We develop a microscopic biophysical model for self-organization and reshaping of artificial tissue, that is codriven by microscopic active forces between cells and an extracellular matrix (ECM), and macroscopic forces that develop within the tissue, finding close agreement with experiment. Microscopic active forces are stimulated by $\mu \mathrm{m}$-scale interactions between cells and the ECM within which they exist, and when large numbers of cells act together these forces drive, and are affected by, macroscopic-scale self-organization and reshaping of tissues in a feedback loop. To understand this loop, there is a need to (1) construct microscopic biophysical models that can simulate these processes for the very large number of cells found in tissues, (2) validate and calibrate those models against experimental data, and (3) understand the active feedback between cells and the extracellular matrix, and its relationship to macroscopic self-organization and reshaping of tissue. Our microscopic biophysical model consists of a contractile network representing the ECM, that interacts with a large number of cells via dipole forces, to describe macroscopic self-organization and reshaping of tissue. We solve the model using simulated annealing, finding close agreement with experiments on artificial neural tissue. We discuss the calibration of model parameters. We conclude that feedback between microscopic cell-ECM dipole interactions and tissue-scale forces is a key factor in driving macroscopic self-organization and reshaping of tissue. We discuss the application of the biophysical model to the simulation and rational design of artificial tissues.

We report the influence of the excitation conditions on the emission behavior of a carbon-nanodot-based microcavity. The emission properties under nanosecond excitation indicate room-temperature lasing with a lasing threshold of $0.1{\mathrm{Jcm}}^{-2}$. The input-output characteristic is also dependent on the repetition rate of the excitation source. Higher repetition rates generate more heat in the gain medium, which increases the losses in the cavity and has a negative impact on the emission characteristics.

Several complex systems can be modeled as large networks in which the state of the nodes continuously evolves through interactions among neighboring nodes, forming a high-dimensional nonlinear dynamical system. One of the main challenges of Network science consists in predicting the impact of network topology and dynamics on the evolution of the states and, especially, on the emergence of collective phenomena, such as synchronization. We address this problem by proposing a Dynamics Approximate Reduction Technique (DART) that maps high-dimensional (complete) dynamics unto low-dimensional (reduced) dynamics while preserving the most salient features, both topological and dynamical, of the original system. DART generalizes recent approaches for dimension reduction by allowing the treatment of complex-valued dynamical variables, heterogeneities in the intrinsic properties of the nodes as well as modular networks with strongly interacting communities. Most importantly, we identify three major reduction procedures whose relative accuracy depends on whether the evolution of the states is mainly determined by the intrinsic dynamics, the degree sequence, or the adjacency matrix. We use phase synchronization of oscillator networks as a benchmark for our threefold method. We successfully predict the synchronization curves for three phase dynamics (Winfree, Kuramoto, theta) on the stochastic block model. Moreover, we obtain the bifurcations of the Kuramoto-Sakaguchi model on the mean stochastic block model with asymmetric blocks and we show numerically the existence of periphery chimera state on the two-star graph. This allows us to highlight the critical role played by the asymmetry of community sizes on the existence of chimera states. Finally, we systematically recover well-known analytical results on explosive synchronization by using DART for the Kuramoto-Sakaguchi model on the star graph. Our work provides a unifying framework for studying a vast class of dynamical systems on networks.

We propose to induce a time-crystalline state in a high-$T_c$ superconductor, by optically driving a sum resonance of the Higgs mode and a Josephson plasma mode. The generic cubic process that couples these fundamental excitations converts driving of the sum resonance into simultaneous resonant driving of both modes, resulting in an incommensurate subharmonic motion. We use a numerical implementation of a semiclassical driven-dissipative lattice gauge theory on a three-dimensional layered lattice, which models the geometry of cuprate superconductors, to demonstrate the robustness of this motion against thermal fluctuations. We demonstrate this light-induced time-crystalline phase for mono- and bilayer systems and show that this order can be detected for pulsed driving under realistic technological conditions.

