Communications Physics最新文献

Magnetic vortices are highly tunable, nonlinear systems with ideal properties for being applied in spin wave emission, data storage, and neuromorphic computing. However, their technological application is impaired by a limited understanding of non-conservative forces, that results in the open challenge of attaining precise control over vortex dynamics in coupled vortex systems. Here, we present an analytical model for the gyrotropic dynamics of coupled magnetic vortices within nano-pillar structures, revealing how conservative and non-conservative forces dictate their complex behavior. Validated by micromagnetic simulations, our model accurately predicts dynamic states, controllable through external current and magnetic field adjustments. The experimental verification in a fabricated nano-pillar device aligns with our predictions, and it showcases the system's adaptability in dynamical coupling. The unique dynamical states, combined with the system's tunability and inherent memory, make it an exemplary foundation for reservoir computing. This positions our discovery at the forefront of utilizing magnetic vortex dynamics for innovative computing solutions, marking a leap towards efficient data processing technologies.

The cuprate superconductor La_2-x Ba _x CuO₄ (LBCO) near x = 0.125 is a striking example of intertwined electronic orders, where 3D superconductivity is anomalously suppressed, allowing spin and charge stripe order to develop. Understanding this interplay remains a key challenge in cuprates, highlighting the necessity of external tuning for deeper insight. While in-plane uniaxial stress enhances superconductivity and suppresses stripe order, the effects of c-axis compression remains largely unexplored. Here, we use muon spin rotation (μSR) and AC susceptibility with an in situ piezoelectric stress device to investigate the spin-stripe order and superconductivity in LBCO-0.115 under c-axis compression. The measurements reveal a gradual suppression of the superconducting transition temperature (T _c) with increasing c-axis stress, in stark contrast to the strong enhancement observed under in-plane stress. We further show that while in-plane stress rapidly reduces both the magnetic volume fraction (V _m) and the spin-stripe ordering temperature (T _so), c-axis compression has no effect, with V _m and T _so exhibiting an almost unchanged behavior up to the highest applied stress of 0.21 GPa. These findings demonstrate a strong anisotropy in stress response.

Thermalization in quantum many-body systems typically unfolds over timescales governed by intrinsic relaxation mechanisms. Yet, its spatial aspect is less understood. We investigate this phenomenon in the nonequilibrium steady state (NESS) of a Bose-Hubbard chain subject to coherent driving and dissipation at its boundaries, a setup inspired by current designs in circuit quantum electrodynamics. The dynamical fingerprints of chaos in this NESS are probed using semiclassical out-of-time-order correlators within the truncated Wigner approximation. At intermediate drive strengths, we uncover a two-stage thermalization along the spatial dimension: phase coherence is rapidly lost near the drive, while amplitude relaxation occurs over much longer distances. This separation of scales gives rise to an extended hydrodynamic regime exhibiting anomalous temperature profiles, which we designate as a "prethermal" domain. At stronger drives, the system enters a nonthermal, non-chaotic finite-momentum condensate characterized by sub-Poissonian photon statistics and a spatially modulated phase profile, whose stability is undermined by quantum fluctuations. We explore the conditions underlying this protracted thermalization in space and argue that similar mechanisms are likely to emerge in a broad class of extended driven-dissipative systems.

Finding molecular ground states and energies with variational quantum eigensolvers is central to chemistry applications on quantum computers. Physically motivated ansätze based on excitation operators respect physical symmetries, but existing quantum-aware optimizers, such as Rotosolve, have been limited to simpler operator types. To fill this gap, we introduce ExcitationSolve, a fast quantum-aware optimizer that is globally-informed, gradient-free, and hyperparameter-free. ExcitationSolve extends these optimizers to parameterized unitaries with generators G of the form G ³ = G exhibited by excitation operators in approaches such as unitary coupled cluster. ExcitationSolve determines the global optimum along each variational parameter using the same quantum resources that gradient-based optimizers require for one update step. We provide optimization strategies for both fixed and adaptive variational ansätze, along with generalizations for simultaneously selecting and optimizing multiple excitations. On molecular ground state energy benchmarks, ExcitationSolve outperforms state-of-the-art optimizers by converging faster, achieving chemical accuracy for equilibrium geometries in a single parameter sweep, yielding shallower adaptive ansätze and remaining robust to real hardware noise. By uniting physical insight with efficient optimization, ExcitationSolve paves the way for scalable quantum chemistry calculations.

