitations of bilayer and trilayer nickelates within a multi-orbital itinerant framework. For the bilayer system, although Hartree-Fock calculations slightly favor a double-stripe order, the calculated excitation spectrum of the single-stripe state, characterized by an anisotropic low-energy cone at $Q_{\text{BL}}$ and isotropic high-energy excitations near $\Gamma$, exhibits good qualitative agreement with recent RIXS and neutron scattering experiments. We further identify mirror-even optical interlayer modes at $Q_{\text{BL}}$ whose energies match the mirror-odd modes at $\Gamma$. For the trilayer system, both mirror-odd and mirror-even spin-density-wave states can be stabilized near $Q_{\text{TL}}$, with the mirror-odd state lower in energy in the parameter regime studied. The mirror-odd state hosts an additional nearly gapless mode dominated by the middle layer, while the mirror-even state contains only one acoustic branch together with two gapped optical modes. Comparison with available RIXS data favors the mirror-odd spin-density-wave scenario. Our results show that magnetic excitations provide a sensitive probe of the magnetic order and support a common itinerant origin of magnetism in multilayer nickelates.

This work investigates the electric, thermal, and thermoelectric responses of a rotating pion gas of finite transverse radius in the presence of a background magnetic field, with the rotation axis aligned with the magnetic field. We explicitly calculate the parameter limits for $\pi^+$ condensation and restrict our working regime safely outside these boundaries, ensuring well-behaved transport coefficients. Notably, the system exhibits a condensation asymmetry, with $\pi^-$ remaining uncondensed at the parameters that induce $\pi^+$ condensation. Using the Boltzmann Transport Equation under the Relaxation Time Approximation, we calculate the longitudinal electrical conductivity, thermal conductivity, and the Seebeck coefficient. Our results reveal a competing interplay between the magnetic field and rotation, highlighting the substantial impact of rotation on the medium's transport properties: while the magnetic field suppresses the transport coefficients in a static medium, rotation, acting as an effective chemical potential, introduces an energy shift that favors their increase. Beyond an angular velocity, this rotational enhancement overpowers the magnetic suppression, leading to an increase in the transport coefficients with increasing magnetic field. Finally, we analyze the relative significance of charge and heat transport through the Lorenz number, providing further insight into the transport characteristics of the rotating magnetized pion medium.

Two recent independent analyses of Belle II $B^+ \! \to \! K^+\nu\bar\nu$ data yield limits on ${\mathcal B}(B^+ \! \to \! K^+ a)$ – the two-body mode to a light invisible particle such as the QCD axion – differing by a factor of roughly four; we trace this to the choice of kinematic variable space. The central figure of merit is the resolution in the reconstructed di-neutrino invariant mass $q^2_{\rm rec}$: fine-grained binning resolves the narrow axion signal, while coarse binning dilutes it into a background-dominated range. A BDT axis trained on $B^+ \! \to \! K^+\nu\bar\nu$ adds little discriminating power for $B^+ \! \to \! K^+ a$, as this axis is largely uncorrelated with $q^2$. These expectations are confirmed by a set of numerical tests. The subleading shape systematics omitted from our $q^2_{\rm rec}$-based approach lower, not raise, the $B^+ \! \to \! K^+ a$ limit: by better accommodating the $B^+ \! \to \! K^+\nu\bar\nu$ shape, they leave less room for the axion signal, making our $q^2_{\rm rec}$-based bound conservative, if anything. A dedicated reanalysis confirms that the kinematic-axes choice alone accounts for the factor-of-four sensitivity difference, and that the $B^+ \! \to \! K^+ a$ bound varies sizeably within the $q^2_{\rm rec}\times\eta({\rm BDT}_2)$ space, depending on the SM-likeness of $B^+ \! \to \! K^+\nu\bar\nu$, thus losing the dual-probe feature of our $q^2_{\rm rec}$-based approach. These results point to a broader consideration: likelihoods dominated by BDT variables are of limited use for reinterpretations when the signal shape differs appreciably from the BDT's training signal. We therefore advocate that experimental collaborations publish likelihood projections in physical variable spaces alongside BDT-based likelihoods, to maximise the reinterpretability of their measurements.

