We report on the shift of energies released during Auger cascades in rare-gas ions, when the ions are not isolated but embedded in a homogeneous cluster. By combining high-resolution electron spectroscopy with multielectron coincidence spectroscopy of small Ar and Xe clusters, we investigate the dependence of Auger electron emission energies on the charge state of the emitting ion in the polarizable cluster medium up to charge states of $+4$. Our results show deviations from the predictions of the established electrostatic Born model by a factor of $\approx 2$ for the final step of the cascades.

Erasure of the binary memory, 0 or 1, is an essential step for digital computation as it involves irreversible logic operations. In the classical case, the erasure of a bit of memory is accompanied by the evolution of a minimum amount of heat set by the Landauer bound $k_{B}Tln2$, which can be achieved in the asymptotic limit. However, the erasure of memory needs to be completed within a finite time for practical and effective computational processes. It is observed that the higher the speed of erasure, the greater the amount of heat released, which leads to unfavorable environmental conditions. Therefore, this is a fundamental challenge to reduce the evolved heat related to finite-time memory erasure. In the present work, we address this crucial aspect in the field of information thermodynamics. We proceed by considering the physical model framework where the two memory states correspond to the two wells of a bistable potential, as in the conventional cases. However, the potential is asymmetric in terms of the width of the two wells. Moreover, the two memory states are separated by a barrier that is asymmetric in structure. This type of asymmetry models the two binary memory states that occupy different phase-space volumes, although they are energetically equivalent, in a general setup. We examine in detail the effect of the degree of asymmetry on the success rate of the erasure process and the work done or heat released associated with it. We find that the asymmetry in the width of the potential wells and the barrier partitioning them, i.e., the two memory states, plays a very significant role in improving the efficiency of the erasure process, in view of the success rate and the thermodynamic costs. Our thorough simulation study establishes the fact that one can reach below the Landauer bound in an appropriate asymmetric setup. Importantly, it develops a quantitative understanding of the deviation from the Landauer limit as a function of the degree of asymmetry of the potential governing the erasure mechanism. Moreover, through our current work, we identify the effective free energy change for the finite-time bit erasure process as a general lower bound for the work done or evolved heat even when the departure from the Landauer limit is observed. We retrieve the approach toward the Landauer limit in terms of the energetics involved with the erasure mechanism under the symmetric setup.

We address the problem of implementing bottleneck layers from classical pretrained neural networks on a quantum computer, with the goal of exploring intrinsically quantum ansatz for representing large linear layers within hybrid classical-quantum models. Our approach begins with a compression step in which the target linear layer is represented as an effective matrix product operator (MPO) without degrading model performance. The MPO is then further disentangled into a more compact form. This enables a hybrid classical-quantum execution scheme, where the disentangling circuits are deployed on a quantum computer while the remainder of the network—including the disentangled MPO—runs on classical hardware. We introduce two complementary algorithms for MPO disentangling: (1) an explicitly disentangling variational method leveraging standard tensor network optimization techniques and (2) an implicitly disentangling gradient-descent-based approach. We validate these methods through a proof-of-concept translation of simple classical neural networks for MNIST and CIFAR-10 image classification into a hybrid classical-quantum form.

The functional renormalization group (fRG) is acknowledged as a powerful tool in quantum many-body physics and beyond. On the technical side, conventional implementations of the fRG rely on regulators for bare propagators only. Starting from Schwinger-Dyson and Bethe-Salpeter equations, we develop here an fRG formulation where both bare propagators and bare interactions can be dressed with regulators. The approach thus obtained is an extension of the multiloop fRG recently introduced for many-fermion systems. Using the single-boson exchange decomposition, we show that the underlying flow equations are simply interpreted as adding a regulator to the bosonic propagator and that such an extension scarcely changes the original structure of the flow equations. Overall, we provide a framework for implementing approaches that cannot be realized with conventional fRG methods, such as temperature flows for models with retarded interactions. For concrete applications, we analyze the loop convergence of our formulation against a conventional cutoff scheme for the Anderson impurity model. Finally, we devise a temperature-flow scheme that implements a cutoff in both the propagator and the bare interaction, and demonstrate its validity on a model of an Anderson impurity coupled to a phonon.

