A mixed-species geometric phase gate has been proposed for implementing quantum logic spectroscopy on trapped ions, which combines probe and information transfer from the spectroscopy to the logic ion in a single pulse. We experimentally realize this method, show how it can be applied as a technique for identifying transitions in currently intractable atoms or molecules, demonstrate its reduced temperature sensitivity, and observe quantum-enhanced frequency sensitivity when it is applied to multi-ion chains. Potential applications include improved readout of trapped-ion clocks and simplified error syndrome measurements for quantum error correction.

Two-dimensional semiconductors provide an ideal platform for exploration of linear exciton and polariton physics, primarily due to large exciton binding energy and strong light-matter coupling. These features, however, generically imply reduced exciton-exciton interactions, hindering the realization of active optical devices such as lasers or parametric oscillators. Here, we show that electrical injection of itinerant electrons into monolayer molybdenum diselenide allows us to overcome this limitation: dynamical screening of exciton-polaritons by electrons leads to the formation of new quasiparticles termed polaron-polaritons that exhibit unexpectedly strong interactions as well as optical amplification by Bose-enhanced polaron-electron scattering. To measure the nonlinear optical response, we carry out time-resolved pump-probe measurements and observe polaron-polariton interaction enhancement by a factor of 50 ($0.5\mu \mathrm{eV}\mu {\mathrm{m}}^2$) as compared to exciton-polaritons. Concurrently, we measure a spectrally integrated transmission gain of the probe field of $\gtrsim 2$ stemming from stimulated scattering of polaron-polaritons. We show theoretically that the nonequilibrium nature of optically excited quasiparticles favors a previously unexplored interaction mechanism stemming from a phase-space filling in the screening cloud, which provides an accurate explanation of the strong repulsive interactions observed experimentally. Our findings show that itinerant electron-exciton interactions provide an invaluable tool for electronic manipulation of optical properties, demonstrate a new mechanism for dramatically enhancing polariton-polariton interactions, and pave the way for realization of nonequilibrium polariton condensates.

In contrast to magnetic order formed by electrons’ dipolar moments, ordering phenomena associated with higher-order multipoles (quadrupoles, octupoles, etc.) are more difficult to characterize because of the limited choice of experimental probes that can distinguish different multipolar moments. The heavy-fermion compound ${\mathrm{CeB}}_6$ and its La-diluted alloys are among the best-studied realizations of the long-range-ordered multipolar phases, often referred to as “hidden order.” Previously, the hidden order in phase II was identified as primary antiferroquadrupolar and field-induced octupolar order. Here, we present a combined experimental and theoretical investigation of collective excitations in phase II of ${\mathrm{CeB}}_6$. Inelastic neutron scattering (INS) in fields up to 16.5 T reveals a new high-energy mode above 14 T in addition to the low-energy magnetic excitations. The experimental dependence of their energy on the magnitude and angle of the applied magnetic field is compared to the results of a multipolar interaction model. The magnetic excitation spectrum in a rotating field is calculated within a localized approach using the pseudospin representation for the ${\mathrm{\Gamma }}_8$ states. We show that the rotating-field technique at fixed momentum can complement conventional INS measurements of the dispersion at a constant field and holds great promise for identifying the symmetry of multipolar order parameters and the details of intermultipolar interactions that stabilize hidden-order phases.

When monitoring the dynamics of stochastic systems, such as interacting particles agitated by thermal noise, disentangling deterministic forces from Brownian motion is challenging. Indeed, we show that there is an information-theoretic bound, the capacity of the system when viewed as a communication channel, that limits the rate at which information about the force field can be extracted from a Brownian trajectory. This capacity provides an upper bound to the system’s entropy production rate and quantifies the rate at which the trajectory becomes distinguishable from pure Brownian motion. We propose a practical and principled method, stochastic force inference, that uses this information to approximate force fields and spatially variable diffusion coefficients. It is data efficient, including in high dimensions, robust to experimental noise, and provides a self-consistent estimate of the inference error. In addition to forces, this technique readily permits the evaluation of out-of-equilibrium currents and the corresponding entropy production with a limited amount of data.

