We study the propagation of electromagnetic waves in tenuous plasmas, where the wave frequency ${\omega }_0$ is much larger than the plasma frequency ${\omega }_{\mathrm{P}}$. We show that in pair plasmas, nonlinear effects are weak for $a_0\ll {\omega }_0/{\omega }_{\mathrm{P}}$, where $a_0$ is the wave strength parameter. In electron-proton plasmas, a more restrictive condition must be satisfied, namely, either $a_0\ll 1/{\omega }_{\mathrm{P}}{\tau }_0$, where ${\tau }_0$ is the duration of the radiation pulse, or $a_0\ll 1$. We derive the equations that govern the evolution of the pulse in the weakly nonlinear regime. Our results have important implications for the modeling of fast radio bursts. We argue that (i) millisecond duration bursts with a smooth profile must be produced in a proton-free environment, where nonlinear effects are weaker, and (ii) propagation through an electron-proton plasma near the source can imprint a submicrosecond variability on the burst profile.

This work introduces an approach to quantum simulation by leveraging continuous-variable systems within a photonic hardware-inspired framework. The primary focus is on simulating static properties of the ground state of Hamiltonians associated with infinite-dimensional systems, such as those arising in quantum field theory. We present a continuous-variable variational quantum eigensolver compatible with state-of-the-art photonic technology. The framework we introduce allows us to compare discrete and continuous variable systems without introducing a truncation of the Hilbert space, opening the possibility to investigate the scenarios where one of the two formalisms performs better. We apply it to the study of static properties of the Bose-Hubbard model and demonstrate its effectiveness and practicality, highlighting the potential of continuous-variable quantum simulations in addressing complex problems in quantum physics.

We propose a generalized Fourier's law, based on recent mode-coupling calculations, for one-dimensional nonlinear lattices in nonequilibrium simulations, where the boundaries are kept at different temperatures. Though not useful for the typical textbook numerical examples, we are able to demonstrate numerically the theory for an anharmonic lattice with force field parameters similar to those of realistic low-dimensional structures such as covalently bonded molecular chains or carbon nanotubes, specifically showing that the asymptotic heat conductivity is described by the mode-coupling theory in good approximation, without any adjustable parameter.

We propose a simplified probabilistic model to investigate the fundamental physics of particle transport in parallel channels having bypasses and defects, mimicking the distinctive pathways found in a class of solid-electrolyte materials. Our results reveal that increases in the bypass interval and the number of defects induce significant macroscopic density fluctuations, substantially reducing overall flow rates. Notably, we find that the particle flow rate decreases exponentially with the product of bypass interval and defect probability. The decay exponent was dependent on the particle density, which reflects the nonlinear effects of particle congestion. Our findings may suggest that the presence of more bypass sites and fewer defects in the pathways has a significant impact on the performance of electrolyte materials.

We show that swallowtail catastrophe consisting of various-order non-Hermitian and Hermitian degeneracies naturally exists in the dynamics of two-mode driven-dissipative quadrature squeezing systems that break pseudo-Hermiciticy by judiciously engineered losses. We reveal that the swallowtail degeneracy structure enables nontrivial braiding of complex eigenvalues by looping around an exceptional line in the parameter space. Our findings provide a comprehensive understanding of the degeneracy geometry in two-mode driven-dissipative bosonic quadratic systems, opening pathways toward topologically nontrivial control of Gaussian states.

Radionuclide identification is a radioanalytical method employed in various scientific disciplines that utilize alpha-particle or gamma-ray spectrometric assays, ranging from astrophysics to nuclear medicine. Radionuclide libraries in conventional radionuclide identification systems are crafted in a manual fashion, accompanying labor-intensive and error-prone user tasks and hindering library customization. This research presents a computational algorithm and the architecture of its dedicated software that can automatically generate tailored radionuclide libraries. Progenitor-progeny recurrence relations were modeled to enable recursive computation of radionuclide subsets. This theoretical concept was incorporated into open-source software called RecurLib and validated against four actinide decay series and 12 radioactive substances, including a uranium-glazed legacy Fiestaware, natural uranium and thorium sources, a ${}^{226}\mathrm{Ra}$ sample, and the medical radionuclides ${}^{225}\mathrm{Ac}, {}^{177}\mathrm{Lu}$, and ${}^{99m}\mathrm{Tc}$. The developed algorithm yielded radionuclide libraries for all the tested specimens within minutes, demonstrating its efficiency and applicability across diverse scenarios. The proposed approach introduces a framework for computerized radionuclide library generation, thereby trivializing library-driven radionuclide identification and facilitating the spectral recognition of unregistered radionuclides in radiation spectrometry.