Non-line-of-sight (NLOS) imaging typically relies on the use of ultrashort laser pulses and time-resolved detection to then reconstruct 3D environments that are hidden from the direct line-of-sight. However, the same scattering mechanism and wall-reflections that allow light to propagate into the hidden environment and back again ultimately limit both resolution and imaging distances even at high laser powers. Non-optical, such as acoustic and radio-wave approaches promise to solve some of these issues but have yet to achieve results comparable to optical systems. We present an ultrasound-based NLOS imaging system based on a scanning ultrasound emitter and receiver operating in a frequency range similar to common bats that demonstrates high-resolution 3D reconstruction of hidden scenes. We successfully image multiple targets and complex scenes with  ~ cm depth resolution at distances up to 2 m away from the scattering surface. Measurements of the NLOS modulation transfer function quantify the spatial resolution to also be  ~ 1 cm, which is comparable to traditional optical NLOS techniques.

Microscopic particles flowing through narrow channels may accumulate near bifurcation points provoking flow reduction, clogging and ultimately chip breakage in a microfluidic device. Here we show that the full flow behavior of colloidal particles through a microfluidic Y-junction can be controlled by tuning the pair interactions and the degree of confinement. By combining experiments with numerical simulations, we investigate the dynamic states emerging when magnetizable colloids flow through a symmetric Y-junction such that a single particle can pass through both gates with the same probability. We show that clogging, induced by the inevitable presence of a stagnation point, can be avoided by repulsive interactions. Moreover we tune the pair interactions to steer branching into the two channels: attractive particles are flowing through the same gate, while repulsive colloids alternate between the two gates. Even details of the particle assembly such as buckling at the exit gate are tunable by the interactions and the channel geometry.

Recently there has been much effort in developing a quantum generalisation of reference frame transformations. Despite important progress, a complete understanding of their principles is still lacking. Here we derive quantum reference frame transformations for a broad range of symmetry groups from first principles, using only standard quantum theory. Our framework, naturally based on incoherent rather than coherent group averaging, yields reversible transformations that only depend on the reference frames and system of interest. We find more general transformations than those studied so far, which are valid only in a restricted subspace. Our framework contains additional degrees of freedom in the form of an "extra particle", which carries information about the quantum features of reference frame states. We study the centrally extended Galilei group specifically, highlighting key differences from previous proposals.

It is well-understood that the network structure of complex systems affects their robustness; the role played by the shape of spatially embedded networks, however, is less explored. Here, we study the robustness of networks where links are physical objects or physically transfer some quantity, hence the links can be disrupted at any point along their trajectory. To model physical damage, we tile each network with boxes and we sequentially damage these boxes, removing any link from the network that intersects a damaged tile. Using model and empirical networks, we systematically explore how the layout and the structure of networks jointly affect the resulting percolation transition. For example, we analytically and numerically show that randomly damaging a vanishing fraction of tiles is enough to destroy large-scale connectivity in randomly embedded networks. This demonstrates that the presence of long-range links makes networks extremely vulnerable to physical damage. Our work contributes to the emergent theory of physical networks.

From fundamental sciences to economics and industry, discrete optimization problems are ubiquitous. Yet, their complexity often renders exact solutions intractable, necessitating the use of approximate methods. Heuristics inspired by classical physics have long played a central role in this domain. More recently, quantum annealing has emerged as a promising alternative, with hardware implementations realized on both analog and digital quantum devices. Here, we develop a heuristic inspired by quantum annealing, using Generalized Coherent States as a parameterized variational Ansatz to represent the quantum state. This framework allows for the analytical computation of energy and gradients with low-degree polynomial complexity, enabling the study of large problems with thousands of spins. Concurrently, these states capture non-trivial entanglement, crucial for the effectiveness of quantum annealing. We benchmark the heuristic on the three-dimensional Edwards-Anderson model and compare the solution quality and runtime of our method to other popular heuristics. Our findings suggest that it offers a scalable way to leverage quantum effects for complex optimization problems, with the potential to complement or improve upon conventional alternatives in large-scale applications.

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.