Thin elastic solids are easily deformed into a myriad of three-dimensional shapes, which may contain sharp localized structures as in a crumpled candy wrapper or have smooth and diffuse features like the undulating edge of a flower. Anticipating and controlling these morphologies is crucial to a variety of applications involving textiles, synthetic skins, and inflatable structures. Here, we show that a “wrinkle-to-crumple” transition, previously observed in specific settings, is a ubiquitous response for confined sheets. This unified picture is borne out of a suite of model experiments on polymer films confined to liquid interfaces with spherical, hyperbolic, and cylindrical geometries, which are complemented by experiments on macroscopic membranes inflated with gas. We use measurements across this wide range of geometries, boundary conditions, and length scales to quantify several robust morphological features of the crumpled phase, and we build an empirical phase diagram for crumple formation that disentangles the competing effects of curvature and compression. Our results suggest that crumples are a generic microstructure that emerge at large curvatures due to a competition of elastic and substrate energies.

Both helium and ammonia are main components of icy giant planets. While ammonia is very reactive, helium is the most inert element in the universe. It is of great interest whether ammonia and helium can react with each other under planetary conditions, and if so, what kinds of structures and states of matter can form. Here, using crystal structure prediction methods and first-principles calculations, we report three new stable stoichiometries and eight new stable phases of $\mathrm{He}\text{−}{\mathrm{NH}}_3$ compounds under pressures up to 500 GPa. These structures may exhibit perovskitelike structures for ${\mathrm{HeNH}}_3$ and ${\mathrm{He}}_2{\mathrm{NH}}_3$, and a host-guest crystal structure for $\mathrm{He}{({\mathrm{NH}}_3)}_2$. Superionic states are found in all these $\mathrm{He}\text{−}{\mathrm{NH}}_3$ compounds under high pressures and temperatures in which the hydrogen atoms are diffusive while the nitrogen and helium atoms remain fixed. Such dynamical behavior in helium ammonia compounds is quite different from that in helium water compounds, where weakly interacting helium is more diffusive than stronger bound hydrogen. The low-density host-guest phase of space group $I4cm$ is found to be stable at very low pressures (about 3 GPa) and it enters into a plastic state, characterized by freely rotating ammonia molecules. The present results suggest that plastic or superionic helium ammonia compounds may exist under planetary conditions, and helium contributes crucially to the exotic physics and chemistry observed under extreme conditions.

Quantum error correction is of crucial importance for fault-tolerant quantum computers. As an essential step toward the implementation of quantum error-correcting codes, quantum nondemolition measurements are needed to efficiently detect the state of a logical qubit without destroying it. Here we implement quantum nondemolition measurements in a Si/SiGe two-qubit system, with one qubit serving as the logical qubit and the other serving as the ancilla. Making use of a two-qubit controlled-rotation gate, the state of the logical qubit is mapped onto the ancilla, followed by a destructive readout of the ancilla. Repeating this procedure enhances the logical readout fidelity from $75.5\pm 0.3\%$ to $94.5\pm 0.2\%$ after 15 ancilla readouts. In addition, we compare the conventional thresholding method with an improved signal processing method called soft decoding that makes use of analog information in the readout signal to better estimate the state of the logical qubit. We demonstrate that soft decoding leads to a significant reduction in the required number of repetitions when the readout errors become limited by Gaussian noise, for instance, in the case of readouts with a low signal-to-noise ratio. These results pave the way for the implementation of quantum error correction with spin qubits in silicon.

Inspired by recent experiments on graphene, we examine the nondissipative viscoelastic response of anisotropic two-dimensional quantum systems. We pay particular attention to electron fluids with point group symmetries and those with discrete translational symmetry. We start by extending the Kubo formalism for viscosity to systems with internal degrees of freedom and discrete translational symmetry, highlighting the importance of properly considering the role of internal angular momentum. We analyze the Hall components of the viscoelastic response tensor in systems with discrete point group symmetry, focusing on the hydrodynamic implications of the resulting forces. We show that though there are generally six Hall viscosities, there are only three independent contributions to the viscous force density in the bulk. To compute these coefficients, we develop a framework to consistently write down the long-wavelength stress tensor and viscosity for multicomponent lattice systems. We apply our formalism to lattice and continuum models, including a lattice Chern insulator and anisotropic superfluid.

