We show that the spatial structure of electromagnetic vacuum fluctuations, predicted by quantum electrodynamics, can be indirectly observed using thermal noise at radio frequencies. Using simple laboratory equipment like coaxial cables and radio frequency splitters, we detect a clear suppression of thermal noise near conducting boundaries, mirroring the expected modulation of vacuum modes. This provides accessible, experimental evidence for quantum vacuum behavior without requiring advanced optics or cryogenics.

Evidence is mounting that species interactions often involve long-term memory, with highly varying waiting times between successive events and long-range temporal correlations. Accounting for memory undermines the common Markovian assumption, and dramatically impacts key ingredients of population dynamics including birth, foraging, predation, and competition processes. Here we study a critical aspect of population dynamics, namely, non-Markovian multispecies competition. This is done in the realm of the zero-sum rock-paper-scissors (zRPS) model that is broadly used in the life sciences to metaphorically describe cyclic competition among three interacting species. We develop a general non-Markovian formalism for multispecies dynamics, allowing us to determine the regions of the parameter space where each species dominates. In particular, when the dynamics are Markovian, the waiting times are exponentially distributed and the fate of the zRPS model in large well-mixed populations is encoded in a remarkably simple condition, often referred to as the “law of the weakest” (LOW), stating that the species with the lowest growth rate is the most likely to prevail. We show that the survival behavior and LOW of the zRPS model are critically affected by nonexponential waiting times and, especially, by their coefficient of variation. Our findings provide key insight into the influence of long waiting times on non-Markovian evolutionary processes.

We study the excitation spectrum of the $S=1/2$ Heisenberg model on the randomly diluted square lattice by stochastic analytic continuation of imaginary-time correlations obtained by quantum Monte Carlo simulations in the low-temperature limit. Focusing on relatively low dilution fractions, $p=1/16$ and $p=1/8$, the dynamic structure factor $S(\mathit{q},\omega )$ exhibits a strongly damped magnon peak with anomalous dispersion near $\mathit{q}=(0,0)$ and $(\pi ,\pi )$, a nondispersive low-energy peak from localized excitations, and a second peak within the continuum connecting these two features. A magnon with anomalous logarithmic dispersion, close to our result, was predicted in spin wave and $T$-matrix theory [Chernyshev et al., Phys. Rev. B 65, 104407 (2002)], with the quasiparticle description breaking down at the low localization energy. However, no intermediate mode was predicted. Analyzing spectral functions in real space for individual vacancy realizations by energy tomography, we find that these excitations are concentrated on a subset of the spins adjacent to vacancies, with fewer spins involved as the energy is lowered. We argue that the low-energy excitations are those of a sparse random network of effective moments at a fraction of the vacancies, leading to a damped diffusive-like behavior with localization at the lowest energies (the localized mode). In the case of the magnon, there is a shift in the real-space spectral weight distribution, from the spins away from vacancies at high energy to those adjacent to vacancies at lower energy. We also analyze the Anderson quantum rotor excitation at $\omega \propto N^{-1}$ (with $N=L^2$ the system size), which in the clean system is visible in $S(\mathit{q},\omega )$ only at $\mathit{q}=(\pi ,\pi )$ but spreads through the entire Brillouin zone due to the lack of translational invariance when $p>0$. Beyond weight close to $\mathit{q}=(0,0)$ and $(\pi ,\pi )$, which we explain by local sublattice imbalance within a classical dimer-monomer model, there is also intricate structure arising from correlated singlet fluctuations, which we demonstrate by enhancing said fluctuations with four-spin couplings in the Hamiltonian. The features in momentum space reflect spatially nonuniform breaking of the spin rotation symmetry and should be experimentally observable with elastic neutron scattering on layered quantum antiferromagnets doped with nonmagnetic impurities. All the $\omega >0$ excitations should be observable by inelastic neutron scattering. Technically, our work demonstrates that surprisingly complex spectral information can be obtained from quantum Monte Carlo data, despite the “ill-posed” numerical analytic continuation problem.

We perform a systematic study of the signatures of fragile topology in over 50 nonmagnetic two-dimensional materials with formula ${\mathrm{AB}}_2$, belonging to space group $P\overline{3}m1$. Using group theory analysis in the framework of topological quantum chemistry, we find fragile bands near the Fermi level for all the materials studied. Since stable topological bands are also present in these systems, the interplay of both phases is discussed, showing that corner charges appear in over 80% of the materials and are linked to fragile topology. Using first-principles calculations, we predict fractionally charged corner charges protected by $C_3$ symmetry. Our work aims to broaden the scope of materials with experimentally accessible fragile bands.

