Communications Physics最新文献

The timing mechanisms in biological systems operate across a vast range of scales, from microsecond precision for sound localization to annual cycles. A key open question concerns the mechanisms for encoding intermediate time intervals-hundreds of milliseconds to minutes-that are essential for navigation, communication, memory, and prediction. Here we present a theoretical framework that explains how neurons can represent such intervals using a common biophysical property: neural fatigue, where activity diminishes during sustained stimulation. Our Bayesian framework combines parametrically heterogeneous stochastic dynamical modeling with interval priors to predict available timing information independent of the actual decoding mechanism. We find that a trade-off emerges between accurately representing the most recent interval and retaining information about previous ones. We show that cellular diversity is not just tolerated but required to encode sequences of time intervals. Our work highlights the computational role of biological heterogeneity in shaping memory for time, with implications for understanding temporal processing in neural circuits.

Biological systems often consist of a small number of constituents and are therefore inherently noisy. To function effectively, these systems must employ mechanisms to constrain the accumulation of noise. Such mechanisms have been extensively studied and comprise the constraint by external forces, nonlinear interactions, or the resetting of the system to a predefined state. Here, we propose a fourth paradigm for noise constraint: self-organized resetting, where the resetting rate and position emerge from self-organization through time-discrete interactions. We study general properties of self-organized resetting systems using the paradigmatic example of cooperative resetting, where random pairs of Brownian particles are reset to their respective average. We demonstrate that such systems undergo a delocalization phase transition, separating regimes of constrained and unconstrained noise accumulation. Additionally, we show that systems with self-organized resetting can adapt to external forces and optimize search behavior for reaching target values. Self-organized resetting has various applications in nature and technology, which we demonstrate in the context of sexual interactions in fungi and spatial dispersion in shared mobility services. This work opens routes into the application of self-organized resetting across various systems in biology and technology.

The excitation scheme is essential for single-photon sources, as it governs exciton preparation, decay dynamics, and the spectral diffusion of emitted photons. While phonon-assisted excitation has shown promise in other quantum emitter platforms, its proper implementation and systematic comparison with alternative excitation schemes have not yet been demonstrated in transition metal dichalcogenide (TMD) quantum emitters. Here, we investigate the impact of various optical excitation strategies on the single-photon emission properties of bilayer WSe₂ quantum emitters. Based on our theoretical predictions for the exciton preparation fidelity, we compare the excitation via the longitudinal acoustic and breathing phonon modes to conventional above-band and near-resonance excitations. Under acoustic phonon-assisted excitation, we achieve narrow single-photon emission with a reduced spectral diffusion of 0.0129 nm, a 1.8-fold improvement over above-band excitation. Additionally, excitation through breathing-phonon mode yields a high purity of 0.947 ± 0.079  and reduces the decay time by over an order of magnitude, reaching (1.33 ± 0.04) ns. Our comprehensive study demonstrates the crucial role of phonon-assisted excitation in optimizing the performance of WSe₂-based quantum emitters, providing valuable insights for the development of single-photon sources for quantum photonics applications.

Understanding spatial and temporal order in many-body systems is a key challenge, particularly in out-of-equilibrium settings. A major hurdle is developing controlled model systems to study these phases. We propose an experiment with a driven quantum gas coupled to a dissipative optical cavity, realizing a non-equilibrium phase diagram featuring both spatial and temporal order. The system's control parameter is the detuning between the drive frequency and cavity resonance. Negative detunings yield a spatially ordered phase, while positive detunings produce phases with both spatial order and persistent oscillations, forming dissipative spatio-temporal lattices. We also identify a phase where the dynamics dephase, leading to chaotic behavior. Numerical and analytical evidence supports these superradiant phases, showing that the spatio-temporal lattice originates from cavity dissipation. The atoms experience accelerated transport, either via uniform acceleration or abrupt momentum transitions. Our work provides insights into temporal phases of matter not possible at equilibrium.

Proving thermalization from the unitary evolution of closed quantum systems is one of the oldest questions that is still only partially resolved. Efforts led to various versions of the eigenstate thermalization hypothesis (ETH), which implies thermalization under certain conditions. Whether the ETH holds in specific systems is however difficult to verify from the microscopic description of the system. In this work, we focus on thermalization under local Hamiltonians of low-entanglement initial states, which are operationally accessible in many natural physical settings, including schemes for testing thermalization in experiments and quantum simulators. We prove thermalization of these states under precise conditions that have operational significance. More specifically, motivated by arguments of unavoidable finite resolution, we define a random energy smoothing on local Hamiltonians that leads to local thermalization when the initial state has low entanglement. Finally we show that this transformation affects neither the Gibbs state locally nor, under generic smoothness conditions on the spectrum, the short-time dynamics.