Recent advances in quantum computing have demonstrated its potential to significantly enhance the analysis and forecasting of complex classical data. Among these, quantum reservoir computing (QRC) has emerged as a particularly powerful approach, combining quantum computation with machine learning for modeling nonlinear temporal dependencies in high-dimensional time series. As with many data-driven disciplines, quantitative finance and econometrics can hugely benefit from emerging quantum technologies. In this work, we investigate the application of QRC for realized volatility forecasting. Our model employs a fully connected transverse-field Ising Hamiltonian as the reservoir with distinct input and memory qubits to capture temporal dependencies. The QRC approach is benchmarked against several econometric models and standard machine learning algorithms. The models are evaluated using multiple error metrics and the model confidence set procedures. To enhance interpretability and mitigate current quantum hardware limitations, we utilize wrapper-based forward selection for feature selection, identifying optimal subsets and quantifying feature importance via Shapley values. Our results indicate that the proposed quantum reservoir approach consistently outperforms benchmark models across various metrics, highlighting its potential for financial forecasting despite existing quantum hardware constraints. This work serves as a proof of concept for the applicability of quantum computing in econometrics and financial analysis, paving the way for further research into quantum-enhanced predictive modeling as quantum hardware capabilities continue to advance.

Although many animals rely on visual information to navigate, optic flow is inherently ambiguous as it confounds information about motion speed and object distance. As a result, the visual feedback produced by a given motor command is context dependent and requires an appropriately adapted response. Recent experiments have investigated how the fish Danionella cerebrum use visual cues to stabilize their position against simulated external currents. Logarithmic sensorimotor transformations have been proposed to enable adaptive responses to perturbations while preventing delay-induced instabilities. Here, we develop the theoretical framework introduced for continuous locomotion to show how logarithmic coding naturally gives rise to this adaptive behavior. The system is modeled by a nonlinear delay differential equation, which is analyzed using dynamical systems theory. We further analyze experimental data to uncover the mechanisms underlying swimming initiation and positional drift correction. Finally, we extend our framework to intermittent locomotion, resulting in a nonlinear difference equation, and show that it still produces robust adaptive behavior. This is motivated by the literature on zebrafish, where visuomotor stabilization has been extensively studied, but burst-and-coast swimming obscures the underlying adaptation mechanism. We show that our theory can reproduce the experimental results reported for motor adaptation in zebrafish without invoking internal models. Overall, our results highlight logarithmic coding as a unifying principle for visuomotor stability across continuous and intermittent locomotor regimes.

Quantum harmonic oscillators serve as fundamental building blocks for quantum information processing, particularly in the context of the bosonic circuit quantum electrodynamics (cQED) platform. Conventional methods for extracting oscillator properties rely on predefined analytical gate sequences to access a restricted set of observables or resource-intensive tomography processes. Here, we introduce the Optimized Routine for Estimation of any Observable (OREO), a numerically optimized protocol that maps the expectation value of arbitrary oscillator observables onto that of an ancillary qubit. We demonstrate OREO in a bosonic cQED system as a means to efficiently measure phase-space quadratures and their higher moments, directly obtain faithful non-Gaussianity ranks, and effectively achieve state preparation independent of initial conditions in the oscillator. These results position OREO as a valuable tool for direct and efficient information extraction from bosonic quantum states, unlocking possibilities for measurement, control, and state preparation in continuous-variable quantum information processing.

In genuine nonequilibrium systems under continuous driving, thermodynamic forces are nonconservative and cannot be described by any free energy potential. Nonetheless, we show that such systems can be associated with a generalized free energy derived from a large-deviation variational principle. This variational principle yields a decomposition of fluxes, forces, and entropy production into a conservative excess part and a nonconservative housekeeping part, exemplifying an information-geometric Pythagorean theorem. The decomposition is broadly applicable—including to stochastic master equations as well as closed and open deterministic chemical reaction networks—and accessible to thermodynamic inference from short-time trajectory data. We also show that the excess entropy production obeys a thermodynamic speed limit bounding the rate of state evolution and external fluxes. We illustrate the framework on driven Markov jump processes, nonlinear chemical oscillators, and real-world metabolic networks, where we obtain tight dissipation bounds and identify futile metabolic cycles. Connections are drawn to large deviations, Onsager theory, and previous excess/housekeeping decompositions.