Atom arrays are a new type of quantum light-matter interface. Here we propose to employ one-dimensional ordered arrays as atomic waveguides. These arrays support optical guided modes that do not decay into free space. We show that these modes can be harnessed to mediate tunable, long-range interactions between additional “impurity qubits” coupled to the chain, without need for photonic structures. The efficient coupling between qubits and atomic waveguides enables the realization of tunable qubit-qubit interactions, which can be short or long range, dissipative or coherent, as well as chiral. Moreover, owing to the two-level nature of atoms, these waveguides are intrinsically quantum. In contrast to classical waveguides, where photons do not interact with each other, atomic waveguides display strong nonlinearities, which create a tunable dissipative channel for qubit-qubit interactions, and opens the door to the exploration of many-body physics between guided photons. This physics is universal as it only relies on photon interference and can also be observed with other types of quantum emitters, such as those in molecular or solid-state systems.

Due to its wide reaching implications for everything from identifying hot spots of income inequality to political redistricting, there is a rich body of literature across the sciences quantifying spatial patterns in socioeconomic data. In particular, the variability of indicators relevant to social and economic well-being between localized populations is of great interest, as it pertains to the spatial manifestations of inequality and segregation. However, heterogeneity in population density, sensitivity of statistical analyses to spatial aggregation, and the importance of predrawn political boundaries for policy intervention may decrease the efficacy and relevance of existing methods for analyzing spatial socioeconomic data. Additionally, these measures commonly lack either a framework for comparing results for qualitative and quantitative data on the same scale, or a mechanism for generalization to multiregion correlations. To mitigate these issues associated with traditional spatial measures, here we view local deviations in socioeconomic variables from a topological lens rather than a spatial one, and use an information theoretic network approach based on the generalized Jensen-Shannon divergence to distinguish distributional quantities across adjacent regions. We apply our methodology in a series of experiments to study the network of neighboring census tracts in the continental US, quantifying the decay in two-point distributional correlations across the network, examining the county-level socioeconomic disparities induced from the aggregation of tracts, and constructing an algorithm for the division of a city into homogeneous clusters. These results provide a framework for analyzing the variation of attributes across regional populations and shed light on universal patterns in socioeconomic attributes.

A triangular-lattice antiferromagnet of ${\mathrm{Ag}}_2{\mathrm{FeO}}_2$ was synthesized under high pressure. Its magnetism was studied in terms of electrical resistivity, magnetic susceptibility, heat capacity, powder neutron scattering, and Mössbauer spectroscopy. The magnetic state of ${\mathrm{Ag}}_2{\mathrm{FeO}}_2$ changes successively through a second-order phase transition at $T_{\mathrm{p}}=36$ K and a crossover at $T_{\mathrm{c}}=20$ K. A partially disordered (PD) state appears below $T_{\mathrm{p}}$, in which $\approx \mathrm{2/3}$ spins are ordered and the remaining $\approx \mathrm{1/3}$ spins fluctuate, which state persists at least down to 5 K. The spin correlation length starts to grow at $T_{\mathrm{p}}$; however, it remains very short ($\approx 27$ Å) below $T_{\mathrm{c}}$. This exotic magnetism is concerned with strong frustration in the classic antiferromagnetic triangular lattice.

Vortices are particle-type solutions with topological charges that can steer the dynamics in various physical systems. By the application of electromagnetic fields onto a homeotropic nematic liquid crystal cell, we are able to induce a vortex triplet that remains stable and trapped at a given location. For a low frequency of the driven voltage, we observe that the vortex triplet is unstable and gives rise to the appearance of a topological lattice. Based on an amplitude equation valid close to reorientational instability, it allows us to reveal the origin of the vortex triplet and vortex lattice. Numerical simulations show a quite fair agreement with theoretical findings and experimental observations.

The phenomenon of metastability can shape dynamical processes on all temporal and spatial scales. Here, we induce metastable dynamics by pumping ultracold bosonic atoms from the lowest band of an optical lattice to an excitation band, via a sudden quench of the unit cell. The subsequent relaxation process to the lowest band displays a sequence of stages, which include a metastable stage, during which the atom loss from the excitation band is strongly suppressed. Using classical-field simulations and analytical arguments, we provide an explanation for this experimental observation, in which we show that the transient condensed state of the atoms in the excitation band is a dark state with regard to collisional decay and tunneling to a low-energy orbital. Therefore the metastable state is stabilized by destructive interference due the chiral phase pattern of the condensed state. Our experimental and theoretical study provides a detailed understanding of the different stages of a paradigmatic example of many-body relaxation dynamics.

