An elastic arch is an archetypal bistable system. Here, we combine elastica theory and photomechanical experiments to elucidate the mechanics of an active arch with a spatiotemporally varying preferred curvature $\overline{\kappa }\left(s\right)$. Our shallow-arch theory completely describes any such system via the decomposition of its $\overline{\kappa }\left(s\right)$ into Euler-buckling modes. Intuitively, if $\overline{\kappa }\left(s\right)$ overlaps with the fundamental mode, it snaps the arch up/down. Conversely, nonoverlapping $\overline{\kappa }\left(s\right)$ drives a second-order transition to a higher-order shape. Furthermore, the form of $\overline{\kappa }\left(s\right)$ enables control over the instability’s character; we find the forms for snapping with maximum energy release and at the lowest stimulation (both binary patterns) and design forms for symmetric and asymmetric switching pathways. Analogous control can also be achieved in boundary-driven instabilities of passive arches by fabricating them with suitable $\overline{\kappa }\left(s\right)$. We thus anticipate that our results will improve switchable/snapping elements in micro-electromechanical systems, robotics, and mechanical metamaterials.

Classical shadow tomography has become a powerful tool in learning about quantum states prepared on a quantum computer. Recent works have used classical shadows to variationally enforce $N$-representability conditions on the two-particle reduced density matrix. In this paper, we build upon previous research by choosing an improved estimator within classical shadow tomography and rephrasing the optimization constraints, resulting in an overall enhancement in performance under comparable measurement shot budgets. We further explore the specific regimes where these methods outperform the unbiased estimator of the stand-alone classical shadow protocol and quantify the potential savings in numerical studies.

Symmetry constraints provide a powerful means to control the dynamics of open quantum systems. However, the set of accessible control parameters is often limited. Here, we show that a tunable phase in the collective light-matter coupling of a cavity QED system induces a phase-dependent strong symmetry of the Liouvillian, enabling dynamical control of the open quantum system evolution. We demonstrate that tuning this phase substantially reduces the critical driving strength for dissipative phase transitions between stationary and nonstationary phases. We illustrate this mechanism in two experimentally relevant cavity QED settings: a two-species ensemble of two-level atoms and a single-species ensemble of three-level atoms. Our results establish phase control as a versatile tool for engineering dissipative phase transitions, with implications for quantum state preparation.

We investigate how the space-time entanglement of photon pairs generated by multimode parametric down-conversion (PDC) propagates away from the source. We show that the entangled two-photon probability, tightly confined in space and time at the source, quickly broadens and decays out of a focal plane, behaving as a focused coherent beam of approximately twice the wavelength of the twin photons. This behavior is in strong contrast with the much slower decay rate of the classical mutual coherence of the PDC radiation, which follows the prescription of the generalized Van Cittert-Zernicke theorem. Besides their fundamental interest, our results could be relevant for the design of experiments, such as quantum-enhanced two-photon processes, that exploit the spatiotemporal entanglement of the pairs.

Establishing adaptive particles that sense their state, anticipate their evolution, and compute control inputs onboard has been a major challenge in nonequilibrium physics. We address this challenge by realizing an autonomous brainbot, building on a recently developed programmable bristlebot. First, we construct a physics-informed digital twin of the device, based on a kinematic model that reproduces measured trajectory statistics and generates long, statistically faithful synthetic trajectories. The kinematics forms the foundation for implementing onboard model predictive control, enabling autonomous trajectory tracking, demonstrated by accurate execution of a nontrivial target path. This provides a proof of principle for a brainbot that senses its state, predicts its evolution, and computes control inputs onboard, unlike conventional active particles with fixed motility, thereby transforming the brainbot into an agentic physical entity. By integrating physical modeling, data-driven parameter identification, and control into a unified framework, our approach provides a scalable platform for machine-learning-enabled multiagent studies and lays the groundwork for intelligent, adaptive active matter.

