Determining the pulse duration of femtosecond electron bunches is challenging and often experimentally invasive. An effective method for measuring the duration based on the time-dependent variations in electron beam divergence induced by a passive plasma lens is described. Reconstruction of the temporal shape of the electron bunch down to $c\times dt=10\mathrm{n}\mathrm{m}$ ($\sim 30\mathrm{a}\mathrm{s}$) without external RF cavities or multioctave spanning spectrometer is shown numerically. Experimental data from an $\sim 3$ fs electron bunch demonstrates practical applicability of this method. While this approach can be used with any high-current electron beam, it is particularly well suited to laser-driven and particle-driven wakefield accelerators, which accommodate electron beams with a time-dependent beam energy (e.g., “chirp”).

We present a general open-quantum-system framework to model decoherence induced by stochastic Planck-scale fluctuations of spacetime, focusing on the $\kappa$-Minkowski noncommutative geometry as a representative quantum gravity scenario. Treating the deformation parameter as Gaussian white noise, we derive a Lindblad-type master equation applicable to arbitrary quantum systems and obtain a distinctive inverse-energy scaling of the decoherence rate, $\mathrm{\Gamma }\propto E^{-4}$. As an illustrative example, we analyze a three-level system motivated by neutrino flavor oscillations and derive closed-form expressions for survival and transition probabilities with spacetime-induced damping. The $E^{-4}$ scaling contrasts sharply with the positive power laws often invoked in quantum gravity phenomenology and predicts negligible decoherence for high-energy neutrinos—consistent with IceCube observations—while implying that the strongest effects arise in the extreme low-energy regime. In this context, the sub-eV-scale energies characteristic of the cosmic neutrino background provide a natural infrared benchmark for illustrating the enhanced sensitivity to quantum spacetime fluctuations. Our results establish a unified formalism connecting quantum information methods, open-system dynamics, and quantum spacetime phenomenology, thereby offering a framework for exploring potential signatures of Planck-scale physics in future low-energy neutrino studies.

We introduce a model-independent framework to reinterpret Belle II results using only public data, analytically reconstructing the mapping between true and reconstructed kinematic variables within the statistically dominant Inclusive Tagging Analysis. This enables rare-decay measurements to probe light invisible particles—such as the QCD axion or axionlike particles, collectively denoted by $a$—without relying on internal simulations. Applying the method to $B^{+}\to K^{+}\nu \overline{\nu }$ yields the strongest bound on the branching fraction for $B^{+}\to K^{+}a$, improving existing limits by about a factor of 9 and constraining the axion's fundamental flavor-changing coupling to $b$ and $s$ quarks. The approach establishes $B^{+}\to K^{+}\nu \overline{\nu }$ as a dual probe—simultaneously testing short-distance new physics and light invisible states, the two probes working independently to an excellent approximation—and provides a general strategy for model-independent reinterpretation of collider data.

Quantum dynamics compilation is an important task for improving quantum simulation efficiency: It aims to synthesize multiqubit target dynamics into a circuit consisting of as few elementary gates as possible. Compared to deterministic methods such as Trotterization, variational quantum compilation (VQC) methods employ variational optimization to reduce gate costs while maintaining high accuracy. In this work, we make a paradigm shift in VQC’s framework design. First, we use a cost function from recently established out-of-distribution generalization results in quantum machine learning: By learning the action of a given many-body dynamics on a small dataset of product states, we can obtain a unitary circuit that generalizes to highly entangled states such as the Haar random states. Moreover, a key observation is that the low complexity in the data states allows us to use tensor-network methods to compress such time-evolved product states by exploiting their low entanglement features. Utilizing warm start methods, our approach exceeds state-of-the-art compilation results in both system size and accuracy in one dimension. For the first time, we extend VQC to systems to two-dimensional systems, demonstrating a significant resource advantage over standard Trotterization methods. The flexibility of our framework highlights the method's promise for advancing quantum simulation tasks on near-term quantum processors.

${\mathrm{SrVO}}_3$ has long been considered a prototype for studying electronic correlations due to its simple electronic structure and coherent $t_{2g}$ quasiparticle band. However, the origin of the incoherent satellite at 1.5 eV binding energy seen in photoemission remains enigmatic, having been attributed to Hubbard physics, oxygen vacancies, or plasmonic coupling. Here, we investigate the $t_{2g}$ band and satellite feature in epitaxially strained thin films of ${\mathrm{SrVO}}_3$ using angle-resolved photoemission spectroscopy. We observe an increase in the quasiparticle effective mass due to tensile strain as a result of the narrowing of the corresponding bandwidth. While a low-energy kink at 60 meV indicates additional electron-phonon interactions, no strain-dependent behavior is detected. The increased effective mass coincides with a decrease in the plasma frequency from reported ellipsometry measurements and theoretical calculations. From this, we associate the plasma frequency with the quasiparticle-satellite energy separation that follows the same strain-induced changes. This consistency affirms the identification of the satellite feature as a replica band due to coherent coupling between quasiparticles and low-energy plasmons and emphasizes the role of dynamical screening as a key organizing principle in the electronic structure of correlated oxides.

