Machine-learning (ML) models trained on Ising spin configurations have demonstrated surprising effectiveness in classifying phases of Potts models, even when processing severely reduced representations that retain only two spin states. To unravel this remarkable capability, we identify a family of alternative order parameters for the $q=3$ and $q=4$ Potts models on a square lattice, constructed from the occupancies of secondary and minimal spin states rather than the conventional dominant-state order parameter. Through systematic finite-size scaling analyses, we demonstrate that these quantities, along with a magnetizationlike quantity derived from a reduced spin representation, accurately capture critical behavior, yielding critical temperatures and exponents consistent with established theoretical predictions and numerical benchmarks. Furthermore, we rigorously establish the fundamental relationships between these alternative (quasi)order parameters, demonstrating how they collectively encode criticality through different aspects of spin configurations. Our results clarify, within this specific setting, how reduced spin representations can retain the essential thermodynamic information needed for identifying critical behavior. Taken together, this work establishes a concrete bridge between Ising-trained ML models and critical phenomena in Potts systems by showing that Potts criticality can be encoded in more compact, nontraditional forms, thereby opening avenues for discovering analogous order parameters in broader spin systems.

Nonpairwise multibody interactions have recently attracted much attention due to their promising applications in the realm of quantum information science and technology. In this work, we propose a method to synthesize a nonpairwise three-body interaction of superconducting qubits using a coupler qubit dispersively coupled to two spatially separated qubits and a microwave pulse. This interaction describes physical processes of an excitation from the coupler qubit to two spatially separated qubits and the reverse process. We show that the interaction can be utilized to deterministically generate three-body Greenberger-Horne-Zeilinger (GHZ) entangled states, transfer the state of the coupler qubit into a two-qubit entangled state, and construct three-qubit controlled-controlled-phase and Toffoli gates. Our proposal only requires a coupler qubit and a single-step operation; thus, the system architecture and method are quite simple. As examples, we numerically simulate the dynamics of the system, and the numerical results demonstrate that high-fidelity preparation of the proposed three-body GHZ entangled state and quantum gates is feasible with current superconducting circuit technology. This approach is general and applicable to a wide range of quantum systems.

The development of large-scale, programmable photonic circuits capable of performing generic matrix-vector multiplication is essential for both classical and quantum information processing. However, this goal is hindered by high losses, hardware errors, and difficulties in programmability. We propose an enhanced architecture for programmable photonic circuits that minimizes circuit depth and offers analytical programmability, properties that have not been simultaneously achieved in previous circuit designs. Our proposal exploits a previously overlooked representation of general nonunitary matrices as sums of two unitaries. Furthermore, similar to the traditional singular value decomposition-based circuits, the circuits in our unitary-sum-based architecture inherit the advantages of the constituent unitary circuits. Overall, our proposal provides a significantly improved solution for matrix-vector multiplication compared to the established approaches.

The cytoskeleton is crucial in maintaining cell shape and structural integrity. It consists of three types of filaments—actin filaments, microtubules, and intermediate filaments (IFs). Among them, actin filaments and IFs exhibit distinctly different force-strain behavior: while IFs show nonlinear behavior with exceptional extensibility and remarkable resistance against rupture at high strains, actin filaments break at low strains. Here, we address the question of whether the intriguing mechanical behavior of vimentin IFs translates to the network scale. We apply high strains to in vitro reconstituted networks using optical tweezers and find contrastive behavior for the two cytoskeletal networks: vimentin networks show strain stiffening behavior and respond in an elastic, solidlike manner with high forces opposing the active movement of the beads, whereas actin networks strain soften and fluidize at low forces. Our work highlights the complementary nature of the components of the cytoskeleton, which—in the cell—are partly colocalized and believed to constitute a composite biological material.

Aquatic animals offer compelling evidence that flow sensing alone, without vision, is sufficient to guide a swimming organism to the source of an unsteady hydrodynamic trail. However, the sensory feedback strategies that allow these remarkable trail-tracking abilities remain opaque. Here, by integrating mechanistic flow simulations with reinforcement learning techniques, we discovered two simple and equally effective strategies for hydrodynamic trail following. Though not a priori obvious, these strategies possess parsimonious interpretations, analogous to Braitenberg’s simplest vehicles, where the agent senses local flow signals and turns away from or toward the direction of stronger signals. A rigorous stability analysis shows that the effectiveness of these strategies in robustly tracking flow currents is independent of the type of sensor but depends on sensor placement and the traveling nature of the flow signal. Importantly, these results inform a suite of versatile strategies for hydrodynamic trail following applicable to both vortical and turbulent flows. These insights support the future design and implementation of adaptive real-time sensory feedback strategies for autonomous robots in dynamic flow environments.