Electrohydrodynamics (EHD) are widely utilized to manipulate fluid behaviors in practical applications, such as EHD high-resolution printing and EHD nebulization for producing monodispersed small droplets. In these applications, multicomponent multiphase (MCMP) phenomena with large density contrasts are usually involved. However, previous studies either focused on modeling single-phase multicomponent EHD problems where the densities of different components are the same or multiphase single-component EHD flows with large density contrasts where only one fluid component is considered. Therefore, in this work, a hybrid numerical framework combining MCMP pseudopotential LBM and FDM is implemented and applied to simulate EHD flows, which is capable of simulating MCMP EHD flows with large density ratios and offers tunable surface tension that is independent of viscosity and density ratios. The numerical model is comprehensively validated through benchmarks, including the thermodynamic consistency test, Young-Laplace droplet test, contact angle test, and droplet deformation under an electric field. The results demonstrate the capability of the present model for accurately simulating EHD MCMP flows with high-density ratios, good thermodynamic consistency, low spurious velocity, adjustable surface tension, and wettability. Lastly, the developed model is applied to investigate the effects of fluid electrical properties and electric field strength on the deformation and wettability of a sessile droplet under an external electric field.

The Chern vector is a vectorial generalization of the scalar Chern number, being able to characterize the topological phases of three-dimensional (3D) Chern insulators. Such a vectorial generalization extends the applicability of Chern-type bulk-boundary correspondence from one-dimensional edge states to two-dimensional surface states, whose unique features, such as robust chiral surface states and forming nontrivial torus knots or links in the surface Brillouin zone, have been demonstrated recently in 3D photonic crystals. However, to date, the Chern vectors have been limited to the simplest form with only one nonzero component, resulting in the chiral surface states only propagating along four side surfaces parallel to the Chern vector, and the surface-state torus knots or links emerging, not on the surface of a single crystal as in other 3D topological phases, but only along an internal domain wall between two crystals with perpendicular Chern vectors. Here, we extend the 3D Chern insulator phase to acoustic crystals for sound waves and propose a scheme to construct an arbitrary Chern vector with multiple nonzero components that allow the chiral surface states to propagate along all six surfaces and the emergence of surface-state torus knots or links on the surface of a single crystal. These results provide a complete picture of bulk-boundary correspondence for Chern vectors and may have potential applications in designing topological acoustic devices.

Josephson junctions (JJs) with nonsinusoidal current-phase relations (CPRs) have gathered increasing attention, partly due to growing interest in topological two-dimensional materials. Understanding how CPRs and inhomogeneities in JJs influence their response is crucial for accurate interpretation of experimental observations. This work reports that a nonsinusoidal CPR, combined with asymmetries in the JJ, can break spatial symmetry and give rise to the Josephson diode effect (JDE) in the short-junction regime. This nonreciprocity is shown to emerge as an intrinsic mechanism related to the maximization of the supercurrent, rather than being solely driven by geometric or material asymmetries. Further analysis shows that JDE efficiency is strongly influenced by the CPR shape but is largely insensitive to junction asymmetry, making the observed nonreciprocity not only a potential experimental signature of unconventional CPRs but also a possible method for probing their properties.

Quantum universal invariants of general $N$-beam Gaussian fields are investigated from the point of view of fields' intensity moments. A method that uniquely links these invariants, including the global and marginal fields' purities, to intensity moments is suggested. Determination of these invariants identifies the Gaussian states including their quantum correlations. In particular, the Peres-Horodecki separability criterion is reformulated in terms of quantum universal invariants, and consequently in terms of experimental intensity moments, offering a practical tool for determining the entanglement or separability of these states. The approach is experimentally demonstrated by determining the invariants of noisy symmetric three-beam Gaussian states using photon-number resolved measurements. Furthermore, their entanglement properties are analyzed and characterized.

Spin qubits based on valence band hole states are highly promising for quantum information processing due to their strong spin-orbit coupling and ultrafast operation speed. As these systems scale up, achieving high-fidelity single-qubit operations becomes essential. However, mitigating crosstalk effects from neighboring qubits in larger arrays, particularly for anisotropic qubits with strong spin-orbit coupling, presents a significant challenge. We investigate the impact of crosstalk on qubit fidelities during single-qubit operations and derive an analytical equation that serves as a synchronization condition to eliminate crosstalk in anisotropic media. Our analysis proposes optimized driving field conditions that can robustly synchronize Rabi oscillations and minimize crosstalk, showing a strong dependence on qubit anisotropy and the orientation of the external magnetic field. Taking experimental data into our analysis, we identify a set of parameter values that enable nearly crosstalk-free single-qubit gates, thereby paving the way for scalable quantum computing architectures.