Unraveling bacterial strategies for spatial exploration is crucial for understanding the complexity in the organization of life. Bacterial motility determines the spatiotemporal structure of microbial and controls infection spreading and the microbiota organization in guts or in soils. Most theoretical approaches for modeling bacterial transport rely on their run-and-tumble motion. For Escherichia coli, the run-time distribution is reported to follow a Poisson process with a single characteristic time related to the rotational switching of the flagellar motors. However, direct measurements on flagellar motors show heavy-tailed distributions of rotation times stemming from the intrinsic noise in the chemotactic mechanism. Currently, there is no direct experimental evidence that the stochasticity in the chemotactic machinery affects the macroscopic motility of bacteria. In stark contrast with the accepted vision of run and tumble, here we report a large behavioral variability of wild-type E. coli, revealed in their three-dimensional trajectories. At short observation times, a large distribution of run times is measured on a population and attributed to the slow fluctuations of a signaling protein triggering the flagellar motor reversal. Over long times, individual bacteria undergo significant changes in motility. We demonstrate that such a large distribution of run times introduces measurement biases in most practical situations. Our results reconcile the notorious conundrum between run-time observations and motor-switching statistics. We finally propose that statistical modeling of transport properties, currently undertaken in the emerging framework of active matter studies, should be reconsidered under the scope of this large variability of motility features.

Half a century after its discovery, the Josephson junction has become the most important nonlinear quantum electronic component at our disposal. It has helped reshape the International System of Units around quantum effects and is used in scores of quantum devices. By itself, the use of Josephson junctions in volt metrology seems to imply an exquisite understanding of the component in every aspect. Yet, surprisingly, there have been long-standing subtle issues regarding the modeling of the interaction of a junction with its electromagnetic environment. Here, we find that a Josephson junction connected to a resistor does not become insulating beyond a given value of the resistance due to a dissipative quantum phase transition, as is commonly believed. Our work clarifies how this key quantum component behaves in the presence of a dissipative environment and provides a comprehensive and consistent picture, notably regarding the treatment of its phase.

Unraveling the structure and function of the brain requires a detailed knowledge about the neuronal connections, i.e., the spatial architecture of the nerve fibers. One of the most powerful histological methods to reconstruct the three-dimensional nerve fiber pathways is 3D-polarized light imaging (3D-PLI). The technique measures the birefringence of histological brain sections and derives the spatial fiber orientations of whole human brain sections with micrometer resolution. However, the technique yields only a single fiber orientation for each measured tissue voxel even if it is composed of fibers with different orientations, so that in-plane crossing fibers are misinterpreted as out-of-plane fibers. When generating a detailed model of the three-dimensional nerve fiber architecture in the brain, a correct detection and interpretation of nerve fiber crossings is crucial. Here, we show how light scattering in transmission microscopy measurements can be leveraged to identify nerve fiber crossings in 3D-PLI data and demonstrate that measurements of the scattering pattern can resolve the substructure of brain tissue like the crossing angles of the nerve fibers. For this purpose, we develop a simulation framework that permits the study of transmission microscopy measurements—in particular, light scattering—on large-scale complex fiber structures like brain tissue, using finite-difference time-domain (FDTD) simulations and high-performance computing. The simulations are used not only to model and explain experimental observations, but also to develop new analysis methods and measurement techniques. We demonstrate in various experimental studies on brain sections from different species (rodent, monkey, and human) and in FDTD simulations that the polarization-independent transmitted light intensity (transmittance) decreases significantly (by more than 50%) with an increasing out-of-plane angle of the nerve fibers and that it is mostly independent of the in-plane crossing angle. Hence, the transmittance can be used to distinguish regions with low fiber density and in-plane crossing fibers from regions with out-of-plane fibers, solving a major problem in 3D-PLI and allowing for a much better reconstruction of the complex nerve fiber architecture in the brain. Finally, we show that light scattering (oblique illumination) in the visible spectrum reveals the underlying structure of brain tissue like the crossing angle of the nerve fibers with micrometer resolution, enabling an even more detailed reconstruction of nerve fiber crossings in the brain and opening up new fields of research.