Quantum-logic spectroscopy has become an increasingly important tool for the state detection and readout of trapped atomic and molecular ions which do not possess easily accessible closed-cycling optical transitions. In this approach, the internal state of the target ion is mapped onto a co-trapped auxiliary ion. This mapping is typically mediated by normal modes of motion of the two-ion Coulomb crystal in the trap. The present study investigates the role of spectator modes not directly involved in a measurement protocol relying on a state-dependent optical-dipole force. We identify a Debye-Waller-type effect that modifies the response of the two-ion string to the force. We show that cooling the spectator modes of the string allows for the detection of the rovibrational ground state of an $N_2^{+}$ molecular ion with a computed statistical fidelity exceeding 99.99%, improving on previous experiments by more than an order of magnitude while also halving the experimental time. This enhanced sensitivity enables the simultaneous identification of multiple rotational states with markedly weaker signals.

Microorganisms such as bacteria and algae navigate complex fluids, where their dynamics are vital for medical and industrial applications. However, the influence of the Reynolds number (Re) on the transport and rotational behavior of microswimmers in viscoelastic media remains poorly understood. Here, we investigate these effects for a model squirmer in flexible polymer solutions across a range of Re using Lattice Boltzmann simulations. The interaction between swimmer activity and polymer heterogeneity strongly affects behavior, with rotational enhancement up to 1400-fold and reduced self-propulsion and diffusivity for squirmers. These effects result from hydrodynamic and mechanical interactions: polymers wrap ahead of pushers and accumulate behind pullers, enhancing rotation while hindering translation through forces and torques from direct contacts or asymmetric flows. The influence of Re and squirmer-polymer boundary conditions (no-slip vs. repulsive) is also examined. Notably, no-slip conditions intensify effects above a critical Reynolds number ( ${\mathrm{Re}}_c=0.2$ ). Below this value, stronger viscous drag minimizes differences. Our findings emphasize the crucial role of polymer-swimmer interactions in shaping microswimmer behavior in viscoelastic media, informing microrobotic design in complex environments.

Biological and artificial systems encode information through complex nonlinear operations across multiple timescales. A clear understanding of the interplay between this multiscale structure and the nature of nonlinearities at play is, however, missing. Here, we study a general model where the input signal is propagated to an output unit through a processing layer via nonlinear activation functions. We focus on two widely implemented paradigms: nonlinear summation, where signals are first nonlinearly transformed and then combined; nonlinear integration, where they are combined first and then transformed. We find that fast-processing capabilities systematically enhance input-output mutual information, and nonlinear integration outperforms summation in large systems. Conversely, a nontrivial interplay between the two strategies emerges in lower dimensions as a function of interaction strength, heterogeneity, and sparsity of conections between the units. Finally, we reveal a tradeoff between input and processing sizes in strong-coupling regimes. Our results shed light on relevant features of nonlinear information processing with implications for both biological and artificial systems.

Deciding on when to initiate or relax an intervention in response to an emerging infectious disease is both difficult and important. Uncertainties from noise in epidemiological surveillance data must be hedged against the potentially unknown and variable costs of false alarms and delayed actions. Here, we clarify and quantify how case under-reporting and latencies in case ascertainment, which are predominant surveillance noise sources, can restrict the timeliness of decision-making. Decisions are modelled as binary choices between responding or not that are informed by reported case curves or transmissibility estimates from those curves. Optimal responses are triggered by thresholds on case numbers or estimated confidence levels, with thresholds set by the costs of the various choices. We show that, for growing epidemics, both noise sources induce additive delays on hitting any case-based thresholds and multiplicative reductions in our confidence in estimated reproduction numbers or growth rates. However, for declining epidemics, these noise sources have counteracting effects on case data and limited cumulative impact on transmissibility estimates. We find that this asymmetry persists even if more sophisticated feedback control algorithms that consider the longer-term effects of interventions are employed. Standard surveillance data, therefore, provide substantially weaker support for deciding when to initiate a control action or intervention than for determining when to relax it. This information bottleneck during epidemic growth may justify proactive intervention choices.

Two-dimensional materials with flat electronic bands are promising for realising exotic quantum phenomena such as unconventional superconductivity and nontrivial topology. However, exploring their vast chemical space is a significant challenge. Here we introduce elf, an unsupervised convolutional autoencoder that encodes electronic band structure images into fingerprint vectors, enabling the autonomous clustering of materials by electronic properties beyond traditional chemical paradigms. Unsupervised visualisation of the fingerprint space then uncovers hidden chemical trends and identifies promising candidates based on similarities to well-studied exemplars. This approach complements high-throughput ab initio methods by rapidly screening candidates and guiding further investigations into the mechanisms underlying flat-band physics. The elf autoencoder is a powerful tool for autonomous discovery of unexplored flat-band materials, enabling unbiased identification of compounds with desirable electronic properties across the 2D chemical space.