General & Applied Physics · Physics & Astronomy
Chaotic Dynamics
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41 papers, from 4 September 2026 to 31 October 2026: 41 arXiv preprints.
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Newest papers
Persistence of Fractal Structures in Iterated Quantum Protocols: Robustness to Preparation and Gate Errors
Repeatedly combining entangling gates on an ensemble of qubits with measurement and postselection on a part of them induces a nonlinear evolution of the unmeasured qubits' state, whose fractal…Chaos, Fractals, and Ergodicity in Nonlinear Quantum Protocols
Iterated nonlinear quantum protocols provide a straightforward way of realizing nonlinear dynamics in quantum systems through measurement and postselection.Analog and hybrid computing in the 21st century
Analog computers solve problems through physical representations of mathematical variables and relations, often using the dynamics of electronic circuits and their associated signals.Generative AI in Turbulence: Foundations, Applications, Validations and Benchmarks
Generative AI offers new approaches to turbulence problems in which incomplete observations or unresolved d.o.f. admit multiple physically plausible solutions.Extrapolating learned dynamics across bifurcations based on generalized synchronization
Predicting dynamics across bifurcations by extrapolating trained models is crucial in diverse fields of physics.Higher Order OTOCs in Chaotic Quantum Maps: Ruelle-Pollicott Resonances and Asymptotic Freeness
Out-of-time-ordered correlators (OTOCs) measure the scrambling of information and are a powerful diagnostic of chaos.Bifurcations from Weight Space: JEPAWG on Parametric Dynamical Systems
Trained network weights are increasingly treated as data, but there is rarely ground truth about what a population of models should encode, which hampers systematic validation of weight-space methods.Scrambling Separates Superradiance from Quantum Chaos
Early studies on the Dicke model linked superradiance to quantum chaos, whereas later anisotropic Dicke and Rabi studies reached apparently conflicting conclusions.Chaos and scrambling in anisotropic Dicke and Rabi models
Identifying the quantum signatures of classical chaos remains a central question in quantum physics, and quantum scrambling provides a new perspective on this correspondence.Cumulative locality governs collective quantum charging
Quantum batteries can outperform their classical counterparts through collective quantum effects, yet the conditions enabling such advantage remain unclear.Physics-Inspired Optimizers beyond the Edge of Stability: Period-Two Orbits and Delayed Instability
Gradient descent with momentum can be viewed as a particle moving with friction in the loss landscape.Action-Quantized Scar Multiplets and Dissipative Cat States from Fractal Period-Locking in the Open Quantum Baker Map
We open the quantum baker map with a position window of width $w$ and center $q_c$.From Flow-Box Invariants to Global Conservation Laws: Machine Learning-Assisted Detection of Singularities and Near-Integrability
Conservation laws reveal invariant structure in dynamical systems and constrain their evolution to lower-dimensional level sets.The Physics Is the Network: Fast, Training-Free Neural Emulation
Neural emulation need not require learning a substitute for known physics.Beyond RMSE and Granger Gain: Multi-Horizon Forecast-Error Entropy for Nonlinear Predictive Coupling
We investigate whether the past of an auxiliary time series provides predictive information about a target beyond that contained in the target's own history.Reverse quantum aging: Restoring rhythmicity from quantum aging state
Quantum aging transition has recently been reported, whereby the oscillatory dynamics of a network of globally coupled quantum self-sustained oscillators is degraded sharply when the fraction of…Cyclotomic Structure and Exact Recurrences in Floquet Many-Body Quantum Dynamics
Periodically driven quantum systems exhibit a wide range of complex dynamics, including chaos.Quantum extreme learning for forecasting experimental chaotic electronic circuits
Quantum reservoir computing and its special case, quantum extreme learning, are powerful tools for predicting classical discrete time-series with quantum devices.Understanding Latent-Dimension Scaling in Dynamical-System Learning through Spectral Reliability
In deep learning, approximation theory motivates increasing representation size.Parameter Estimation in Machining Dynamics with Regenerative Delay and Nonsmooth Friction using Physics-Informed Neural Networks
A multi-domain eXtended Physics-Informed Neural Network (XPINN) framework is developed for nonsmooth Delay Differential Equations (DDEs).
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