Higher symmetries in interacting many-body systems often give rise to new phases and unexpected dynamical behavior. Here, we theoretically investigate a variant of the Dicke model with higher-order discrete symmetry, resulting from complex-valued coupling coefficients between quantum emitters and a bosonic mode. We propose a driven-dissipative realization of this model focusing on optomechanical response of a driven atom tweezer array comprised of $n$ subensembles and placed within an optical cavity, with the phase of the driving field advancing stepwise between subensembles. Examining stationary points and their dynamical stability, we identify a phase diagram for $n\ge 3$ with three distinctive features: a ${\mathbb{Z}}_n$ (${\mathbb{Z}}_{2n}$) symmetry-breaking superradiant phase for even (odd) $n$, a normal unbroken-symmetry phase that is dynamically unstable due to nonreciprocal forces between emitters, and a first-order phase transition separating these phases. This $n$-phase Dicke model may be equivalently realized in a variety of optomechanical or optomagnonic settings, where it can serve as a test bed for studying high-order symmetry breaking and nonreciprocal interactions in open systems.

The Quantum Approximate Optimization Algorithm (QAOA) is a leading candidate for near-term quantum advantage, yet its practical impact is hindered by limited performance on symmetric local Hamiltonians and the costly optimization of variational parameters. The recursive-QAOA (RQAOA) introduced by Bravyi et al. [Phys. Rev. Lett. 125, 260505 (2020)] addresses the first limitation while also reducing circuit size, and parameter transfer techniques can be used to bypass the optimization loop effectively. In this work, we combine these two ideas to develop an optimization-free RQAOA and evaluate its performance on the binary paint shop problem (BPSP)—an optimization problem found in manufacturing where a sequence of cars must be painted under constraints while minimizing the number of color changes. The BPSP can be formulated as an Ising ground state problem with a symmetric local Hamiltonian in the form of MAX-CUT and properties well suited for the application of QAOA parameter transfer. We benchmark QAOA and RQAOA with parameter transfer against classical solvers and heuristics, and investigate their resilience to suboptimal parameters. For circuit optimization, we use reverse-causal cones and introduce a method of trimming outer two-qubit gates. To estimate the classical resources needed to simulate these quantum algorithms, we compute entanglement entropy and bond dimensions using matrix product state methods. We also compare circuit sizes and measurement counts across implementations. Our results show that RQAOA is inherently robust to parameter deviations, maintaining near-optimal solutions without noticeable degradation under parameter transfer while substantially reducing quantum resource requirements compared to QAOA. This highlights a viable route toward scalable quantum optimization without the overhead of the classical optimization loop and its challenges with barren plateaus.

Physical transport processes organize through local interactions that redistribute imbalance while preserving conservation. Classical solvers enforce this organization by applying fixed discrete operators on rigid grids. We introduce the Hebbian Physics Network (HPN), a computational framework that replaces this rigid scaffolding with a plastic transport geometry. An HPN is a coupled dynamical system of physical states on nodes and constitutive weights on edges in a graph. Residuals, local violations of continuity, momentum balance, or energy conservation, act as thermodynamic forces that drive the joint evolution of both the state and the operator (i.e., the adaptive weights). The weights adapt through a residual-modulated anti-Hebbian rule, which we prove constitutes a strictly local gradient descent on the residual energy. This mechanism ensures thermodynamic consistency: Near equilibrium, the realized transport operator converges to a symmetric, positive-definite form, reproducing Onsager's reciprocal relations without explicit enforcement. Far from equilibrium, the system self-organizes into transport topologies within a thermodynamically admissible class that redistribute imbalance through adaptive local geometry. Unlike optimization-based approaches that impose physics through global loss functions, HPNs embed conservation intrinsically: Transport is restored locally by the evolving operator itself, without a global Poisson solve or backpropagated objective. We demonstrate the framework on scalar diffusion and incompressible lid-driven cavity flow, showing that physically consistent transport geometries and flow structures emerge from random initial conditions solely through residual-driven local adaptation. HPNs thus reframe computation not as the solution of a fixed equation, but as a thermodynamic relaxation process in which the constitutive geometry and physical state coevolve.

