Due to the interference of ion dynamics in perovskite materials, specifically the mixed ionic-electronic conduction property, the impedance response of perovskite solar cells exhibits numerous anomalous features. Herein, we conduct numerical simulations using the drift-diffusion model coupled with ion migration to elucidate how ionic and carrier behavior govern the impedance response. Here we show that, in the Nyquist plot, the low frequency and high frequency semicircular features of direct current voltage-dependent impedance spectroscopy are determined by ion migration and carrier transport, respectively. The low-frequency semicircular feature disappears when ion migration is fully suppressed. As ion mobility or mobile ion concentration increases, both the low-frequency decay region of the real component and low-frequency peak of the imaginary component in frequency-domain impedance plot shift toward higher frequencies. As the bulk or interface carrier recombination rate increases, both the high frequency decay region of the real component and the high frequency peak of the imaginary component shift toward higher frequencies.
Doped quantum magnets, especially spin liquids with fractionalized excitations, have long attracted attention as a promising path towards unconventional superconductivity. We investigate the hole-doped Kitaev-Heisenberg (t–J–K) model on a two-leg ladder geometry using the density-matrix renormalization group (DMRG). We consider the behavior of the antiferromagnetic Kitaev spin-liquid phase as a function of hopping strength t and doping level. This reveals intriguing pairing tendencies only for $$\frac{t}{K}\lesssim 0.65$$, consistent with prior results on three-leg ladders, and firmly supports the emerging picture that the physics of doped Kitaev spin liquids strongly depends on the kinetic energy of the doped holes. Analysis of one- and two-hole doping uncovers close links between spatial profiles of the plaquette operator and the charge density. We also construct a doping-dependent phase diagram for antiferromagnetic Heisenberg interactions and intermediate hopping t = 1. Upon doping, the rung-singlet region develops dominant superconducting correlations. Spin-density wave-like behavior is found in the Kitaev limits, and in the stripy phase. Quantum spin liquids-exotic phases of matter that defy ordering even at zero temperature-are believed to produce unconventional superconducting states upon doping. The authors reveal that pairing in the Kitaev-Heisenberg model strongly depends on the kinetic energy of doped holes, linking a pairing obstruction to deterioration of the spin liquid.
Muonic atoms provide one of the most sensitive probes of nuclear charge distributions and stringent tests of bound-state quantum electrodynamics (QED). Their full potential has been compromised by a long-standing fine-structure anomaly in heavy muonic atoms, hindering the reliable interpretation of precision spectroscopic data. Here we show that the manifestation of this anomaly in 90Zr is resolved by a complete treatment of the relativistic-recoil effect. Fitting ab initio QED calculations to precision measurements of the muonic 90Zr spectrum performed four decades ago yields a root-mean-square charge radius of rrms[90Zr] = 4.2732(7) fm, improves the quality of the fit by a factor of four, and reduces the uncertainty of the extracted radius by a factor of six. The resulting charge radius is consistent with the previous value from muonic spectroscopy but more than 3σ larger than the accepted literature value. Applying the same analysis to 120Sn yields rrms[120Sn] = 4.6518(34) fm, in agreement with the accepted value. These results show that the long-standing fine-structure anomaly originated from an incomplete treatment of QED effects, restoring the predictive power of muonic-atom spectroscopy. Muonic atoms are crucial for probing nuclear charge distributions and testing quantum electrodynamics (QED), yet a fine-structure anomaly in heavy muonic atoms has impeded progress. Here, the authors resolve this anomaly in muonic 90Zr by fully accounting for relativistic-recoil effects, significantly refining the charge radius measurement and enhancing the predictive power of muonic-atom spectroscopy.
Trust is fundamental to cooperation in social and economic interactions, yet its emergence is difficult to explain when individuals occupy asymmetric roles and differ in trustworthiness. How heterogeneous trustee behaviours interact with their spatial organisation to shape population-level trust remains unclear. Here we show that diversity in the amount returned by cooperative trustees can sustain trust even at relatively low levels of trustworthiness. This mechanism is driven by a percolation phenomenon: large clusters of trustworthiness promote trust formation within clusters but impede its outward diffusion, whereas an intermixed distribution facilitates the spread of trust while hindering its local emergence. Consequently, a moderate degree of spatial clustering of trustworthiness maximizes overall trust by balancing connectivity and nucleation. These findings reveal how spatial organisation and behavioural heterogeneity jointly govern trust formation in large populations with asymmetric interactions, and provide a framework for future studies of cooperation in structured social systems. Trust between individuals depends not only on how trustworthy people are on average, but also on how they are arranged in space. Simulations of a trust game on a lattice reveal a percolation trade-off: clustering generous partners helps trust take hold locally, but only intermediate spatial correlation lets it spread widely.