Quantum communication and computation heavily rely on entanglement distillation protocols. There is a plethora of distillation protocols for Pauli channels and also for some non-Pauli channels. However, an effort to relate the effectiveness of these protocols for different types of noise has been missing. For most quantum channels, the gap between the existing lower and upper bounds on distillation rates is substantial, and improvements of achievable rates have been stagnant for decades. In this work, we improve the best known distillation lower bounds, for both the amplitude damping and depolarizing channels. We build on a key observation that distillation protocols reshape several uses of a very noisy channel into a better effective channel. We apply this channel processing in an adaptive and recurrent manner. For the amplitude damping channel, our suggested protocol reshapes the channel into an erasure channel, achieving rates exceeding the best known lower bound given by the channel’s reverse coherent information. For the depolarizing channel, we introduce the Greedy recurrence protocol with proven performance guarantees and construct a combined protocol improving upon previously known distillation rates. Improved bounds on attainable distillation rates give insights for both practical implementations and theoretical understanding of quantum information processing.

In indirect-drive inertial confinement fusion, the radiative ablation process is vitally important for both building up of a uniform x-ray radiation in the high-$Z$ hohlraum and subsequently capsule implosion ablated by the x-ray radiation. For the former circumstance, various supersonic and subsonic ablation models are developed under the local thermodynamic equilibrium (LTE) assumption. However, for the latter, since the capsule plasma is of low $Z$, the electron and radiation temperatures are hard to achieve equality; i.e., LTE assumption breaks. So far, how to precisely describe and simulate such a non-LTE ablation process is unknown. Here, by self-consistently including the energy exchange between radiations and electrons into the radiation hydrodynamic (RHD) equations and considering the heat wave velocity as subsonic, we derive and set up a non-LTE subsonic radiative ablation model. Different from previous models where only the Rosseland opacity ${\sigma }_R$ functions, in our non-LTE model, besides ${\sigma }_R$, the Planck opacity ${\sigma }_P$ plays an important role as well. Our model shows that most characteristic physical quantities of the non-LTE ablation, such as the ablation velocity, ablation pressure, and shock velocity, depend on a combination of ${\sigma }_P/{\sigma }_R$, while the damping scale length of the radiation temperature scales as $L\propto {\left({\sigma }_P{\sigma }_R\right)}^{1/2}$. Therefore, we propose using a simple single multiplier $\zeta$ on both opacities as $\widetilde{{\sigma }_P}=\zeta {\sigma }_P$ and $\widetilde{{\sigma }_R}=\zeta {\sigma }_R$ to match the RHD simulation with experimental results, where $L$, or more practically, the preheating radiation temperature $T_{\mathrm{pre}}$, can be chosen as a proper benchmark.

By simulating the real-time multi-electron wavefunction with the multiconfigurational time-dependent Hartree-Fock (MCTDHF) approach, we conduct an ab initio study of the single-photon ionization process of a body-fixed water molecule (${\mathrm{H}}_2\mathrm{O}$) driven by attosecond pulses. To this end, we present a full-dimensional implementation of the MCTDHF method based on one-center expansions, allowing for the simulation of arbitrarily polarized lasers and multicenter polyatomic potentials. With a rigorous definition of the time-dependent hole state (TDHS) using the time-domain generalization of extended Koopmans' theorem, we derive the reduced ion density matrix within the MCTDHF framework, which inherently encodes the total and channel-resolved photoionization cross sections of ${\mathrm{H}}_2\mathrm{O}$. The cross sections obtained are benchmarked against existing experimental and theoretical results, validating the TDHS formalism. Furthermore, by adjusting the phase delay and intensity ratio of a pair of orthogonally polarized attosecond pulses, we explore the ultrafast control of attosecond coherence between electronic states of ${\mathrm{H}}_2{\mathrm{O}}^{+}$.

We analyze the universality classes of phase transitions in a variety of nonlinear voter models. By mapping several models with symmetric absorbing states onto a canonical model introduced in previous studies, we confirm that they exhibit a Generalized Voter transition. We then propose a canonical mean-field model that extends the original formulation by incorporating a noise term that eliminates the absorbing states. This generalization gives rise to a phase diagram featuring two distinct types of phase transitions: a continuous Ising transition and a discontinuous transition we call Modified Generalized Voter. These two transition lines converge at a tricritical point. We map diverse noisy nonlinear voter models onto this extended canonical form. Using finite-size scaling techniques above and below the upper-critical dimension, we show that the continuous transition of these models belongs to the Ising universality class in their respective dimensionality. We also find universal behavior at the tricritical point. Our results provide a unifying framework for classifying phase transitions in stochastic models of opinion dynamics with both nonlinearity and noise.