Systems with energy injection and dissipation are characterized by self-organization through spontaneous breaking of translational symmetry, giving rise to the formation of patterns. Depending on the energy injection, patterns can be regular in space and stationary, or exhibit irregular spatial distributions and complex temporal dynamics. The emergence of complexity in two-dimensional patterns is not yet fully understood nor characterized. Based on a liquid crystal light valve with optical feedback, we characterize experimentally the formation of a pattern in the presence of noise and the route to the spatiotemporal complexity. A universal model of pattern formation, valid close to nascent bistability and spatial bifurcation, is also numerically simulated and studied. In the experimental and in the simulated data, the spatiotemporal complexity is characterized via several complexity measures that provide complementary information. We find that, when varying the control parameter, from the homogeneous state, a stationary pattern emerges, which then becomes chaotic, with characteristic temporal dynamics and spatial irregularities. These transitions were correctly identified by a clustering algorithm. Our results pave the way for future studies of other pattern-forming complex systems.

Lunar dust—the submillimeter fraction of the regolith—controls the optical, thermophysical, electrical, mechanical, and environmental behavior of the Moon's surface. These properties set the performance envelopes of remote-sensing retrievals, regolith geotechnics, volatile cycles, and exploration systems, while also posing operational and biomedical risks. We synthesize Apollo sample analyses and in situ observations (Surveyor, Lunokhod, Apollo) with contemporary datasets from the Lunar Reconnaissance Orbiter (LRO) Diviner Lunar Radiometer, the Lunar Atmosphere and Dust Environment Explorer (LADEE)/Lunar Dust Experiment on LADEE exospheric dust measurements, and Chang’e-4 Lunar Penetrating Radar. We also incorporate 2024–2025 results: Chandrayaan-3 Chandra's Surface Thermophysical Experiment thermophysics at the Vikram lander's site, Stereo Cameras for Lunar Plume-Surface Studies (SCALPSS) plume-surface diagnostics from Intuitive Machines Mission 1, and Negative Ions at the Lunar Surface detections of a dayside near-surface ${\mathrm{H}}^{-}$ population on Chang’e-6. The review links (1) production and modification processes to (2) grain-scale physical/chemical/electrical/optical/mechanical properties, then to (3) mobilization pathways (meteoroid ejecta, electrostatic hopping, and rocket-plume entrainment), and finally to (4) region-specific design ranges across maria, highlands, pyroclastic units, magnetic swirls, and permanently shadowed regions (PSRs). We quantify temperature-illumination dependence across day/night and PSR-equator regimes through a two-channel $k(T,\rho )$ model and charge-relaxation scaling. We provide closed-form expressions for adhesion-aware lift thresholds and for near-surface (0–3 m) dust transport at apex/hover heights as functions of sheath structure. The result is a design-ready set of relations, figures, and tables that propagate microphysics and composition into engineering parameters for upcoming landed and rover operations.

Political polarization, fueled by public discourse and echo chambers, has profound implications for the functioning of democratic elections. However, traditional one-dimensional opinion models—assuming “support for one party equals opposition to another”—fail to capture the nuanced dynamics of swing voters (including neutrals, left leaners, and right leaners), who are critical for the final election outcomes. This study introduces a two-dimensional opinion model that classifies voters into five groups, enabling precise characterization of the swing group's interactive behaviors. Importantly, we introduce antagonism effect to describe the intensity with which the two camps incite opposition and exert voting pressure in the run-up to the election, typically via Us-versus-Them framing. By integrating the open-mindedness of voters, the stubbornness of opinion interactions, and the antagonism effect arising from strategic campaigns, we systematically explore the intricate interplay between top-down political campaigns and bottom-up interpersonal opinion dynamics, unveiling their nonlinear coupling impacts on the emergence and evolution of swing voters. Counterintuitively, we find that extreme antagonism effects might backfire in presidential election: When both parties adopt intense antagonistic strategies, the party that polarizes more strongly risks alienating swing voters, thereby enabling its ostensibly weaker opponent to prevail. These insights are also illustrated on the core retweet networks during the 2020 U.S. presidential election. Building upon multidimensional opinion model, our results highlight the possibility of mobilizing swing voters and shaping electoral outcomes through antagonistic strategies of political parties. Our work also provides a nuanced and generalizable framework for analyzing opinion dynamics in other polarized public discourse.

Understanding the energy cost of quantum measurement process and its connection to the measurement performance faces the challenge of modeling the objectification process. The latter turns the measurement result into an objective fact, available to independent observers, and is responsible for the measurement irreversibility. To address this issue, we propose and analyze a dynamical model of quantum measurement, able to capture nonideal (weak and inefficient) measurements. In this model, the objectification is induced by a contact with a macroscopic reservoir at equilibrium that is responsible for the redundant broadcast of the measurement outcome (producing a spectrum broadcast structure state) while inducing decoherence in the pointer basis, in the line of the theory of quantum Darwinism. We analyze the performance of the obtained measurement process by introducing figures of merit to quantify the strength of the measurement and its efficiency. We also derive a lower bound on the measurement work cost that we can relate to the measurement quality. We take as an illustration the readout of a qubit via its coupling to a harmonic oscillator. We investigate the long sequences of extremely short and weak measurements (also known as continuous measurements), to find under which conditions they converge to an ideal (projective) measurement and analyze their work cost. Surprisingly, we find that a sequence converging to projective measurement has a much larger work cost than an equivalent strong measurement obtained from a single intense interaction with the apparatus. We extend this result to a large class of models owing to scaling arguments. Our analysis offers insights into the trade-offs between measurement strength, energy consumption, and information extraction in quantum measurement protocols.