We identify quantum imaginary time evolution as a Riemannian gradient flow on the unitary group. We develop an upper bound for the error between the two evolutions that can be controlled through the step size of the Riemannian gradient descent that minimizes the energy of the system. We discuss implementations through adaptive quantum algorithms and present a stochastic Riemannian gradient descent algorithm in which each step is efficiently implementable on a quantum computer. We prove that for a sufficiently small step size, the stochastic evolution concentrates around the imaginary time evolution, thereby providing performance guarantees for cooling the system through stochastic Riemannian gradient descent. Finally, we show that Riemannian gradient descent can be understood as an approximation to quantum imaginary time evolution that does not employ classical optimization.

The emergent practical applicability of the Quantum Approximate Optimization Algorithm (QAOA) for approximate combinatorial optimization is a subject of considerable interest. One of the primary limitations of QAOA is the task of finding a set of good parameters, which is usually done using a variational optimization loop. Parameter transfer, or parameter concentration, is a phenomenon where QAOA angles trained on problem instances that are self-similar tend to perform well for other problem instances from that similar class. This suggests a potentially highly efficient and scalable nonvariational learning method for QAOA angle finding. In this work, we systematically study QAOA parameter transferability from small problem sizes (16 and 27 decision variables) onto large problem instances (up to 156 qubits) for heavy-hex graph Ising models with geometrically local higher-order terms using the Julia based QAOA simulation tool JuliQAOA to perform classical angle finding for up to 49 QAOA layers $\left(p\right)$. Parameter transfer of the fixed angles is validated using a combination of full state vector, projected entangled pair states, matrix product state, and LOWESA numerical simulations. We find that the QAOA parameter transfer from single instances applied to other (unseen) problem instances does not in general provide monotonically improving performance as a function of $p$—there are many cases where the performance temporarily decreases as a function of $p$—but despite this the transferred angles have a general trend of improved expectation value as the QAOA depth increases, in many cases converging close to the true ground-state energy of the $100+$ qubit instances. We also sample the hardware-compatible Ising models using the ensemble of transfer-learned QAOA parameters on several superconducting qubit IBM quantum processors with 127, 133, and 156 qubits. We find continuous solution quality improvement of the hardware-compatible QAOA circuits run on the IBM noisy intermediate-scale quantum processors up to $p=5$ on ibm_fez, up to $p=9$ on ibm_torino, and up to $p=10$ on ibm_pittsburgh.

When a highly elastic drop of a polymer solution hits a superhydrophobic surface at a high speed, a growing tail-like filament emerges vertically from the impact spot as the contact line recedes. Notably, the ligament transitions into a balloonlike shape before detaching completely from the surface (balloon regime). The ligament formation is attributed to liquid impalement upon impact into the surface protrusion spacing, and elastic forces due to polymers prevent ligament breakup. The detachment of the ligament happens when polymeric stresses balance or overcome the adhesion at the surface. This study shows that tuning droplet rheology and surface roughness enables droplets to rebound completely and without splashing at high impact speeds.

Soft hydrogels are widely used as antifouling coatings because they strongly suppress deposition of particles, cells, and microorganisms from flowing aqueous suspensions, yet the physical origin of this resistance remains unclear, as several near-wall phenomena occur simultaneously over rapidly changing length scales. Here, we combine deposition kinetics, single-particle trajectory tracking, and independent measurements of adhesion dynamics to identify the key kinetic barrier. Using polystyrene microbeads depositing onto a submicron poly(ethylene glycol) hydrogel coating in a parallel-plate flow cell, we examine how the near-wall deceleration of depositing particles during the last few seconds preceding arrest compares with the one expected for particles rolling in contact with the surface and decelerating through adhesive force maturing over time. Estimating the latter using a braking model, derived from the adhesion dynamics extracted from AFM force spectroscopy using identical colloidal probes and surface, predicts immobilization of several orders of magnitude faster than observed. When further combined with attachment rate constants deduced from the variation of the deposition rate along the channel and their ionic-strength dependence, this disagreement rules out rolling-in-contact scenario. This implies that most particles stay hovering above the surface, separated from the hydrogel by a thin fluid gap maintained by a repulsive force. We further rule out as such force both the electric double-layer interactions, attractive in the present system, and the inertial lift, as it is negligible at the present shear rates. This points to elastohydrodynamic lift (EHL)—arising from pressure-deformation coupling in lubrication flow amplified by large compliance of the hydrogel—as the only plausible barrier force. Using mechanical parameters measured by AFM and established EHL scaling, we show that EHL can indeed impose a sufficient kinetic barrier at a few tens of nanometers from the surface, cutting off attractive adhesion forces and suppressing deposition.