We show that chiral (nearly) flat band superconductivity can develop and host novel Majorana fermions at a time-reversal pair of symmetry-protected three-band crossing points. Based on symmetry analysis, mean-field study, and superfluid stiffness calculation, we determine and analyze the irreducible pairing channels with flat band pairings in the low-energy spin-1 fermion theory. Flat band pairing can enhance superconductivity dramatically, where the critical temperature scales linearly in the interaction strength. While fully gapped flat band pairing states develop in the single-component pairing channels, we find chiral $\overline{p}\pm i\overline{p}$ flat band superconductivity in the multicomponent pairing channels. Three-dimensional itinerant Majorana fermions arise at the bulk nodal points, whereas Majorana arcs appear on the surface.

We theoretically investigate a quasi-one-dimensional quantum wire, where the lowest two subbands are populated, in the presence of a helical magnetic field. We uncover a backscattering mechanism involving the helical magnetic field and Coulomb interaction between the electrons. The combination of these ingredients results in scattering resonances and partial gaps which give rise to nonstandard plateaus and conductance dips at certain electron densities. The positions and values of these dips are independent of material parameters, serving as direct transport signatures of this mechanism. Our theory applies to generic quasi-one-dimensional systems, including a Kondo lattice and a quantum wire subject to intrinsic or extrinsic spin-orbit coupling. Observation of the universal conductance dips would identify a strongly correlated fermion system hosting fractional excitations, resembling the fractional quantum Hall states.

We investigate ultrafast coherent quantum dynamics of undoped $\mathrm{Ba}\mathrm{Bi}{\mathrm{O}}_3$ driven by a strong laser pulse. Our calculations demonstrate that in a wide range of radiation frequencies and intensities the system undergoes a transient change from the insulating to the metallic state, where the charge density wave and the corresponding energy spectrum gap vanish. The transition takes place on the ultrafast timescale of tens femtoseconds, comparable to the period of the corresponding lattice vibrations. The dynamics are determined by a complex interplay of the particle-hole excitation over the gap and of the tunneling through it, giving rise to the highly nontrivial time evolution which comprises high harmonics and reveals periodic reappearance of the gap. The time evolution is obtained by solving the dynamical mean-field theory equations with the realistic parameters for the system and radiation. Results are summarized in the phase diagram, helpful for a possible experimental setup to achieve a dynamical control over the conduction state of this and other materials with the similarly strong electron-phonon interaction.

We present a self-contained theory for the exact calculation of particle number counting statistics of noninteracting indistinguishable particles in the canonical ensemble. This general framework introduces the concept of auxiliary partition functions, and represents a unification of previous distinct approaches with many known results appearing as direct consequences of the developed mathematical structure. In addition, we introduce a general decomposition of the correlations between occupation numbers in terms of the occupation numbers of individual energy levels, that is valid for both nondegenerate and degenerate spectra. To demonstrate the applicability of the theory in the presence of degeneracy, we compute energy-level correlations up to fourth order in a bosonic ring in the presence of a magnetic field.

Quantum computation promises an exponential speedup of certain classes of classical calculations through the preparation and manipulation of entangled quantum states. So far, most molecular simulations on quantum computers, however, have been limited to small numbers of particles. Here we prepare a highly entangled state on a 53-qubit IBM quantum computer, representing 53 particles, which reveals the formation of an exciton condensate of photon particles and holes. While the experimental realization of ground state exciton condensates remained elusive for more than 50 years, such condensates were recently achieved for electron-hole pairs in graphene bilayers and metal chalcogenides. Our creation of ground state photon condensates has the potential to further the exploration of exciton condensates, and this novel preparation may play a role in realizing efficient room-temperature energy transport.

The study of superconducting materials that also possess nontrivial correlations or interactions remains an active frontier of condensed matter physics. $\mathrm{Nb}{\mathrm{Se}}_2$ belongs to this class of superconductors and recent research has focused on the two-dimensional properties of this layered, superconducting material. Often, electronic transport measurements are used to investigate the superconducting properties of these materials, hence it is key to obtain a thorough understanding of transport in these systems. We investigate the transition between the superconducting and resistive state using radio-frequency AC excitation. Despite being a single piece of superconductor, the devices show novel dynamical Josephson effects behavior reminiscent of the AC Josephson effect observed in Josephson junctions. Detailed analysis uncovers the origin of this effect, identifying two types of vortex motion that categorize the transition to the normal state. Our results shed light on the nature of superconductivity in this material, unearthing exotic phenomena by exploiting nonequilibrium superconducting effects in atomically thin materials.