Predicting ordered states in long-range interacting lattice models constitutes a challenging task relevant to condensed matter physics, statistical mechanics, quantum many-body theory, materials science, and computational physics. Energy landscapes often contain many competing states with similar energies, while finite-size simulations can depend sensitively on geometry. Here, we use superconducting qubit quantum annealing devices to determine ground states of Ising models with algebraically decaying competing long-range interactions in the thermodynamic limit from finite system optimizations. This is enabled by a unit-cell-based optimization scheme. We demonstrate the approach on three paradigmatic problems: the calculation of devil’s staircases of magnetization plateaux of the long-range Ising model in a longitudinal field on the triangular lattice, motivated by atomic quantum simulators; the ground state of the same model on the Kagomé lattice without a field, motivated by artificial spin ice metamaterials; and models with additional few-nearest-neighbor interactions relevant for frustrated Ising compounds. Our work provides a realistic application of existing quantum annealing technology across many research areas. Predicting ordered ground-states in long-range interacting lattice models is a challenging question appearing across many domains of many-body physics. Here, the authors use superconducting qubit quantum annealing devices to determine ground states of Ising models with algebraically decaying competing long-range interactions in the thermodynamic limit.
Optical Ising machines provide a hardware approach for combinatorial optimization and physical-system emulation, but their scalability is limited by redundant matrix operations and analog modulation precision. Here we show a programmable optical differential Ising machine that directly evaluates Hamiltonian changes rather than the full Hamiltonian. Numerical simulations from 60 to 2000 spins show that 6-bit analog–digital conversion and 5-bit digital–analog conversion are sufficient to obtain solution quality close to high-precision digital references, with a phase-error tolerance of about 0.3–0.4 rad. Experiments on a dual-channel fiber-optic prototype reproduce phase-transition behavior in representative Ising models and solve MaxCut benchmarks through a multi-spin parallel tempering (PT) routine. The system reaches the best-known cut for the 800-spin G1 instance and 99.66% of the best-known cut for the 2000-spin G22 instance within fewer than 500 PT iterations. These results establish differential optical feedback as a route for scalable Ising-model computation. The authors develop a programmable optical differential Ising machine that directly computes Hamiltonian variations to eliminate redundant full-matrix calculations for Ising combinatorial optimization and physical system emulation. Simulations and fiber-optic prototype experiments verify that the hardware only requires 5-bit DACs and 6-bit ADCs with a phase-error tolerance of 0.3–0.4 rad, and it yields optimal MaxCut solutions for the 800-spin benchmark and 99.66% of the known optimum for the 2000-spin instance via multi-spin parallel tempering.
Continuous-variable quantum key distribution (CVQKD) serves as a vital tool for safeguarding information security. Currently, CVQKD is primarily implemented using a local oscillator. This scheme requires transmitting a high-energy pilot signal as a reference to correct phase drift. However, the pilot signal introduces crosstalk into the quantum signal and complicates digital signal processing. Here we show a pilot-reference-free CVQKD protocol with a local oscillator, where the secret key is encoded on the amplitude of coherent states. The protocol does not require a pilot signal, eliminating pilot-induced crosstalk. We conduct security analysis under collective attacks and simulate its secret key rate. Finally, we perform a proof-of-principle experiment under an optical fiber channel and a free-space channel containing biaxially oriented polypropylene, where the phase of the signal is fully randomized. The protocol remains operable under randomized phase and simplifies physical implementation and digital signal processing, while adapting to optical path fluctuations in complex channels. Continuous variable quantum key distribution with local oscillators typically requires pilot signals to track phase drift. Here, the authors show how to extract keys from signal amplitudes, enabling pilot free operation and proof of principle key generation even when the optical phase is randomised.