Quantum reservoir computing has emerged as a promising paradigm for harnessing quantum systems to process temporal data efficiently by bypassing the costly training of gradient-based learning methods. Here, we demonstrate the capability of this approach to predict and characterize chaotic dynamics in discrete nonlinear maps, exemplified through the logistic and Hénon maps. While achieving excellent predictive accuracy, we also demonstrate the optimization of training hyperparameters of the quantum reservoir based on the properties of the underlying map, thus paving the way for efficient forecasting with other discrete and continuous time-series data. Using closed-loop prediction of distant future steps, our protocol discriminates between chaotic and nonchaotic phases without prior knowledge of the underlying map or the nature of the time series. Furthermore, the framework exhibits robustness against decoherence when trained in situ and shows insensitivity to reservoir Hamiltonian variations as well as robustness to finite-sampling error. These results highlight quantum reservoir computing as a scalable and noise-resilient tool for modeling complex dynamical systems, with immediate applicability in near-term quantum hardware.

We introduce a minimal evolutionary model to show how local cooperation and global competition can create a transition to the diversity of communities such as linguistic groups. By using a lattice model with high-dimensional state agents and evolution under a fitness that depends on an agent’s local neighborhood and global dissimilarity, clusters of diverse communities with different fitness are organized by equalizing the finesses on the boundaries, where their numbers and sizes are robust to parameters. We observe successive transitions over quasistationary states, as triggered by the emergence of new communities on the boundaries. Our abstract framework provides a simple mechanism for the diversification of culture.

The large-scale circulation (LSC) of turbulent convection is a prominent feature of its dynamics and forms the basis for descriptive theories. We show, using experimental and numerical results from thermal convection in a cylindrical cell, that the LSC possesses a persistent internal “clock”: its pulsating velocity as a function of time is described by a constant value of the parameter $U/\left(lf\right)$, where $U$ is the mean velocity, $f$ is the pulsation frequency, and $l$ is the characteristic length scale. By introducing a narrow sidewall barrier, we can trip the LSC, forming a pair of interconnected rolls stacked above and below the barrier. They independently exhibit the same value for the ratio $U/\left(lf\right)$, even for vertically asymmetric pairs, indicating signs of synchrony. Thus, this parameter establishes a direct connection between plume-shedding dynamics and the flow topology.

The potential quantum speedup in solving optimization problems via adiabatic quantum annealing is often hindered by the closing of the energy gap during the anneal, especially when this gap scales exponentially with system size. In this work, we alleviate this bottleneck by demonstrating that for the NP-complete Maximum Weighted Independent Set (MWIS) problem, an informed choice of $n$-local catalysts (operators involving $n$ qubits) can reopen the gap during the annealing process. By analyzing first-order phase transitions in toy instances of the MWIS problem, we first identify direct-tunneling catalysts that effectively eliminate the transition and provide an analytical discussion on when the sign of the catalyst influences its impact. We then reveal that $n$-local catalysts exponentially improve gap scaling and in certain scenarios are as effective as direct-tunnel coupling between two minima. Utilizing this understanding, we show that they also increase the efficiency of ground-state preparation via adiabatic quantum annealing in random graphs and analytically demonstrate the necessity of their placement across unfrustrated loops in the graph for effective performance in MWIS problems. Additionally, using a circuit implementation of the $n$-local catalyst (requiring $2n$ nearest-neighbor gates), we demonstrate that both the circuit depth and the total number of gates required to solve the problem are reduced by several orders of magnitude when compared to the discrete-time version of traditional quantum annealing using local drivers. Our analysis suggests that nonlocal quantum fluctuations entangling multiple qubits as a catalyst are key to achieving the desired quantum advantage.