The domain structure of a fluid ferroelectric nematic is dramatically different from the domain structure of solid ferroelectrics since it is not restricted by rectilinear crystallographic axes and planar surface facets. We demonstrate that thin films of a ferroelectric nematic seeded by colloidal inclusions produce domain walls (DWs) in the shape of conics such as a parabola. These conics reduce the bound charge within the domains and at the DWs. An adequate description of the domain structures requires one to analyze the electrostatic energy, which is a challenging task. Instead, we demonstrate that a good approximation to the experimentally observed polydomain textures is obtained when the divergence of spontaneous polarization—which causes the bound charge—is heavily penalized by assuming that the elastic constant of splay in the Oseen-Frank energy is much larger than those for twist and bend. The model takes advantage of the fact that the polarization vector is essentially parallel to the nematic director throughout the sample.

We investigate the crumpling of an elastoplastic sheet as it is repeatedly crushed onto itself by rolling it into a cylinder and twisting it axially while allowing the end-to-end length to evolve freely. The sheet buckles and folds into structures that repeat but sharpen over hundreds of cycles to a remarkable degree before forming different configurations. Below a critical amplitude, reconfigurations decrease with applied cycles but continue to occur for large enough loading amplitude as the topology of the sheet changes as it tears. The sheet structure as measured by the mean curvature and the total crease length evolves logarithmically with cycle number with a rate that increases with compaction. We explain the progress of creasing using a flat folding model, and we show the logarithmic growth as being a consequence of individual creases becoming sharper with the number of folding cycles, leading to bifurcations in the folding pathway.

We perform a comprehensive analysis of a collective decision-making model inspired by honeybee behavior. This model integrates individual exploration for option discovery and social interactions for information sharing, while also considering option qualities. Our assessment of the decision process outcome employs standard consensus metrics and investigates its correlation with convergence time, revealing common trade-offs between speed and accuracy. Furthermore, we show the model's compliance with Weber's law of relative stimulus perception, aligning with previous analysis of collective decision problems. Our study also identifies nonequilibrium critical behavior in specific limits of the model, where the highest values of consensus are achieved. This result highlights the intriguing relationship between optimal performance, critically, and the fluctuations caused by finite-size effects, often seen in biological systems. Our findings are especially relevant for finite adaptive systems, as they provide insights into navigating decision-making scenarios with similar options more effectively.

Temporal networks present a unique challenge regarding the community discovery task. The inherent dynamism of these systems requires an intricate understanding of memory effects and structural heterogeneity, which are often key drivers of network evolution. This study focuses on Markovian temporal networks and addresses these challenges with an innovative community detection method that introduces a modularity function. We specifically demonstrate how our approach enhances the detectability threshold, thereby improving the effectiveness of community detection in such a dynamic setting. We show that by associating memory directly with nodes' memberships and including it into the modularity expression, we can enhance the detectability threshold compared to scenarios where memory is ignored, thus extending the conditions under which communities can be accurately identified. We validate our approach through extensive numerical simulations, confirming its efficacy in a controlled environment. Additionally, by applying our method to real-world data, we not only demonstrate its practicality and robustness but also reveal its capacity to indirectly tackle additional challenges, such as determining the optimal time window for aggregating data in dynamic graphs.

The algorithm “automated compression of environments” (ACE) [M. Cygorek et al., Nat. Phys. 18, 662 (2022)] provides a versatile way of simulating an extremely broad class of open quantum systems. This is achieved by encapsulating the influence of the environment, which is determined by the interaction Hamiltonian(s) and initial states, into compact process tensor matrix product operator (PT-MPO) representations. The generality of the ACE method comes at high numerical cost. Here, we demonstrate that orders-of-magnitude improvement of ACE is possible by changing the order of PT-MPO contraction from a sequential to a treelike scheme. The problem of combining two partial PT-MPOs with large inner bonds is solved by a preselection approach. The drawbacks of the preselection approach are that the MPO compression is suboptimal and that it is more prone to error accumulation than sequential combination and compression. We therefore also identify strategies to mitigate these disadvantages by fine-tuning compression parameters. This results in a scheme that is similar in compression efficiency and accuracy to the original ACE algorithm, yet is significantly faster. Our numerical experiments reach similar conclusions for bosonic and fermionic test cases, suggesting that our findings are characteristic of the combination of PT-MPOs more generally.

Algorithms developed to solve many-body quantum problems, like tensor networks, can turn into powerful quantum-inspired tools to tackle issues in the classical domain. This work focuses on matrix product operators, a prominent numerical technique to study many-body quantum systems, especially in one dimension. It has been previously shown that such a tool can be used for classification, learning of deterministic sequence-to-sequence processes, and generic quantum processes. We further develop a matrix product operator algorithm to learn probabilistic sequence-to-sequence processes and apply this algorithm to probabilistic cellular automata. This new approach can accurately learn probabilistic cellular automata processes in different conditions, even when the process is a probabilistic mixture of different chaotic rules. In addition, we find that the ability to learn these dynamics is a function of the bitwise difference between the rules and whether one is much more likely than the other.