The propulsion direction of active particles determines their non-equilibrium behavior and has been shown to depend on the propulsion mechanism and environmental conditions. However, whether and how an anisotropic particle shape can influence the propulsion direction is experimentally unexplored. Here, using 3D micro-printed catalytically active particles, we demonstrate that active discs, tori, and bent rods reverse their direction - moving with their inert side forward at low and with their catalytic side forward at high concentrations of hydrogen peroxide- while spheres and side-propelling straight rods do not. Direction reversal robustly occurs for different material compositions and sizes, only changing the hydrogen peroxide concentration at which reversal appears. We find that the propulsion direction originates from an interplay of pH-dependent zeta potential ratio and particle geometry, and is possibly further enhanced by substrate-induced solute confinement. Our findings demonstrate that particle shape affects the motion of catalytically active particles, important for understanding their interactions and collective effects in out-of-equilibrium model systems. The propulsion direction of active particles determines their non-equilibrium behavior, yet the impact of anisotropic shape remains largely unexplored experimentally. Here, the authors demonstrate that shape plays a crucial role in catalytically active particles as it can cause a reversal of the propulsion direction upon small changes in the fuel concentration.
Temporal higher-order networks, where each hyperlink involving a group of nodes is activated or deactivated over time, effectively represent social interactions. They serve as substrates for the spread of epidemics and information. However, the contribution of each hyperlink to a contagion process, namely, the average number of nodes that are infected via its activation, and the network properties of hyperlinks that influence this contribution, remain unexplored. Here we show, for the Susceptible-Infectious threshold process on temporal higher-order networks derived from human face-to-face interactions, that the contribution of each hyperlink can be quantified by a contagion backbone, whose dependency on the diffusion parameters is demonstrated and supported by theoretical analysis. We design centrality metrics of hyperlinks to estimate hyperlink rankings based on their contributions, revealing that local properties of hyperlinks can effectively identify high-contributing hyperlinks, and explain why different centrality metrics perform better under different process parameters. These insights are crucial for designing effective interventions that mitigate the spread of epidemics or misinformation. A contagion backbone quantifies how much each group interaction in a time-varying higher-order network contributes to spreading. Simple local, time-aware properties, needing no parameter tuning, identify the most influential interactions almost as well as calibrated metrics, and blocking them curbs spreading more than random removal.
Transport and phase separation under geometric confinement play a central role in systems ranging from intracellular organization to porous materials, yet how nonequilibrium activity reshapes collective ordering in constrained environments remains unresolved. Here we demonstrate that confinement alone can qualitatively alter phase separation dynamics, suppressing particle transport, roughening interfaces, and driving an anomalously slow, logarithmic coarsening regime. We further show that introducing activity fundamentally changes this behavior. Self-propulsion restores particle-scale transport, smooths confinement-induced interfacial distortions, and reactivates rapid phase ordering without altering the underlying geometry. By directly relating mean-squared displacement to domain growth kinetics, we show that transport restoration is strongly correlated with, and provides a consistent microscopic explanation for, how activity overcomes geometric barriers and eliminates logarithmic slowdown. Beyond a threshold activity, the system undergoes a transition to fast, power-law coarsening despite persistent confinement. Our results unify activity-enhanced transport in porous media with collective phase separation dynamics and establish a general nonequilibrium principle: activity can reprogram both transport and ordering in geometrically constrained systems. Geometric confinement can trap particles and greatly slow down phase separation in porous materials. Here, the authors show that particle activity restores transport through confined spaces, smooths interfaces, and recovers rapid phase separation even when the confining geometry remains unchanged.
Hydrogen-like systems in ultra-high magnetic fields are of significant interest in interdisciplinary research. Previous studies have focused on the exciton wavefunction shrinkage under magnetic fields down to artificial crystal lattices, while further compression toward the natural crystal lattice remains experimentally challenging. Here we report magneto-absorption measurements on the yellow-exciton series in cuprous oxide using pulsed magnetic fields up to 500 T. The strong low energy absorption features are assigned to the spin Zeeman split 2p0 and 3p0 exciton states, resolving existing ambiguities regarding their quantum number assignments. The high-field data provides an exciton reduced effective mass of 0.415 ± 0.01me. Intriguingly, above 300 T, the energy evolution of the 2p0 state deviates from the hydrogen model, accompanied by a sudden increase in the exciton linewidth. We attribute this to a non-parabolic effect on the valence band due to the band mixing of the valence band top and the split-off band through the large Landau quantization energy. Ultra-high magnetic field absorption measurements in cuprous oxide have been used to investigate the spin Zeeman split 2p0 and 3p0 exciton states. Above 300 T, the energy evolution of the 2p0 state deviates from the hydrogen model which is attributed to valence band mixing induced by the large cyclotron energy.