We investigate the interplay between charge, magnetic, and structural degrees of freedom in the isostructural and isoelectronic $5d^1$ double perovskites ${\mathrm{Ba}}_2{\mathrm{MgReO}}_6$ and ${\mathrm{Ba}}_2{\mathrm{NaOsO}}_6$. Using first-principles-based electronic structure calculations, we show that both materials exhibit a tendency toward spontaneous quadrupolar order in the cubic paramagnetic phase, which is slightly weaker in ${\mathrm{Ba}}_2{\mathrm{NaOsO}}_6$ than in ${\mathrm{Ba}}_2{\mathrm{MgReO}}_6$. Our analysis further reveals an intimate coupling between the local magnetic moments and charge quadrupoles, mediated by the strong spin-orbit interaction, that leads to the unusual canted configuration of magnetic moments observed in these systems. When structural degrees of freedom are included, the two materials exhibit pronounced differences. In ${\mathrm{Ba}}_2{\mathrm{MgReO}}_6$, the strong coupling to Jahn-Teller distortions stabilizes the antiferroic ${\mathcal{Q}}_{x^2-y^2}$ order, yielding excellent agreement with available experimental data. In contrast, the Jahn-Teller coupling is significantly weaker in ${\mathrm{Ba}}_2{\mathrm{NaOsO}}_6$ and appears insufficient to stabilize the antiferroic quadrupolar order. While this is consistent with the absence of any measurable long-range structural distortion above the magnetic transition temperature, it contrasts with experimental results indicating a strong canting of the magnetic moments. Our analysis thus successfully describes the mechanisms shaping the properties of the Re compound while a full quantitative description of the magnetic ground state of ${\mathrm{Ba}}_2{\mathrm{NaOsO}}_6$ is still elusive.

Efficient generation of radiation in the mid- and far-infrared relies primarily on lasers and coherent nonlinear optical phenomena driven by lasers. This wavelength range lacks practical and efficient luminescent devices, particularly, because the spontaneous emission rate becomes much longer than the nonradiative energy relaxation processes and therefore emitters have to count on stimulated emission produced by linear or nonlinear optical gain. However, spontaneous emission is not a fundamental property of the emitter. By engineering metamaterials composed of arrays of nanoemitters into microcavities coupled to patch antennas, we have demonstrated midinfrared electroluminescent devices emitting a collimated beam with excellent spatial properties and a factor of 100 increase in the collected power, compared to standard devices. Our results illustrate that by reshaping the photonic environment around emitting dipoles, as in the Purcell effect, it is possible to enhance the spontaneous emission and conceive efficient optoelectronic light-emitting devices that operate close to the thermodynamical equilibrium as light-emitting diodes in the visible range.

Quantum annealing is typically regarded as a tool for combinatorial optimization, but its coherent dynamics also offer potential for machine learning. We present a model that encodes classical data into an Ising Hamiltonian, evolves it on a quantum annealer, and uses the resulting probability distributions as feature maps for classification. Experiments on the quantum annealer machine with the Digits dataset, together with simulations on MNIST, demonstrate that short annealing times yield higher classification accuracy, while longer times reduce accuracy but lower sampling costs. We introduce the participation ratio as a measure of the effective support of the output probability distribution and show its strong correlation with generalization.

Traditional fixed-sign coupling schemes impose rigid directional constraints on interaction pathways, while integer-order differential models inherently lack the capacity to describe memory effects and long-range temporal correlations. To overcome these limitations, here we establish a fractional-order coupled Van der Pol oscillator system with a rotation parameter to investigate how the rotation parameter and fractional-order derivative control the dynamics. The rotation parameter distributes the coupling to the $x$ and $y$ variables via trigonometric functions, precisely regulating the sign and weight of coupling in each direction; this governs bifurcations and qualitatively determines the steady-state mode. Moreover, the memory effect of the fractional-order derivative exhibits quadrant dependence: As the fractional-order derivative decreases, the rotation parameter delays amplitude death in the second and third quadrants but accelerates incoherent oscillation death in the fourth quadrant. Finally, employing phase reduction theory, we derive the phase model and numerically verify the system’s transition path. Our findings break away from traditional frameworks, deepen the understanding of fractional-order coupled systems, elucidate the regulatory mechanisms of rotation parameters, and provide a theoretical foundation for controlling synchronization, chimera states, and death states in real-world systems.