For a Weyl semimetal (WSM) in a magnetic field, a semiclassical description of the Fermi-arc surface state dynamics is usually employed for explaining various unconventional magnetotransport phenomena, e.g., Weyl orbits, the three-dimensional quantum Hall effect, and the high transmission through twisted WSM interfaces. For a half-space geometry, we determine the low-energy quantum eigenstates for a four-band model of a WSM in a magnetic field perpendicular to the surface. The eigenstates correspond to in- and out-going chiral Landau level (LL) states, propagating (anti)parallel to the field direction near different Weyl nodes, which are coupled by evanescent surface-state contributions generated by all other LLs. These replace the Fermi arc in a magnetic field. Computing the phase shift accumulated between in- and out-going chiral LL states, we compare our quantum-mechanical results to semiclassical predictions. We find quantitative agreement between both approaches.

Topological magnetic insulators host chiral gapless edge modes. In the presence of strong interaction effects, the spin of these modes may fractionalize. Studying a two-dimensional array of coupled insulating spin-$1/2$ chains, we show how spatially modulated magnetic fields and Dzyaloshinskii-Moriya interactions can be exploited to realize chiral spin liquids or integer and fractional spin quantum Hall effect phases. These are characterized by a gapped bulk spectrum and gapless chiral edge modes with fractional spin. The spin fractionalization is manifested in the quantized spin conductance, which can be used to probe the fractional spin quantum Hall effect. We analyze the system via bosonization and perturbative renormalization group techniques that allow us to identify the most relevant terms induced by the spin-spin interactions that open gaps and render the system topological under well-specified resonance conditions. We show explicitly that the emerging phase is a genuine chiral spin liquid. We suggest that the phases can be realized experimentally in synthetic spin chains and ultracold atom systems.

Laboratory experiments with bacterial colonies under well-controlled conditions often lead to evolutionary diversification, where at least two ecotypes emerge from an initially monomorphic population. Empirical evidence suggests that such an “evolutionary branching” occurs stochastically, even under fixed and stable conditions. This stochasticity is characterized by (i) the occurrence of branching in a significant fraction, but not all, of the experimental settings, (ii) the emergence at widely varying times, and (iii) variable relative abundances of the resulting subpopulations across experiments. Theoretical approaches to understanding evolutionary branching under these conditions have been previously developed within the (deterministic) framework of “adaptive dynamics.” Here, we advance the understanding of the stochastic nature of evolutionary outcomes by introducing the concept of “stochastic trade-offs” as opposed to “hard” ones. The key idea is that the stochasticity of mutations occurs in a high-dimensional trait space and this translates into variability that is constrained to a flexible tradeoff curve in a lower-dimensional space. By incorporating this additional source of stochasticity, we are able to account for the observed empirical variability and make predictions regarding the likelihood of evolutionary branching under different experimental conditions. This approach effectively bridges the gap between theoretical predictions and empirical observations, providing insights into when and how evolutionary branching is more likely to occur in laboratory experiments.

Serial femtosecond crystallography (SFX) is a powerful technique for studying ultrafast structural dynamics in matter. However, the analysis of SFX data is challenging and would benefit from a systematic theoretical framework. By employing a quantum-electrodynamics approach, we demonstrate that conventional SFX practices are not only intuitively plausible but also firmly grounded in well-established principles. We show that standard SFX assumptions remain valid even when non-Born-Oppenheimer effects near conical intersections are significant. Through a modal decomposition of diffraction signals in reciprocal space, we derive a corresponding decomposition of the electron density in real space, valid in the limit of small pump excitation probability. This result justifies a key assumption made in a recent study [A. Hosseinizadeh et al., Nature (London) 599, 697 (2021)]. Remarkably, we find that the number of structural modes contributing to the SFX signal near conical intersections depends solely on the number of electronic states involved, regardless of the number of nuclear degrees of freedom. Our work establishes a foundation for accelerating the structural inversion of time-resolved SFX data and provides a rigorous theoretical basis for interpreting ultrafast structural dynamics.

We study the mass exchange between two rotating, quantum massive vortices in a two-component Bose-Einstein condensate. The vortices, in the majority component, exhibit a filled core, where the in-filling minority component undergoes a quantum tunneling effect. Remarkably, we observe that the two-vortex system features stable Josephson oscillations, as well as all the nonlinear phenomena, including the macroscopic quantum self-trapping, that characterize a Bosonic Josephson Junction. We propose an analytical model for describing the intervortex tunneling, obtained by implementing the coherent-state representation of the two-mode Bose-Hubbard model. This allows us to give the explicit expression of the model's parameters in terms of the physical macroscopic parameters of the two-vortex system. The comparison of the dynamical scenario predicted by the model with that emerging from the Gross–Pitaevskii equations is very good for sufficiently small atom numbers, while at larger atom numbers it grows less precise, presumably due to the partial exclusion of the many-body interactions from our model. The definition of an effective self-interaction parameter allows us to include the many-body effects, thus restoring a quite good agreement with the numerical results. Interestingly, the recognition of the bosonic Josephson dynamics paves the way to the investigation of new dynamical behaviors in multivortex configurations.