Abstract Plasma-based particle accelerators, realized by driving plasma waves with either an intense laser pulse or particle beam, offer accelerating fields orders of magnitude higher than conventional accelerators. This enables sources of highly relativistic electron beams with significantly reduced spatial and economic footprints. However, optimizing beam quality and brightness remains the key challenge for demanding applications such as free-electron lasers. The plasma photocathode is a method for generating high-quality beams directly within plasma waves - but hitherto was exclusively realizable using the high-current drive beams from the kilometer-scale conventional accelerator at SLAC. Here we show implementation of a plasma photocathode in a millimeter-scale, purely plasma-based hybrid wakefield accelerator, powered by a 150 TW laser system. Its operation at high plasma densities enables exploitation of micrometer-scale source sizes and rapid acceleration through high-field gradients, which increases the potential output beam quality substantially. This high-density plasma photocathode can be implemented at state-of-the-art laser-plasma accelerators worldwide, and opens the door for high-quality beam production.
Directed cycles are fundamental motifs in natural, social and artificial networks, yet their distinct roles in processing information remain poorly understood, particularly as higher-order structures. Furthermore, it is unclear how the function of a cycle depends on the wider network in which it is embedded. Here we show, using information-theoretic measures, that network size, sparsity and directionality critically shape how directed cycles process information. In networks with no preferred direction, feedforward cycles enable greater information flow, while feedback cycles support stronger information integration. The relative orientation of a feedforward cycle, and the structural incoherence it induces, tunes its capacity to generate higher-order behaviour. Introducing feedback into otherwise feedforward architectures increases the diversity of network activity while modifying the computational behaviour. In a supervised learning task, feedforward networks train faster and tolerate random node ablations, whereas feedback makes them robust to input noise. These findings reveal directed cycles as tunable computational units whose behaviour is set by their structural context. Directed cycles are ubiquitous in natural, social and artificial systems, yet how they shape the information flow in the network remains poorly understood. This study shows a cycle’s direction sets its computational role-feedforward cycles transfer information while feedback cycles integrate it-and that this behaviour can be tuned by the surrounding network.
X-ray imaging is widely employed in clinical medicine, industrial inspection, and various scientific research fields. Unfortunately, high-sensitivity tabletop X-ray two-dimensional (2D) detectors suffer from a fundamental trade-off between pixel count and readout time, which limits their performance for real-time imaging of fast-moving objects and introduces frame loss due to readout dead time. X-ray ghost-imaging (XGI) offers an alternative approach to image an object using only a highly sensitive single-pixel detector. However, a critical limitation of existing XGI methods is the excessive total acquisition time required, rendering it impractical for real applications. Here, we demonstrate a rapid spatial modulation scheme based on random binary patterns encoded onto a fast-spinning mask. Clear X-ray visualization of moving objects is demonstrated with imaging rates up to 200 frames per second (fps) with a resolution of 225 µm. Our method has greatly improved the XGI imaging speed and paves the way for X-ray imaging applications of motion objects, such as the inspection of rotating aero-engines and in vivo medical imaging. Conventional X-ray imaging suffers from a trade-off between spatial resolution and speed due to detector readout limits; to overcome this, we develop a tabletop ghost-imaging system using a fast-spinning binary mask, single-pixel detection and motion compensation. Our system continuously records moving objects at up to 200 frames per second with 225 μm spatial resolution and no readout dead time, enabling dynamic X-ray applications such as rotating machinery inspection and live medical imaging.
Topological Anderson insulators provide a paradigm for disorder-induced topology, in which the underlying system turns from a trivial to a topological phase. It is widely recognized that the latter disappears at large disorder amplitude. Here, and contrary to the general belief, we provide evidence for successive disorder-driven topological transitions ending up in an unconventional topological Anderson phase that remains unexpectedly robust at strong disorder. The corresponding topological invariant increases stepwise with disorder, and then saturates in the strong-disorder regime, without reverting to a trivial Anderson insulator. We experimentally realize these topological Anderson staircase transitions in a one-dimensional topolectrical circuit that emulates single-wall nanotubes. Our work opens the road to topological disordertronics, in which robust topological phases can be tuned by disorder. Using a topolectrical circuit, the authors realise an unconventional disorder-tuned cascade of topological Anderson phase transitions ending up, contrary to the general belief, in an extremely robust topological phase. This work opens the gate for topological disordertronics, where topological channels can be tuned by disorder.