Quantum heat engines are nanoscale devices that convert heat into work by exploiting quantum effects, such as coherence and interference. Previous studies of these devices did not consider spin-dependent effects, which can influence the thermoelectric performance of the engine. In this work, we study the thermoelectric behavior of a quantum heat engine based on an Aharonov-Bohm ring—a mesoscopic ring where electrons exhibit interference depending on the magnetic flux it encloses—incorporating Rashba spin-orbit interaction (SOI), which couples the electron's motion and spin. We find that Rashba SOI enhances the figure of merit $ZT$, a measure of the engine's conversion efficiency. Our results suggest that controlling spin-dependent interference could lead to improvements in the fabrication of efficient thermoelectric devices.

Active nematics have served as model systems for studying defect dynamics for decades, yet the general mechanism of defect formation in three dimensions (3D) remains unclear. Here, we introduce the principal-plane framework to study disclination-loop nucleation in 3D active nematics. This approach intuitively captures the 3D director field around disclinations and connects perturbative instability analysis with geometric characterization. With particle-based simulations, we further reveal that disclinations arise from the bend instability, while their tilt reflects the competition between activity and elasticity. We show that an activity-dependent effective stiffness ${\kappa }_{\text{eff}}=\kappa /(1+2f_{\text{a}}^2)$ governs both defect morphology and the system's steady behavior. Together, these results establish a unified picture of disclination-loop emergence and provide design principles for controlling defect-driven dynamics in orientational materials.

The prediction of quantum dynamical responses lies at the heart of modern physics. Yet, modeling these time-dependent behaviors remains a formidable challenge because quantum systems evolve in high-dimensional Hilbert spaces, often rendering traditional numerical methods computationally prohibitive. While large language models have achieved remarkable success in sequential prediction, quantum dynamics presents a fundamentally different challenge: forecasting the entire temporal evolution of quantum systems rather than merely the next element in a sequence. Existing neural architectures such as recurrent and convolutional networks often require vast training datasets and suffer from spurious oscillations that compromise physical interpretability. In this work, we introduce an approach: Kolmogorov-Arnold networks (KANs) augmented with physics-informed loss functions that enforce the Ehrenfest theorems. Our method achieves superior accuracy with significantly less training data: It requires only $5.4\%$ of the samples (200) compared to temporal convolution networks (3700). We further introduce the chain of KANs, an architecture that embeds temporal causality directly into the model design, making it particularly well suited for time series modeling. Our results demonstrate that physics-informed KANs offer a compelling advantage over conventional black-box models, maintaining both mathematical rigor and physical consistency while dramatically reducing data requirements.

We present the ultrafast dynamics of the interaction between a high-intensity extreme contrast (expected to be around $\sim {10}^{-18}$ at hundreds of picoseconds timescale) femtosecond laser and a solid. Simultaneous measurements of probe Doppler spectrometry and reflectivity in pump-probe experiments reveal the presence of extreme pressure in the solid density region, which triggers a long-lived ($\sim 15$ ps) strong inward shock. Hydrodynamic simulations accurately replicate these observations, providing a detailed explanation of the underlying physics.

High-energy ion irradiation deposits extreme energy in a narrow range (∼1–10 nm) along ion trajectories in solid through electronic energy loss, producing unique irradiation effects such as ion tracks. However, intrinsic velocity effects impose an upper limit on electronic energy loss that cannot be overcome by adjusting irradiation parameters. We introduce a method using electric fields during irradiation to enhance nanoscale energy deposition by accelerating ion-excited electrons within subpicosecond timescales. Our extended field-enhanced thermal spike model quantitatively describes this enhancement and predicts a significant reduction in the electronic energy loss required for ion track formation in amorphous ${\mathrm{SiO}}_2$, which is in excellent agreement with experimental observations. This work provides an approach to control energy deposition during irradiation and boosts the wide application of ion tracks in material modification and nanoengineering to much broader extents.