Quantum phenomena such as entanglement provide powerful resources for enhancing classical sensing. Here, we theoretically show that collective entanglement of spin qubits, arising from a condensation of particle-hole pairs, can strongly amplify transitions between ground and excited spin states, potentially improving signal contrast in optically detected magnetic resonance. This collective state exhibits an $\mathcal{O}\left(\sqrt{N}\right)$ enhancement of the transition amplitude with respect to an applied microwave field, where $N$ is the number of entangled spin qubits. We computationally realize this amplification using an ensemble of $N$ triplet spins with magnetic dipole interactions, where the largest transition amplitudes occur at geometries for which the condensation of particle-hole pairs is strongest. This effect, robust to noise, originates from the concentration of entanglement into a single collective mode, reflected in a large eigenvalue of the particle-hole reduced density matrix—an entanglement witness of condensation analogous to off-diagonal long-range order, though realized here in a finite system. These results offer a design principle for quantum sensors that exploit condensation-inspired entanglement to boost sensitivity in spin-based platforms.

Phase estimation in quantum interferometry is a major scenario where the quantum advantage is significantly revealed. Recently, the optimal finite-dimensional probe states (OFPSs) for phase estimation in two-mode quantum interferometry have been provided with the absence of noise [Qin et al., Phys. Rev. A 112, 052428 (2025)]. However, the noise is inevitable in practice and the previously obtained OFPSs may cease to be optimal anymore. Hence, the forms of the true OFPSs in the existence of various noises are still open questions. Here, the noise of particle loss is studied and the true OFPSs under this noise have been investigated with the numerical algorithm named constrained optimization by linear approximation. Furthermore, a two-step measurement strategy is proposed to realize the ultimate precision limit in practice. The validity of this strategy is confirmed by the numerical simulation of practical experiments.

Continuous-variable Gaussian entanglement is an attractive notion, both as a fundamental concept in quantum information theory, based on the well-established Gaussian formalism for phase-space variables, and as a practical resource in quantum technology, exploiting in particular, unconditional room-temperature squeezed-light quantum optics. The readily available high level of scalability, however, is accompanied by an increased theoretical complexity when the multipartite entanglement of a growing number of optical modes is considered. For such systems, we present several approaches to reconstruct the most probable physical covariance matrix from a measured nonphysical one and then test the reconstructed matrix for different kinds of separability (factorizability, concrete partite separability, or biseparability) even in the presence of measurement errors. All these approaches are based on formulating the desired properties (physicality or separability) as convex optimization problems, which can be efficiently solved with modern optimization solvers, even when the system grows. To every optimization problem, we construct the corresponding dual problem used to verify the optimality of the solution. Besides this numerical part of work, we derive an explicit analytical expression for the symplectic trace of a positive definite matrix, which can serve as a simple witness of an entanglement witness, and extend it for positive semidefinite matrices. In addition, we show that in some cases our optimization problems can be solved analytically. As an application of our analytical approach, we consider small instances of bound entangled or genuine multipartite entangled Gaussian states, including some examples from the literature that were treated only numerically, and a family of non-Gaussian states.

Graph neural networks (GNNs) are increasingly widely used for community detection in attributed networks. They combine structural topology with node attributes through message passing and pooling. However, their robustness or lack thereof with respect to different perturbations and targeted attacks in conjunction with community detection tasks is not well understood. To shed light on latent mechanisms behind GNN sensitivity on community detection tasks, we conduct a systematic computational evaluation of six widely adopted GNN architectures graph convolutional network, graph attention network, graph sample and aggregate (GraphSAGE), differentiable pooling (DiffPool), minimum cut pooling (MinCUT), and deep modularity networks (DMoN). The analysis covers three perturbation categories: node attribute manipulations, edge topology distortions, and adversarial attacks. We use element-centric similarity as the evaluation metric on synthetic benchmarks and real-world citation networks. Our findings indicate that supervised GNNs tend to achieve higher baseline accuracy, while unsupervised methods, particularly DMoN, maintain stronger resilience under targeted and adversarial perturbations. Furthermore, robustness appears to be strongly influenced by community strength, with well-defined communities reducing performance loss. Across all models, node attribute perturbations associated with targeted edge deletions and shifts in attribute distributions tend to cause the largest degradation in community recovery. These findings highlight important trade-offs between accuracy and robustness in GNN-based community detection and offer insights into selecting architectures resilient to noise and adversarial attacks.