We study the effects of string axion emission on dark matter production by light primordial black holes (PBHs), through both evaporation and superradiance. We show, in particular, that the Hawking emission of $\mathcal{O}(100-10^5)$ light axion species predicted in realistic string theory constructions can significantly enhance the efficiency of superradiance, given the associated increase in the PBH spin. The string axiverse thus significantly expands the parametric regions (dark matter mass and PBH mass and spin) for which a sizeable fraction of dark matter may presently be in the form of "micro-boson stars": the self-gravitating remnants of superradiant dark matter clouds. Conversely, for too large a number of axion species PBHs evaporate too quickly for superradiant clouds to attain their maximum mass. Finally, assuming that all dark matter is produced by PBHs (through both superradiance and Hawking emission), we show that the axions emitted during PBH evaporation give an immeasurably small contribution to the relativistic degrees of freedom at recombination.

Variational infinite matrix product state (iMPS) calculations usually make Hamiltonians with algebraically decaying interactions compatible with standard MPO algorithms by first replacing the target Hamiltonian with a finite-pole sum-of-exponentials surrogate, thereby introducing a Hamiltonian-representation residual. We formulate the fixed-$D$ variational energy without introducing such a surrogate. For a fixed finite-$D$ MPS, the algebraic tail can be summed directly through the connected transfer matrix: the tail $e^{\mathrm{i} Qr}/r^\alpha$ is represented by the matrix function $F_{\alpha,Q}(\widetilde{T}_A)$, with $F_{\alpha,Q}(z)=\operatorname{Li}_\alpha(e^{\mathrm{i} Q}\,z)/z$. We evaluate the resulting matrix-function action using a Krylov method and obtain stable gradients by combining a Fréchet adjoint with implicit fixed-point differentiation. Benchmarks on long-range free fermions and the inverse-square Heisenberg family, including the Haldane–Shastry point, validate the transfer-matrix-function formulation. A long-range Ising-chain calculation illustrates a practical consequence of avoiding a finite-pole Hamiltonian representation. At a fixed, independently known critical field, finite-pole surrogate Hamiltonians can bias a critical diagnostic away from criticality, whereas the matrix-function calculation retains the expected critical signatures of the target algebraic Hamiltonian.

The process matrix formalism describes quantum correlations in scenarios without a fixed causal order between local laboratories. Operational signatures of such correlations can be investigated through causal games. A paradigmatic example is the Guess-Your-Neighbour's-Input game, in which two parties attempt to guess each other's inputs. Correlations compatible with any definite, or probabilistically mixed, causal order cannot achieve a winning probability exceeding $1/2$. The best process-matrix strategy currently known attains a value of approximately $0.6218$ using local dimension $d=5$, while the strongest known dimension-independent upper bound is $0.7592$. In this work, we investigate whether increasing the local dimension beyond $d = 5$ can narrow this gap. To this end, we employ a see-saw optimization scheme in which each step is formulated as a semidefinite program. For scalability, we develop a custom implementation of the SCS solver in which the dominant computational cost, the projection onto the positive-semidefinite cone, is offloaded to a GPU, yielding a six-fold speedup. Using this implementation, we explore local dimensions up to $d = 8$, and we do not find significant improvements over the value at $d=5$. Our results suggest that either qualitatively different strategies are required to approach the known upper bound, or that the bound itself is not tight.

We introduce the Frontier decoder, a pruned dynamic-programming decoder for sparse quantum decoding problems. Frontier processes error variables in a chosen order, merges prefixes with the same residual syndrome and logical label, and approximates logical-coset posterior masses by retaining only a narrow scored frontier. Without pruning, the recursion is exact ordered inference with exponential complexity. In the code-capacity setting, the decoder reaches thresholds close to optimal for the surface code and the color code. In the circuit-level noise model, it achieves state-of-the-art performance with a very small average retained list size: less than 100 for the gross code $[[144,12,12]]$ at a physical error rate of $0.001$. When the list size is constant, the decoder has linear complexity, suggesting the possibility of low-latency implementations.