Deep neural network architectures often consist of repetitive structural elements. We introduce an approach that reveals these patterns and can be broadly applied to the study of deep learning. Similarly to how a power strip helps untangle and organize complex cable connections, this approach treats neurons as additional degrees of freedom in interactions, simplifying the structure and enhancing the intuitive understanding of interactions within deep neural networks. Furthermore, it reveals the translational symmetry of deep neural networks, which simplifies the application of the renormalization group transformation, a method that effectively analyzes the scaling behavior of the system. By utilizing translational symmetry and renormalization group transformations, we can analyze critical phenomena. This approach may open new avenues for studying deep neural networks using statistical physics.

We explore a nonvariational quantum state preparation approach combined with the ADAPT operator selection strategy in the application of preparing the ground state of a desired target Hamiltonian. In this algorithm, energy gradient measurements determine both the operators and the gate parameters in the quantum circuit construction. We compare this nonvariational algorithm with ADAPT-VQE and with feedback-based quantum algorithms in terms of the rate of energy reduction, the circuit depth, and the measurement cost in molecular simulation. We find that, despite using deeper circuits, this new algorithm reaches chemical accuracy at a similar measurement cost to ADAPT-VQE. Since it does not rely on a classical optimization subroutine, it may provide robustness against circuit parameter errors due to imperfect control or gate synthesis.

In this work, we consider superconductor/flat band material/superconductor (S/FB/S) Josephs on junctions (JJs) where the flat band material possesses isolated flat bands with exactly zero Fermi velocity. Contrary to conventional S/N/S JJs where the critical Josephson current vanishes when the Fermi velocity goes to zero, we show in this work that the critical current in the S/FB/S junction is controlled by the quantum metric length ${\xi }_{\mathrm{QM}}$ of the flat bands. Microscopically, when ${\xi }_{\mathrm{QM}}$ of the flat band is long enough, the interface bound states originally localized at the two S/FB, FB/S interfaces can penetrate deeply into the flat band material and hybridize to form Andreev bound states (ABSs). These ABSs are able to carry long range and sizable supercurrents. Importantly, ${\xi }_{\mathrm{QM}}$ also controls how far the proximity effect can penetrate into the flat band material. This stands in sharp contrast to the de Gennes' theory for S/N junctions which predicts that the proximity effect is expected to be zero when the Fermi velocity of the normal metal is zero. We further suggest that the S/FB/S junctions would give rise to a new type of resonant Josephson transistor which can carry a sizable and highly gate-tunable supercurrent.

The performance of inertial confinement fusion (ICF) implosions is sensitive to the three-dimensional (3D) morphology of the hot-spot and shell configurations. The ability to infer shell-mass uniformity and reconstruct 3D hot spots is crucial for quantifying the degradation of ignition criteria and improving symmetry in ICF implosion experiments. In this work, we present a deep-learning convolutional neural network (CNN) for reconstructing 3D hot-spot and shell structures for ICF capsules. The 3D geometry of the hot spot is reconstructed from x-ray images measured from multiple lines of sight on OMEGA. The shell configuration is inferred indirectly through machine learning using a convolutional neural network extensively trained on a dec3d simulation database. This simulation-dependent approach yields consistent agreement between reconstructed 3D shell densities and machine-learning optimized dec3d simulation results. This work demonstrates a CNN framework that successfully reconstructs 3D capsule structures from two-dimensional images in ICF implosions.

There are no clean samples in nature. Therefore, when we come to discuss the entanglement properties of quantum materials, the effects of disorder must be taken into account. This question is of particular interest for highly entangled phases, such as quantum spin liquids, which lie outside the Landau paradigm for classifying phases of matter. In this work, we explore what experimentally accessible measures, in the form of concurrence, residual tangle, and quantum Fisher information can teach us about the entanglement in the presence of disorder. As a representative example, we consider the Tomonaga-Luttinger liquids (TLL) and disorder-driven random singlet (RS) phases found in antiferromagnetic quantum spin chains. Using quantum Fisher information and residual tangle, we demonstrate that both TLL and RS phases exhibit multipartite entanglement. In the case of the RS phase, we attribute this to entanglement localized below a crossover length scale. We further show that the order of disorder average matters in calculating measures like concurrence and that this can lead to false inferences when interpreting experiments. Nonetheless, correctly interpreted, these witnesses provide useful information about the effects of disorder. We explore how information about the central charge of the TLL can be extracted from the low-temperature behavior of concurrence and conjecture that this analysis can be extended to the effective central charge of the RS phase. Finally, we establish how RS and TLL phases can be distinguished through the growth of multipartite entanglement, as witnessed by the equal-time structure factor. These results establish that, used carefully, experiments based on entanglement witnesses can provide important information about quantum spin systems in the presence of disorder.