Machine learning (ML) has become a versatile tool for analyzing anomalous diffusion trajectories, yet most existing pipelines are trained on large collections of simulated data. In contrast, experimental trajectories, such as those from single-particle tracking (SPT), are typically scarce and may differ substantially from the idealized models used for simulation, leading to degradation or even breakdown of performance when ML methods are applied to real data. To address this mismatch, we introduce a wavelet-based representation of anomalous diffusion that enables data-efficient learning directly from experimental recordings. This representation is constructed by applying six complementary wavelet families to each trajectory and combining the resulting wavelet modulus scalograms. We first evaluate the wavelet representation on simulated trajectories from the andi-datasets benchmark, where it clearly outperforms both feature-based and trajectory-based methods with as few as 1000 training trajectories and still retains an advantage on large training sets. We then use this representation to learn directly from experimental SPT trajectories of fluorescent beads diffusing in F-actin networks, where the wavelet representation remains superior to existing alternatives for both diffusion-exponent regression and mesh-size classification. In particular, when predicting the diffusion exponents of experimental trajectories, a model trained on 1200 experimental tracks using the wavelet representation achieves significantly lower errors than state-of-the-art deep learning models trained purely on ${10}^6$ simulated trajectories. We associate this data efficiency with the emergence of distinct scale fingerprints disentangling underlying diffusion mechanisms in the wavelet spectra. These findings position the wavelet representation as a data-efficient basis for machine-learning analysis of anomalous diffusion, reducing reliance on simulation-based training and enabling more direct use of experimental trajectories.

The manipulation of single spins through spin-polarized tunneling opens pathways for quantum control at the atomic scale. We present a theoretical framework describing spin transfer, spin torques, and spin resonance in molecular quantum dots weakly coupled to magnetic electrodes. By deriving a Lindblad master equation from microscopic tunneling processes, we capture both coherent exchange interactions and dissipative spin torque effects within a unified approach. We analyze how charge transport through localized orbitals influences spin dynamics and show that modulating the tunneling rates in time can induce electron spin resonance. This framework is further extended to coupled spin systems, revealing how spin coherence and entanglement respond to local spin torques and highlighting sources of transport-driven decoherence. Our results provide a general model to interpret spin-resolved tunneling experiments and extend classical spin torque concepts into the quantum regime.

The indirect estimation of couplings in quantum dynamics relies on the measurement of the spectrum and the overlap of eigenvectors with some reference states. These data can be obtained by local measurements on some sites and eliminate the need for full Hamiltonian tomography. For a one-dimensional (1D) chain, access to only one edge site is sufficient to compute all the couplings between the adjacent sites, and consequently to reconstruct the full Hamiltonian. However, its robustness in the presence of perturbations remains a critical question, particularly when sites interact with other lattice sites beyond nearest neighbors. Our work studies the applicability of schemes designed for 1D chains to topologies with interactions beyond nearest neighbor. We treat interactions between the next-nearest sites as perturbation of strength $\varepsilon$ and show that the error in estimation of couplings scales linearly with $\varepsilon$ in the presence of such interactions. Further, we show that, on average, the existence of couplings between sites beyond the next-nearest neighbor results in higher error. We also study the length of the chain that can be estimated (up to a fixed precision) as a function of $\varepsilon$, in the presence of next-nearest-neighbor interactions. Typically, for weak interactions, chains of 30 sites can be estimated within reasonable error. Thus, we study the robustness of estimation scheme designed for a 1D chain when exposed to such multisite perturbations, offering valuable insights into its applicability and limitations.

High-$T_c$ superconductivity has recently been discovered in Ruddlesden-Popper (RP)-phase nickelates under pressure, where the low-energy electronic structure is dominated by Ni $d_{x^2-y^2}$ and $d_{z^2}$ orbitals. However, the respective roles of these orbitals in superconductivity remain unclear. Here, by combining x-ray absorption, electron energy loss spectroscopy, and density functional theory calculations on ${\mathrm{La}}_4{\mathrm{Ni}}_3{\mathrm{O}}_{10}$ single crystals, we identify ligand holes in the $p_{x,y}$ orbitals of planar oxygen and the $p_z$ orbitals of apical oxygen, which hybridize with the Ni $d_{x^2-y^2}$ and $d_{z^2}$ orbitals, respectively. These ligand holes enable orbital-selective O $K$-edge resonant inelastic x-ray scattering (RIXS) study, which reveals that $d_{x^2-y^2}$ states dominate the low-energy charge excitations and are more itinerant. We also observe an $\sim 0.1\mathrm{eV}$ bimagnon through RIXS and Raman spectroscopy. Our results reveal distinct contributions of Ni $d_{x^2-y^2}$ and $d_{z^2}$ orbitals to the electronic and magnetic structure and provide direct experimental insights to understand the RP-phase nickelate superconductors.