In this work, we develop a state estimation framework for two-dimensional Rayleigh-Bénard (RB) convection that combines a stable Galerkin reduced-order model (ROM) with an extended Kalman filter (EKF). The ROM, constructed from controllability modes of the linearized Boussinesq equations, provides the nonlinear dynamical model for the filter prediction step. Direct numerical simulations (DNS) are used to generate synthetic measurements for data assimilation. We assess filter performance across periodic, quasiperiodic, and chaotic regimes, demonstrating that the filter tracks the most energetic modes with high fidelity and achieves time-averaged reconstruction errors below $16\%$ for velocity and $19\%$ for temperature. We apply the ROM-based EKF to a hybrid simulation scenario where the system state is assimilated from velocity measurements. It is shown that velocity observations alone suffice to reconstruct the state, including the temperature field. Finally, we exploit the Kalman gain matrix to develop a greedy sensor placement strategy that progressively removes the least informative sensors. The algorithm reveals a clear hierarchy among sensor types and can be used to derive skeletal observation configurations. It also provides guidance on which measurement variables and spatial locations are most informative for state correction. The present framework is general, and may be applied to other quadratic Galerkin ROMs for state estimation.

We consider protocols for control of dipolar Bose-Einstein condensates where the critical role is played by the long-range anisotropic interatomic magnetic dipole-dipole interaction. The phase diagram of such a condensate has been explored theoretically and experimentally with certain values of the interatomic scattering length corresponding to superfluid and supersolid phases, where supersolidity appears as a modulation in the ground state density. Preparation of this modulated ground state is challenging, since excitations appear as a result of a finite-time evolution required to produce qualitative changes in the wavefunction density. To solve this problem we consider the time-dependent control of a dipolar Bose-Einstein condensate using shortcuts to adiabaticity techniques, concentrating on design of the time-dependent scattering length, a parameter of the system easily tunable by contemporary experiments. The first technique is the variational approach based on the Euler-Lagrange equations for a separable ansatz describing the evolution of the superfluid state. Secondly, we study the transition from superfluid to supersolid using a direct optimization protocol. We discuss the fidelity of the developed protocols in terms of the evolution time.

We investigate the spectral properties of the Anderson operator perturbed by a localized negative potential, \(-V\). Specifically, we analyze the random Schrödinger operator defined by \(H = -\Delta +\ve \sum_{n} \omega_n \chi_n - V\), where the unperturbed operator exhibits a disordered energy landscape. Our primary focus is to establish precise estimates on the number of negative eigenvalues (bound states) induced by the attractive perturbation. By analyzing the competition between Anderson localization and the binding capacity of the potential, we provide quantitative bounds on the discrete spectrum. These results offer new insights into how randomness enhances the eigenvalue bounds.

We present a systematic study of von Neumann entropy estimation in multi-qutrit quantum systems using two complementary approaches: variational quantum algorithms (VQAs) and classical convolutional neural networks (CNNs), evaluated using an ideal (noise-free) quantum simulator. For systems up to three qutrits, we construct and evaluate 11 hardware-efficient SU(3)-inspired ansatzes. A parameter sweep shows that estimation accuracy is primarily determined by the number of trainable parameters, provided sufficient entanglement is present. Based on this study, we fix the parameter count to approximately 120 for subsequent experiments, observing that increasing entangling-gate counts beyond a threshold yields only marginal improvements. For larger systems (two to five qutrits), we use a CNN trained on measurement outcomes from tensor-product mutually unbiased bases. The model achieves accurate and stable predictions and exhibits a systematic improvement in performance with system size, with the highest errors for two-qutrit systems and the lowest for five-qutrit systems. Notably, using only 12.5% of the measurements required for full state tomography is sufficient to reach 90th-percentile absolute errors of approximately 0.13-0.16 nats for both four- and five-qutrit systems. The CNN model is also robust to shot noise and generalizes well to out-of-distribution states. Overall, within the simulated settings studied here, our results indicate a transition in practical methods: VQAs are effective for small systems, while CNN-based estimators offer improved scalability and robustness for larger qutrit systems.

Neutral atom quantum computers (NAQC) are emerging as a promising, scalable quantum computing platform because of their long qubit coherence, flexible qubit arrangement, and multiqubit gate capabilities. However, circuit execution often requires physically moving qubits, making compilation a critical optimization challenge. We propose a circuit independent mathematical framework built on graph-theoretic combinatorial optimization that determines the minimal number of required qubit transfers. This model captures spatial constraints specific to NAQC platforms with zone-limited gate operations and multi-qubit gates. From this framework, we encode the qubit mapping problem as a nonlinear integer program and solve it using a genetic algorithm, enabling trade-offs between minimizing the total traveled distance and the number of parallel transfer operations. Compared to the state-of-the-art scalable compiler for zoned architectures, our approach consistently finds fewer transfers. Depending on the optimization focus, our method produces shorter traveled distances or fewer parallel transfer operations. This work provides both theoretical guaranties and a practical tool for efficient, architecture-aware quantum circuit compilation. As a result, practitioners can generate hardware-aware mappings that reduce movement-induced errors and better exploit atom transfer parallelism, directly improving execution efficiency on NAQC devices.