Thermal counterflow of superfluid ${}^4\mathrm{He}$ past a cylinder produces quasisteady eddies not only downstream but also anomalously upstream. However, the mechanism and organizing principles behind the observed multistable wake topologies (zero-, two-, four-, and six-vortex states) have remained unclear. We show that the full spectrum of reported normal-fluid wake states is captured numerically with a two-fluid model coupled to Vinen’s vortex-line-density equation. Our simulations further reveal that the superfluid component can also develop anomalous upstream eddies, a feature not previously reported. We trace these behaviors to a self-organized zone of enhanced mutual-friction dissipation near the cylinder shoulders that reshapes the effective obstacle, drives upstream eddies in both components, and suppresses intrinsic wake oscillations in the normal fluid. Guided by this mechanism, we perform systematic parameter scans and construct a unified phase diagram in terms of the normal-fluid Reynolds number ${\text{Re}}_n$ and a dimensionless interaction number $N$, separating inertia- and mutual-friction-controlled transitions and delineating the parameter windows for the discrete wake topologies. These results turn a striking phenomenology into a predictive map and establish mutual-friction feedback as a robust route to unusual wake structures in quantum fluids.

The transport and localization of quantum droplets in time-dependent periodic disorder potentials are systematically studied. Our numerical results indicate that such exquisite periodically driven structures may support quantum phases that are principally different compared to static systems. We observe an intriguing anomalous diffusion, with a diffusion exponent depending on the strength of quantum fluctuations and on noise parameters (amplitude and frequency). We show that the interplay of quantum fluctuations and noise tends to suppress localization especially in a large droplet, while the transport is subdiffusive in a small droplet only for a very weak noise. For reducing modulation strength and at fixed dephasing time, the system may exhibit Anderson localization of the Floquet states in the small droplet. We also elucidate the role of thermal effects in the transport properties of quantum droplets. The obtained anomalous diffusion is justified in the frame of a heuristic model.

We apply machine learning methods to demonstrate radar range superresolution using a denoising autoencoder trained without supervision. Focusing on the estimation of a single physical parameter, the separation between two scatterers in the subwavelength regime, we constrain the network to a one-dimensional bottleneck layer with its size matched to the parameter dimensionality. We find that the bottleneck layer forms a reproducible, monotonic mapping with the true separation, showing that the network learns a low-dimensional representation directly aligned with the underlying physical parameter. We further show that this representation preserves the Fisher information of the signal, indicating that the network recovers a physically meaningful representation for parameter estimation. We investigate the behavior of the bottleneck layer for the following types of pulses: a traditional sinc pulse, a bandlimited triangle-type pulse, and a theoretically near-optimal pulse created from a spherical Bessel function basis. Our results demonstrate that neural networks can discover physically meaningful compressed representations without explicit supervision, with performance ultimately governed by the information content of the signal.

Turing patterns are a central paradigm for describing spatial patterns in nature. The corresponding theory of reaction-diffusion dynamics combines ideal diffusion with nonlinear reactions, resulting in patterns when species diffuse at different rates and reactions are sufficiently nonlinear. However, real systems are more complex and particularly involve physical interactions between constituents. While such interactions can promote patterns, we here show that they can also induce persistent spatiotemporal patterns. These patterns exhibit well-defined length and timescales, which result from cycles of droplet coarsening and fission. The dynamical patterns combine properties of traditional Turing patterns and chemically active droplets, which emerge for strong physical interactions. Our analysis thus reveals three qualitatively different regimes that emerge when two components interact physically and undergo nonlinear reactions.