We study the potential of all-electrical inductive techniques for the spectroscopy of propagating forward volume spin waves. We develop a one-dimensional model to account for the electrical signature of spin-wave reflection and transmission between inductive antennas and validate it with experiments on a perpendicularly magnetized Co/Ni multilayer. We describe the influence of the antenna geometry and antenna-to-antenna separation, as well as that of the material parameters on the line shape of the inductive signals. For a finite damping, the broadband character of the antenna emission in the wave vector space imposes to take into account the growing decoherence of the magnetization waves on their spatial propagation. The transmission signal can be viewed as resulting from two contributions: A first one from propagating spin-waves leading to an oscillatory phase of the broadband transmission coefficient and another one originating from the distant induction of ferromagnetic resonance because of the long-range stray fields of realistic antennas. Depending on the relative importance of these two contributions, the decay of the transmitted signal with the propagation distance may not be exponential and the oscillatory character of the spin-wave phase on propagation may be hidden. Our model and its experimental validation allow us to define geometrical and material specifications to be met to enable the use of forward volume spin waves as efficient information carriers.

Machine-learning techniques have shown their capability for studying phase transitions in condensed matter physics. Here, we employ machine-learning techniques to study the nuclear liquid-gas phase transition. We adopt an unsupervised learning and classify the liquid and gas phases of nuclei directly from the final-state raw experimental data of heavy-ion reactions. Based on a confusion scheme which combines the supervised and unsupervised learning, we obtain the limiting temperature of the nuclear liquid-gas phase transition. Its value $9.24\pm 0.04\mathrm{MeV}$ is consistent with that obtained by the traditional caloric curve method. Our study explores the paradigm of combining machine-learning techniques with heavy-ion experimental data, and it is also instructive for studying the phase transition of other uncontrollable systems, such as QCD matter.

Quantum state transformations that are robust to experimental imperfections are important for applications in quantum information science and quantum sensing. Counterdiabatic (CD) approaches, which use knowledge of the underlying system Hamiltonian to actively correct for diabatic effects, are powerful tools for achieving simultaneously fast and stable state transformations. Protocols for CD driving have thus far been limited in their experimental implementation to discrete systems with just two or three levels, as well as bulk systems with scaling symmetries. Here, we extend the tool of CD control to a discrete synthetic lattice system composed of as many as nine sites. Although this system has a vanishing gap and thus no adiabatic support in the thermodynamic limit, we show that CD approaches can still give a substantial improvement in fidelity over naive, fast protocols.

We show that, under an appropriate out-of-plane static magnetic field, nuclear spins in a thin specimen on a surface acoustic wave (SAW) cavity can be resonantly excited and detected through spin-rotation coupling. Since such a SAW cavity can have the quality factor as high as ${10}^4$ and the mode volume as small as ${10}^{-2}{\mathrm{mm}}^3$ the signal-to-noise ratio in detecting the resonance is estimated to be quite high. We argue that detecting nuclear spin resonance of a single flake of an atomically thin layer of two-dimensional semiconductor, which has so far been beyond hope with the conventional inductive method, can be a realistic target with the proposed scheme.

Anisotropic dipole-dipole interaction often plays a key role in biological, soft, and complex matter. For it to induce nontrivial order in the system, there must be additional repulsive interactions or external potentials involved that partially or completely fix the positions of the dipoles. These positions can often be represented as an underlying lattice on which dipole interaction induces orientational ordering of the particles. On lattices in the Euclidean plane, dipoles have been found to assume different ground-state configurations depending on the lattice type, with a global ordering in the form of a macrovortex being observed in many cases. A similar macrovortex configuration of dipoles has recently been shown to be the sole ground state for dipoles positioned on spherical lattices based on solutions of the Thomson problem. At the same time, no symmetric configurations have been observed, even though the positional order of Thomson lattices exhibits a high degree of symmetry. Here, we show that a different choice of spherical lattices based on Caspar-Klug construction leads to ground states of dipoles with various degrees of symmetry, including the icosahedral symmetry of the underlying lattice. We analyze the stability of the highly symmetric metastable states, their symmetry breaking into subsymmetries of the icosahedral symmetry group, and present a phase diagram of symmetries with respect to lattice parameters. The observed relationship between positional order and dipole-induced symmetry breaking hints at ways of fine-tuning the structure of spherical assemblies and their design.