As quantum-computing platforms are susceptible to noise, the fidelity of quantum operations is limited by decoherence. Understanding this limitation is crucial for building utility-scale quantum processors. In previous works [Phys. Rev. Lett. 129, 150504 (2022); Quantum 9, 1684 (2025)], we presented analytical formulae for the average gate fidelity of multi-qubit operations under static Markovian noise processes, including operations that temporarily leave the computational subspace. However, some quantum-computing architectures dynamically modulate qubit or coupler frequencies to implement two-qubit gates, e.g., baseband flux gates; such modulation can lead to dissipation rates varying in time. In this Letter, we therefore generalize the fidelity-reduction formulae to encompass time-dependent dissipation. Applying our generalized formula, we obtain a fidelity bound for adiabatic operations and demonstrate that flux-dependent noise sensitivity, combined with qubit-coupler hybridization, significantly reduces the fidelity of adiabatic controlled-Z (CZ) gates in superconducting quantum computers. Our work thus provides essential theoretical tools for evaluating error budgets and optimizing the design of quantum operations in tunable quantum-computing architectures, and may also find applications in quantum-sensing and quantum-communication protocols that are affected by time-dependent dissipation.

Amorphous silicon (a-Si) is understood to be the canonical continuous random network material, ideally defined by fully fourfold coordination. Here, we show that a defect-free ('ideal') model of a-Si from machine-learning-driven molecular-dynamics simulations [L. A. M. Rosset et al., Nat. Commun. 16, 2360 (2025)], subsequently evaluated with hybrid-level density-functional theory computations, can accurately reproduce the experimentally observed electronic bandgap. We compare this model with one resulting from the Wooten-Winer-Weaire (WWW) bond-switching approach and with other recent approximants to ideal a-Si. More broadly, our work provides a platform for studies of band tails, optical properties, and transport in a-Si.

In this study of the eclipsing shell star binary HD 48914 (V505 Mon) existing photometric and radial velocity data have been completed by an unbiased determination of the surface temperature of the companion star and new data from the TESS mission. For the mean effective temperatures in our equatorial view, polar/equatorial radii and masses we get about 16000 K (dimmed), 4.27 / 6.37 Rsun and 7.40 Msun for the Be star, and about 14400 K, 6.19 / 7.17 Rsun and 2.03 Msun for the companion star (a contracting hot subdwarf precursor). Their calculated rotational equatorial velocities both are surprisingly high: 467 km/s for the Be star rotating at its centrifugal limit as well as 131 km/s for its former donor star companion. The Be star is surrounded by a large equatorial decretion disk having a radius of about 65 Rsun and fits into the main sequence of stellar models at a sub-solar metallicity of Z = 0.006.

Navigating the vast space of synthetically accessible molecules demands surrogate models that are interpretable and capable of handling multiple competing objectives at the same time. Deep learning approaches struggle to satisfy them under the computational constraints of quantum-level chemistry. Here, we introduce a modular pipeline that combines interpretable linear meta-learning models and adaptive-confidence uncertainty quantification into an Efficient Global Optimization (EGO) framework for multi-objective molecular discovery. For the first time, linear meta-learning is deployed in a multi-objective chemical search setting: by training across chemical objectives and cheap auxiliary properties, the meta-learned surrogates acquire transferable chemical knowledge that adapts rapidly to new objectives from limited data. Evaluated empirically on a real large scale search for spin-crossover metal-organic complexes, the baseline performs 78% worse in Pareto sense than the meta-learning alternative. We also address the calibration challenges inherent to active search. Since optimal candidates typically lie precisely in the distributional tails, standard uncertainty estimates fail. We introduce an adaptive confidence-tuning algorithm that dynamically recalibrates the exploration-exploitation trade-off as the molecular search evolves. Empirically, dynamic confidence tuning further dominates over 50% of the statically calibrated front.