Topological defects are a conspicuous feature of active liquid crystals that have been associated with important morphogenetic transitions in organismal development. Robust development thus requires a tight control of the motion and placement of topological defects. In this article, we study a mechanism to control $+1$ topological defects in an active polar fluid confined to a disk. If activity is localized in an annulus within the disk, the defect moves on a circular trajectory around the center of the disk. Using an ansatz for the polar field, we determine the dependence of the angular speed and the circle radius on the boundary orientation of the polar field and the active annulus. Using a proportional-integral controller, we guide the defect along complex trajectories by changing the active annulus size and the boundary orientation.

Preparation of nonclassical light with special quantum properties is essential for quantum technologies. High-harmonic generation (HHG) is a process that not only enables the creation of attosecond pulses but also has the potential to generate light with intricate quantum properties. In a recent experiment [PRX Quantum 5, 040319 (2024)], nonclassical interharmonic correlations have been measured from an HHG source between low-order harmonics. In this work, we theoretically investigate entanglement between different harmonics within an effective, phenomenological quantum optical model. This model implements a significant degree of simplification regarding the processes within the target material, treating the material through susceptibilities, as it is usual in quantum optics. Such an approach yields a general description of HHG in the few-harmonic generation regime, permitting the implications that can be derived within it to hold broadly within the domain of validity. We find that entanglement is produced as a result of the often neglected backaction. We can qualitatively reproduce experimentally measured nonclassicalities, which suggests that intermodal entanglement can, to an extent, be considered a universal phenomenon associated with HHG, rather than a result of using specific material targets.

We investigate the generation of genuine tripartite entanglement in a triangular spin-qubit system due to spatially correlated noise. In particular, we demonstrate how the formation of a highly entangled dark state, a $W$ state, enables robust, long-lived tripartite entanglement. Surprisingly, we find that environmentally induced coherent coupling does not play a crucial role in sustaining this entanglement. This contrasts sharply with the two-qubit case, where the induced coupling significantly influences the entanglement dynamics. Furthermore, we explore two promising approaches to enhance the tripartite entanglement by steering the system toward the dark state: postselection and coherent driving. Our findings offer a robust method for generating high-fidelity tripartite entangled states with potential applications in quantum computation.

We address the problem of how to prepare single-spinon wave functions, relevant for one-dimensional $S=1/2$ spin models, in a quantum computer. We adopt the recently proposed ansatz [Kulka et al., Phys. Rev. Lett. 134, 236504 (2025)] for the single-spinon wave function, where a state with $S=1/2$ is built in a spin chain with $L+1$ sites, adding a site with $S_z=1/2$ to the configurations of the ground-state wave function for the spin chain with length $L$. We extend the original work to the case of the Haldane-Shastry model. We discuss how to prepare the single-spinon ansatz for both the Heisenberg and Haldane-Shastry models in quantum computers, using a linear combination of unitaries. We consider three different strategies to compute the single-spinon energy in a quantum computer, we analyze their cost in terms of the number of qubits, gates, and circuits, and we test them in silico.