We report a real-time first-principles study of ultrafast spin injection in a ${\mathrm{WSe}}_2$-graphene heterobilayer under circularly polarized laser irradiation, using time-dependent density functional theory. Contrary to conventional expectations, spin transfer into graphene is not a passive process but is actively driven by spin-selective carrier filtering at the interface. Spin-polarized carriers generated in the ${\mathrm{WSe}}_2$ layer induce a preferential migration of opposite-spin carriers from graphene, which results in net spin magnetization in graphene. This process is governed by interlayer band offsets, density-of-state asymmetry, and Pauli blocking. These findings indicate a microscopic mechanism of spin injection in nonmagnetic systems and identify a guiding principle for the design of ultrafast optospintronic functionalities in van der Waals heterostructures.

Exact numerical results for the dc magnetoconductivity tensor of the two-dimensional spatially disordered Yukawa-Sachdev-Ye-Kitaev (2D-YSYK) model on a square lattice, at first order in applied perpendicular magnetic field, are obtained from the self-consistent disorder-averaged solution of the 2D-YSYK saddle-point equations. This system describes fermions endowed with a Fermi surface and coupled to a bosonic scalar field through spatially random Yukawa interactions. The resulting local and energy-dependent fermionic self-energies are employed in the Kubo formalism to calculate the longitudinal and Hall conductivities, the Hall coefficient, the carrier mobility, and the cotangent of the Hall angle, at fixed fermion density. From the interplay between YSYK interactions and square-lattice embedding, and the non-Boltzmann frequency-dependent self-energies, we find nontrivial evolution of the magnetotransport coefficients as a function of temperature and YSYK interaction strength, notably a superlinear evolution of the Hall-angle cotangent and the inverse carrier mobility with temperature, concomitant with linear-in-temperature resistivity, in an extended crossover regime above the low-temperature marginal Fermi liquid ground state. Our model and results provide a controlled theoretical framework to interpret magnetotransport experiments, at linear order in applied magnetic field, in strange-metal phases found in strongly correlated solid-state electron systems.

We systematically investigate the effects of spin order and orbital polarization on the electronic and magnetic structures of nematic FeSe using the $\mathrm{LDA}+U$ method. While our calculations with on-site Coulomb repulsion $U\sim 4.5\mathrm{eV}$ can account for both the nonmagnetic state at ambient pressure and the phase transition to a stripe antiferromagnetic state at moderate pressure as observed experimentally, neither static spin order nor static orbital polarization can describe the experimental band dispersions from angle-resolved photoemission spectroscopy (ARPES) studies, due to the underestimate of quantum fluctuations. By employing a particle swarm optimization algorithm, we construct a tight-binding model with inclusion of correlation effects through seeking appropriate renormalization factors of kinetic energies, which successfully reproduce the ARPES results at various temperatures, highlighting the importance of correlations in FeSe.

Kapitza-Dirac scattering, the diffraction of matter waves from a standing light field, is widely utilized in ultracold gases, but its behavior in the strongly interacting regime is an open question. Here, we develop a numerically exact two-body description of Kapitza-Dirac scattering for two contact-interacting atoms in a one-dimensional harmonic trap subjected to a pulsed optical lattice, enabling us to obtain the numerically exact dynamics. We map how interaction strength, lattice depth, lattice wave number, and pulse duration reshape the diffraction pattern, leading to an interaction-dependent population redistribution in real and momentum space. By comparing the exact dynamics to an impulsive sudden-approximation description, we delineate the parameter regimes where it remains accurate and those, notably at strong attraction and small lattice wave number, where it fails. Our results provide a controlled few-body benchmark for interacting Kapitza-Dirac scattering and quantitative guidance for Kapitza-Dirac-based probes of ultracold atomic systems.

Populations of cells regulate gene expression in response to external signals, but their ability to make reliable collective decisions is limited by both intrinsic noise in molecular signaling and variability between individual cells. In this work, we use optogenetic control of the canonical Wnt pathway as an example to study how reliably information about an external signal is transmitted to a population of cells, and determine an optimal encoding strategy to maximize information transmission from Wnt signals to gene expression. We find that it is possible to reach an information capacity beyond 1 bit only through an appropriate, discrete encoding of signals: using no Wnt, a short Wnt pulse, or a sustained Wnt signal. By averaging over an increasing number of outputs, we systematically vary the effective noise in the pathway. As the effective noise decreases, the optimal encoding comprises more discrete input signals. These signals do not need to be fine-tuned to achieve near-optimal information transmission. The optimal code transitions into a continuous code in the small-noise limit, which can be shown to be consistent with the Jeffreys prior. We visualize the performance of different signal encodings using decoding maps. Our results suggest that optogenetic Wnt signaling allows for regulatory control beyond a simple binary switch and provide a framework to apply ideas from information processing to single-cell in vitro experiments.