In the last years, an anomalously high spreading of West Nile virus (WNV) has been observed in Italy, with particularly high peaks of infections in southern Lazio, Campania and Veneto regions. The main disease vector for WNV is represented by Culex pipiens mosquitoes, which spread human infections through their bites. Here, we investigate WNV fever epidemic diffusion during summer season 2025 in Italy through a computational approach based on a quantum version of the Game of Life (GOL) cellular automaton model. Specifically, human dynamics evolves according to the GOL rules, while stochastic dynamics of disease vectors, i.e., mosquitoes, as well as their interaction with humans, simultaneously occur. We show that this model fits the curves of cumulative infected individuals with high accuracy, either at local and average-regional level, with only optimization of mosquito birth and removal rates parameters. Furthermore, leveraging model flexibility, we show that changes in model parameters values elucidate system response to environmental variations. For instance, we quantify, e.g., the impact of mosquito spreading containment measures or sudden mosquito increasing abundance due to climatic and ecological changes. Overall, we provide a general, quantitative description of WNV infection spreading in Italy which could represent a supportive tool to test different environmental scenarios and could be useful to devise strategies for decision makers to monitor disease vector dynamics and to control consequent virus diffusion.

It was proposed that extra dimensions can acquire large size by higher dimensional inflation connecting two large hierarchies in particle physics and cosmology, namely the weakness of the actual gravitational force to the largeness of the observable universe, in terms of one fundamental scale. This proposal is consistent with the observed approximate scale invariant power spectrum of primordial density perturbations only for one or two extra dimensions of around the micron size. Assuming a stabilisation mechanism of the extra dimensions at the end of inflation, here we propose a cosmological history that describes the Universe evolution after the end of inflation up to the reheating temperature, that guarantees the absence of bulk gravitons at earlier times, avoiding their overproduction in the early universe. The proposed cosmological history connects the period of higher dimensional inflation to the beginning of the standard cosmology.

This paper develops a general framework for analyzing multi-agent systems with feedback loops between agents actions and collective observations. The framework is built on two fundamental agent-level variables: power, which measures agent influence on collective outcomes, and response functions, which determine how agents react to observations. We derive how macroscopic properties, including total power, useful power, entropy, order, fragility, and mobility, emerge from these two variables of heterogeneous agents. To study the trade off between growth and resilience, we introduce a system-level utility function parameterized by a risk-appetite coefficient and derive an optimal degree of order that balances productivity, stability, and adaptability. The analysis suggests that stronger synchronization can increase collective output but may also increase systemic fragility and reduce mobility. We further argue that order, entropy, information, and useful energy are task-dependent and system-relative concepts whose meanings depend on the objectives of the system. By measuring and designing agent power distributions and response functions, it may be possible to better understand, predict, and optimize collective behavior and identify the conditions under which collective intelligence and optimal order emerge.

We consider a family of optimization problems, based on a mean-field description of particles interacting through Coulomb forces in a quadratic trap. In addition, the particles are constrained to lie in a halfspace and we are interested in the way the particle distribution changes as the halfspace varies. In particular, we can prove the existence of a phase transition, thereby settling a recent conjecture by Byun, Forrester, Majumdar and Schehr.

The explicit formulas for the spherical solutions of the Dirac equation in the expanding universe are given. The initial value of the solution can be, in particular, a wave function of the hydrogen-like atom or a spherical wave in the Minkowski space, that then propagates in the Friedmann-Lemaître-Robertson-Walker space-time, which is expanding with the de~Sitter scale factor.

Given a fixed past-directed timelike vector field, does there exist a time function whose gradient is optimally aligned with it? We address this question by introducing a functional that, on the one hand, captures the misalignment between the timelike vector field and the gradients of suitable Sobolev functions, and, on the other hand, penalizes null gradients. Our analysis focuses on compact subsets of smooth stably causal spacetimes. More precisely, we prove that, under suitable assumptions on the Sobolev index and the strength of the null gradient penalization, there exists a unique smooth temporal function which minimizes the considered functional. We refer to this minimizer as the alignment time function. Furthermore, several useful properties of the alignment time function are established: there exists a canonical procedure to improve its steepness, it is stable under $C^{p}$ convergence of the underlying metrics and vector fields and it inherits the symmetries shared by the metric and the given vector field.