Recent advancements in machine learning have led to the development of sophisticated algorithms for automatic differentiation. These software tools simplify the implementation of the adjoint method by automating the derivation of adjoint equations, enabling researchers to apply the adjoint method to a wide range of problems without requiring extensive expertise in the underlying mathematics. One particularly significant application is the state estimation of partially observable systems. We show that for this application the performance of the adjoint method can be significantly improved by a shooting protocol in which the end of the previous segment is used as the initial condition for estimating the next segment, independent of the success of the estimate for the previous segment. The superior performance of this adjoint optimization-based consecutive shooting method is demonstrated using Lorenz-96 models with dimensions from 9 to 300.

We theoretically propose a photonic orbital angular momentum quantum memory, currently of great interest from the perspective of quantum networks, based on a ring-trapped Bose-Einstein condensate interacting with Laguerre-Gaussian beams. The optical states are stored in the large Hilbert space of topologically protected persistent currents of the condensate. In contrast to earlier work, our proposal uses a cavity, which enhances the light-matter interaction by several orders of magnitude, resulting in high fidelity, convenient detection, and no need to repeat read-write cycles degraded by atom loss. Our scheme avoids using multiple internal atomic states as in generic electromagnetically induced transparency-based protocols and thus bypasses problems like off-resonant photoassociation by the control fields and dephasing effects that limit memory storage time. For optimized parameters, our work yields a storage time 3 orders of magnitude better than presently available and opens the way for exploiting existing advantages of cavity-based noise suppression, wavelength transduction, large bandwidth, and nondestructive readout of the memory.

Cell-secreted extracellular vesicles (EVs) play a pivotal role in local and distant cell-to-cell communication by delivering specific cargoes to other cells or to the extracellular space. In many cells, the glycocalyx, a thick sugar-rich layer at the cell surface, and the membrane-cortex attachment are crucially linked to the formation of EVs. However, it remains unclear as to what determines the successful formation of EVs when multiple physical factors are involved. In this work, we developed a model for glycocalyx-membrane-cortex composite to investigate the effects of glycocalyx and membrane-cortex adhesion on the formation of EVs by combining polymer physics-based theory and Helfrich membrane theory. By performing linear stability analysis, we show that modulating the mechanical feedback among the glycocalyx, membrane-cortex attachment, and membrane curvature can give rise to two types of instabilities: a conserved Turing-type instability and a Cahn-Hilliard-type instability. Furthermore, using an equilibrium model, we identified two critical conditions for EV formation: an initial detachment of the membrane from the underlying cortex and then a sufficient driving force to induce membrane deformation for successful EV formation. We further demonstrated that there exists an optimal glycocalyx coating area at which the formation of EVs is most favorable. Finally, we use our model to predict that a heterogeneous size distribution of EVs can be generated through the regulation of glycocalyx properties, shedding insight into how EVs of different radii may be generated.

We present a scheme to generate the macroscopic Gaussian cluster states of multiple ferrimagnetic spheres in a microwave cavity through Kerr interactions. When each such sphere with self-Kerr nonlinearity is coupled to a single-cavity mode, it allows the generation of multimode entanglement and graph states—an important resource for one-way quantum computing through measurement-based protocols—near the strong and in the weak coupling regimes. While optimizing such states over the parameters governing the dynamics, the optimal appears for a far-detuned as well as dissipative cavity. Cluster states for a linear and a ring topology of a few spheres are discussed in the steady state of the system, further applicable to multiple spheres $(n=10)$, for experimentally feasible parameters.

We present an efficient time-dependent Maxwell-Bloch transport model for simulating inner-shell x-ray lasing (XRL) in x-ray free-electron laser-driven highly ionized and excited nonlocal thermodynamic equilibrium plasmas. The model selectively retains all coherences between lasing levels together with nonradiative coherences linking adjacent lasing transitions. This model fully accounts for all processes governing population evolution, enabling accurate and efficient simulation of complex atomic systems. Applications to neon and argon gases reveal distinct amplification behaviors across charge states. In neon, multiple lasing lines spanning low to high ionization stages are identified, where low-charge-state transitions are strongly shaped by autoionization and saturation, while high-charge-state transitions exhibit pronounced coherence signatures such as Rabi oscillations and Rabi splitting. In argon, amplification is dominated by a few strong channels, while weaker ones are suppressed by nonradiative coherence effects in $\mathrm{\Lambda }$-type subsystems. Overall, our results demonstrate that laser field-induced multilevel coherent coupling plays a crucial role in shaping XRL gain and spectra. This framework provides a versatile and predictive tool for designing XRLs with tailored temporal and spectral properties, with potential applications in ultrafast science, nonlinear x-ray optics, and precision spectroscopy.