Although the Harrow-Hassidim-Lloyd (HHL) algorithm offers an exponential speedup in system size for treating linear equations of the form $A\vec{x}=\vec{b}$ on quantum computers when compared to their traditional counterparts, it faces a challenge related to the condition number ($\kappa$) scaling of the $A$ matrix. In this work, we address the issue by introducing the postselection-improved HHL (Psi-HHL) framework that operates on a simple yet effective premise: subtracting mixed and wrong signals to extract correct signals while providing the benefit of optimal scaling in the condition number of $A$ (denoted as $\kappa$) for large $\kappa$ scenarios. This approach, which leads to minimal increase in circuit depth, has the important practical implication of having to use substantially fewer shots relative to the traditional HHL algorithm. The term “signal” refers to a feature of $|x\rangle$. We design circuits for overlap and expectation value estimation in the Psi-HHL framework. We demonstrate performance of Psi-HHL via numerical simulations. We carry out two sets of computations, where we go up to 26-qubit calculations, to demonstrate the ability of Psi-HHL to handle situations involving large-$\kappa$ matrices via (a) a set of toy matrices, for which we go up to size $64\times 64$ and $\kappa$ values of up to $\approx 1\times {10}^6$, and (b) application to quantum chemistry, where we consider matrices up to size $256\times 256$ that reach $\kappa$ of about 393. The molecular systems that we consider are ${\mathrm{Li}}_2$, KH, RbH, and CsH.

Quantum and classical machine learning have been naturally connected through kernel methods, which have also served as proof-of-concept for quantum advantage. Quantum embeddings encode classical data into quantum feature states, enabling the construction of embedding quantum kernels (EQKs) by measuring vector similarities and projected quantum kernels (PQKs) through projections of these states. However, in both approaches, the model is influenced by the choice of the embedding. In this work, we propose using the training of a quantum neural network (QNN) to construct neural quantum kernels, specifically neural EQKs and neural PQKs—problem-inspired kernel functions. Unlike previous approaches, our method requires the kernel matrix to be constructed only once, significantly reducing computational overhead. To achieve this, we introduce a scalable training method for an $n$-qubit data reuploading QNN. Furthermore, we demonstrate neural quantum kernels can alleviate exponential concentration and enhance generalization capabilities compared to problem-agnostic kernels, positioning them as a scalable and robust solution for quantum machine learning applications.

High-order harmonic generation driven by intense extreme ultraviolet (EUV) fields merges quantum optics and attosecond science, giving rise to an appealing route for the generation of coherent EUV and soft x-ray light for high-resolution imaging and spectroscopies. We theoretically investigate ultrafast resonant dynamics during the interaction of He atoms with strong extreme ultraviolet pulses. At high driving intensities, we identify record fast attosecond Rabi oscillations imprinting observable signatures in the high harmonic spectrum. At field strengths suppressing the Coulomb potential barrier for all the bound states, we demonstrate the survival of the attosecond two-level dynamics for several Rabi cycles. Consequently, this intense EUV laser-atom interaction reveals a strong-field scenario in which resonant coupling of two-level bound-bound transitions prevails, in contrast to the bound-continuum transitions typically dominant in conventional infrared strong-field regimes. Our findings of a high-frequency up-conversion regime combining two coexisting pathways—a two-level coupling and a bound-continuum coupling—set an interesting perspective for extreme attosecond nonlinear optics with intense short-wavelength laser fields.

Plasmon resonances of supported metal nanostructures are affected by the substrate in an anisotropic way, which gives rise to mode splitting and inhomogeneous energy shifts. We provide experimental evidence that the commonly observed and intuitive case of a weaker influence at larger distances to the substrate does not apply to the plasmon mode structure of small, quasispherical particles. Instead, for the latter, we observe that the energy shift induced by a carbon substrate is strongest if the plasmon is excited farthest from the support. This nonlocal behavior results from the plasmon mode geometry in conjunction with carbon-induced symmetry breaking, which is revealed by electron energy loss spectroscopy (EELS) of silver clusters deposited at the rim of a carbon film. Decomposing the experimental maps based on spectra obtained from the boundary element method (BEM) uncovers the full mode structure. These results strongly suggest that some of the persisting literature controversy on plasmon energies of small, spherical nanoparticles is due to unconsidered spectral effects of an anisotropic local environment.