Precision microwave electrometry using Rydberg atoms is limited by many-body interactions at moderate or not too low excitation densities, which may result in distorted probe spectra of electromagnetically induced transparency (EIT) as predicted by mean-field superatom simulations. This nonlinearity fundamentally restricts measurement accuracy and renders the linear response formula of Autler-Townes splitting to microwave (MW) amplitude inapplicable. Here we present a machine learning (ML) framework assisted by principal component analysis (PCA) that can well overcome this difficulty by mapping distorted noisy EIT spectra directly to MW electric fields. Numerical results show that a basic PCA-assisted regression model reduces the minimal detectable MW field by a few folds as compared to both linear response formula and nonlinear least squares fitting in the interaction-dominant regime. It is of more interest that we can employ a four-class ensemble ML scheme to further reduce the minimal detectable MW field by two orders of magnitude to approach its noise-dependent physical limit and hence largely improve the measurement sensitivity. Our work transforms detrimental Rydberg interactions into beneficial spectral features, providing a practical route toward optimal sensitivity for MW quantum sensing in the nonlinear regime. Rydberg atom-based microwave electrometry is hindered by many-body interactions, distorting probe spectra and limiting measurement accuracy. Here, the authors employ a machine learning framework with principal component analysis to map distorted spectra to microwave fields, significantly enhancing sensitivity and transforming interaction challenges into advantageous spectral features for quantum sensing.
Quantum emitters having non-trivial spin polarizations are highly demanded for chiral quantum devices and quantum photonic technologies. Here we investigate the magneto photoluminescence spectroscopy of perovskite quantum dots encapsulated in silica and report an observation of chiral magneto optoelectronic responses. The encapsulation stabilizes the emission and also increases the lattice distortion that alters the exciton properties. As a result, asymmetric behaviors for the degree of circular polarization to the positive and negative magnetic fields are observed from both bright and dark states in the quantum dot. The magneto optoelectronic responses further demonstrate the state mixture between bright and dark excitons, which well explains the chiral features of the quantum dot. The chirality and magnetic field manipulation in the encapsulation environment pave the way to apply them in chiral quantum photonic devices with operational stability. Encapsulated perovskite quantum dots provide quantum emitters with high yield and operational stability. The authors reveal the chiral magneto optical response of these quantum dots and pave the way to applications in chiral quantum photonic devices.
Bacterial transport into micron-scale rigid pores plays an important role in microbial competition and community assembly. As the spatial dimensions of the pore approach the width of a bacterium, steric and hydrodynamic constraints sharply limit passive entry. Here, we demonstrate that motile Escherichia coli overcome these barriers via a two-step facilitated entry mechanism: cells scan flat surfaces for target openings, then pivot into alignment with the aid of flagellar thrust to penetrate them. This process effectively transforms the openings of narrow microfluidic channels into wide, dynamic funnels, dramatically expanding the range of permissible cell orientations and lateral offsets for successful entry. The mechanism increases motile cell influx into extreme confinement by ~30-50 times relative to co-axial entry. The accompanying dimensionality reduction in target search also confers a decisive transport advantage on motile bacteria over non-motile ones. These findings establish reduced-dimensionality target search at the cellular scale, a phenomenon previously thought to be exclusive to molecular enzymes undergoing facilitated diffusion. Bacterial transport into micron-scale pores is crucial for microbial competition and community assembly, yet steric and hydrodynamic constraints limit passive entry. Here, the authors reveal that flagellated bacteria utilize a two-step facilitated-entry mechanism, significantly enhancing motile cell influx into confined spaces and conferring a competitive advantage over non-motile bacteria.
Searching for nontrivial boundary states is of particular interest in the study of higher-order band topology. Here, we report a systematic investigation of supercurrent interference phenomena in niobium-palladium ditelluride-niobium Josephson junctions. The comparative study of edge-touched and -untouched junctions provides compelling evidence for the existence of asymmetric edge supercurrents in palladium ditelluride. Notably, the edge supercurrent exhibits a significantly more rapid increase with decreasing temperatures than the bulk/surface supercurrents, suggesting the nontrivial character of the edge electronic states. These experimental results, along with first-principles calculations, support an asymmetric hinge state model, thereby establishing palladium ditelluride as a quantum material hosting both higher-order topology and superconductivity. The search for non-trivial boundary states is central to the study of higher-order band topology. Transport studies in superconducting Josephson junctions fabricated from PdTe2 reveal the presence of asymmetric edge states, establishing PdTe2 as a quantum material with both higher order topology and superconductivity.