Non-Gaussian quantum states of light are essential resources for quantum information processing and precision metrology. Among them, generalized coherent states (GCSs), which naturally arise from the evolution of a coherent state with a nonlinear medium, exhibit useful quantum features such as Wigner negativity and metrological advantages [Phys. Rev. Res. 5, 013165 (2023)]. Because these states remain coherent to all orders, their nonclassical character cannot be revealed through standard intensity-intensity correlation measurements. Here, we demonstrate that the intensity-field correlation function alone provides a simple and experimentally accessible witness of nonclassicality. For GCSs, any deviation of this normalized correlation from unity signals nonclassical behavior. We derive analytical results for Kerr-generated states and extend the analysis to statistical mixtures of GCSs. The proposed approach enables real-time, low-complexity detection of quantum signatures in non-Gaussian states, offering a practical tool for experiments across a broad range of nonlinear regimes.

We reassess the capacity for multimessenger inference of AT2017gfo/GW170817 using both kilonova and gravitational wave emission within the context of a recent simulation-based surrogate model for kilonova emission. Independent of the inclusion of gravitational wave observations, comparisons between observations that incorporate our kilonova model favor a narrow range of ejecta properties, even when allowing for a wide range of systematic uncertainties in our modeling approach. Conversely, we find that astrophysical conclusions about the neutron star itself, including its mass and radius, depend strongly on assumptions about how much material is ejected from the neutron star. Looking forward, our analysis highlights the importance of systematic uncertainty in general and the need for better modeling of neutron star merger mass ejection from first principles, and warns against uncontextualized applications of ejecta predictions using fits to numerical relativity simulations.

Determining the quantum-classical boundary between quantum circuits that can be efficiently simulated classically and those that cannot remains a fundamental question. One approach to classical simulation is to represent the output of a quantum circuit as a Clifford-augmented matrix product state (CAMPS), which, via a disentangling algorithm, decomposes the wave function into Clifford and MPS components, and from which Pauli expectation values can be computed in time polynomial in the MPS bond dimension. In this work, we develop an optimization-free disentangling (OFD) algorithm for Clifford circuits either doped with $T$ gates or, equivalently, preceded by multiqubit gates of the form $\alpha I+\beta P$. We give a simple and easily computed algebraic criterion that characterizes the individual quantum circuits for which OFD generates an efficient CAMPS—the bond dimension is exponential in the null space of a Galois binary field GF(2) matrix induced by a tableau of the twisted Pauli strings $P$. This significantly increases the number of circuits with rigorous polynomial-time classical simulations. We also give evidence that the typical $N$ qubit random Clifford circuit doped with $N$ uniformly distributed $T$ gates of polylogarithmic depth or greater has a CAMPS with polynomial bond dimension. In addition, we compare OFD against disentangling by optimization. We further explore the representability of CAMPS for random Clifford circuits doped with more than $N T$ gates. We also propose algorithms for sampling, probability, and amplitude estimation of bitstrings, and evaluation of entanglement Rényi entropy from CAMPS, which, though still having exponential complexity, are more efficient than standard MPS simulations. This work establishes a versatile framework for understanding classical simulatability of $\mathrm{Clifford}+T$ circuits and explores the interplay between quantum entanglement and quantum magic in quantum systems.

Stern-Gerlach interferometer (SGI) is a kind of matter-wave interferometer driven by magnetic field and has been proposed for various gravity experiments. Stochastic noises can lead to decoherence problems of SGI via various mechanisms. In this paper, I will theoretically study several mechanisms including dephasing, contrast loss, and position localization decoherence. I will first present a rigorous proof that the dephasing behaves as a linear response to the noise, with a transfer function given by the Fourier transform of the unperturbed classical trajectories. Then I will demonstrate that stochastic acceleration noise only induces dephasing to the witness operator constructed in spin space, while it does not lead to contrast loss or position localization decoherence due to common mode cancellation. In contrast, higher-order noise can induce both contrast loss and position localization decoherence, contributing a decay factor proportional to the noise power spectrum density at the intrinsic frequency, which can be physically interpreted as the resonance between the noise and test mass. Based on the result, I apply the framework to analyze two typical noise sources, magnetic field noise and quadratic noise.