The observation of pressure-induced superconductivity in two- and three-layer Ruddlesden-Popper nickelates has spurred intense interest in these materials as a platform for exploring unconventional superconductivity. While the ground state of these systems has been shown to exhibit magnetism, the direct determination of their magnetic structure remains elusive. This is a crucial aspect, as magnetism may play a role in the pairing mechanism of superconductivity in these materials. In this study, we resolve the magnetic structures in the bilayer (2222) polymorphs of ${\mathrm{La}}_3{\mathrm{Ni}}_2{\mathrm{O}}_7$ and ${\mathrm{La}}_2{\mathrm{PrNi}}_2{\mathrm{O}}_7$ compounds, using a combination of complementary techniques, namely, neutron powder diffraction and muon-spin rotation/relaxation ($\mu \mathrm{SR}$). Magnetic neutron scattering in both samples emerges below $\sim 150$ K and is observed at the ($q_x,\frac{1}{2},0$) position, with $q_x=0$ and $\frac{1}{2}$ for ${\mathrm{La}}_3{\mathrm{Ni}}_2{\mathrm{O}}_7$ and $q_x=0$ for ${\mathrm{La}}_2{\mathrm{PrNi}}_2{\mathrm{O}}_7$. Alternating low-magnetic-moment (0.05–$0.075{\mu }_{\text{B}}$) and high-magnetic-moment ($0.66{\mu }_{\text{B}}$) stripes form a single layer; the bilayers are formed through antiferromagnetic stacking of single layers along the out-of-plane direction. The magnetic scattering with two propagation vectors $q_x=0$ and $\frac{1}{2}$ in the undoped ${\mathrm{La}}_3{\mathrm{Ni}}_2{\mathrm{O}}_7$ is attributed to two magnetic stacking polymorphs within a single crystallographic phase. The magnetic structures are substantiated by the $\mu \mathrm{SR}$ spectra. These findings provide a detailed understanding of the magnetic ground state in bilayer nickelates, offering crucial insights into the possible precursor states that may influence the emergence of superconductivity in these materials.

Hyperspectral photoluminescence maps of moiré transition-metal dichalcogenide heterobilayers encode rich information about the underlying disorder, but extracting it is hampered by the ambiguity of assigning individual spectral peaks. We show that the spatial correlations among a set of simple, peak-decomposition-free spectral descriptors—such as the centroid energy, the dominant-peak energy, and a sharp-line fraction—provide a peak-decomposition-free route to infer features of that disorder. Different descriptors act as filters that select different components of a multiscale disorder landscape—a smooth, micron-correlated background and a dense set of localized traps—and therefore acquire different spatial correlation lengths. The central prediction is a correlation-length hierarchy, $\xi \left(E_{\mathrm{cent}}\right)\ge \xi \left(E_{\mathrm{dom}}\right)$, which we derive by splitting the dominant-peak energy into a smooth background part and a short-range trap-switching fluctuation. The same picture explains the measured interdescriptor correlations, including the near-perfect anticorrelation ${\rho }_{\mathrm{S}}(\mathrm{\Delta }{E}_{\mathrm{cd}},R_{\mathrm{HL}})\approx -0.978$, which we show to be a robust geometric trend for spectra dominated by a common emission-envelope asymmetry. Benchmarked against phenomenological simulations, Hamiltonian diagonalization, and the measured ${\mathrm{MoSe}}_2/{\mathrm{WSe}}_2$ descriptor correlations, the framework turns descriptor maps into a quantitative, peak-decomposition-free probe of slow disorder and local traps in moiré excitons and, more broadly, in disordered semiconductor emitters.

The shortest path problem is related to many dynamic processes on networks, ranging from routing in communication networks to signaling in molecular interaction networks. When the network is fully known, the shortest path problem can be solved precisely and in polynomial time. If, however, the network of interest is only partially observable, the shortest path problem is no longer straightforward. Inspired by the shortest path problem in partially observable networks, we investigate the geometric properties of shortest paths in Euclidean soft random geometric graphs (SRGGs). We find that shortest paths are aligned along geodesic curves connecting shortest path end points. The strength of the shortest path alignment, as quantified by the average distance to geodesic from shortest path nodes and the average path stretch, is higher for larger SRGGs with short-range connections. In addition, we find that the strength of the shortest path alignment is nonmonotonic with respect to the average degree of the SRGG. Based on these observations, we establish the conditions under which the alignment of shortest paths may be sufficiently strong to allow the identification of shortest path nodes based on their proximity to geodesic curves. We show that in partially observable networks with uncertain node positions, our geometric approach can outperform network-based shortest path algorithms. In practical settings, our findings may have applications to navigation, wireless routing, and flow characterization in infrastructure networks.