Molecular optogenetic switch systems are extensively employed as a powerful tool to spatially and temporally modulate a variety of signal transduction processes in cells. However, the applications of such systems in mechanotransduction processes where the mechanosensing proteins are subject to mechanical forces of several piconewtons are poorly explored. In order to apply molecular optogenetic switch systems to mechanobiological studies, it is crucial to understand their mechanical stabilities which have yet to be quantified. In this work, we quantify a frequently used molecular optogenetic switch, iLID-nano, which is an improved light-induced dimerization between LOV2-SsrA and SspB. Our results show that the iLID-nano system can withstand forces up to 10 pN for seconds to tens of seconds that decrease as the force increases. The mechanical stability of the system suggests that it can be employed to modulate mechanotransduction processes that involve similar force ranges. We demonstrate the use of this system to control talin-mediated cell spreading and migration. Together, we establish the physical basis for utilizing the iLID-nano system in the direct control of intramolecular force transmission in cells during mechanotransduction processes.

Exploring new parameter regimes to realize and control novel phases of matter has been a main theme in modern condensed matter physics research. The recent discovery of two-dimensional (2D) magnetism in nearly freestanding monolayer atomic crystals has already led to observations of a number of novel magnetic phenomena absent in bulk counterparts. Such intricate interplays between magnetism and crystalline structures provide ample opportunities for exploring quantum phase transitions in this new 2D parameter regime. Here, using magnetic field- and temperature-dependent circularly polarized Raman spectroscopy of phonons and magnons, we map out the phase diagram of chromium triiodide (${\mathrm{CrI}}_3$) that has been known to be a layered antiferromagnet (AFM) in its 2D films and a ferromagnet (FM) in its three-dimensional (3D) bulk. However, we reveal a novel mixed state of layered AFM and FM in 3D ${\mathrm{CrI}}_3$ bulk crystals where the layered AFM survives in the surface layers, and the FM appears in deeper bulk layers. We then show that the surface-layered AFM transits into the FM at a critical magnetic field of 2 T, similar to what was found in the few-layer case. Interestingly, concurrent with this magnetic phase transition, we discover a first-order structural phase transition that alters the crystallographic point group from $C_{3i}$ (rhombohedral) to $C_{2h}$ (monoclinic). Our result not only unveils the complex single-magnon behavior in 3D ${\mathrm{CrI}}_3$, but it also settles the puzzle of how ${\mathrm{CrI}}_3$ transits from a bulk FM to a thin-layered AFM semiconductor, despite recent efforts in understanding the origin of layered AFM in ${\mathrm{CrI}}_3$ thin layers, and reveals the intimate relationship between the layered AFM-to-FM and the crystalline rhombohedral-to-monoclinic phase transitions. These findings further open opportunities for future 2D magnet-based magnetomechanical devices.

In filtering, each output is produced by a certain number of different inputs. We explore the statistics of this degeneracy in an explicitly treatable filtering problem in which filtering performs the maximal compression of relevant information contained in inputs (arrays of zeros and ones). This problem serves as a reference model for the statistics of filtering and related sampling problems. The filter patterns in this problem conveniently allow a microscopic, combinatorial consideration. This allows us to find the statistics of outputs, namely the exact distribution of output degeneracies, for arbitrary input sizes. We observe that the resulting degeneracy distribution of outputs decays as $e^{-c{\log }^{\alpha }d}$ with degeneracy $d$, where $c$ is a constant and exponent $\alpha >1$, i.e., faster than a power law. Importantly, its form essentially depends on the size of the input dataset, appearing to be closer to a power-law dependence for small dataset sizes than for large ones. We demonstrate that for sufficiently small input dataset sizes typical for empirical studies, this distribution could be easily perceived as a power law. We extend our results to filter patterns of various sizes and demonstrate that the shortest filter pattern provides the maximum informative representations of the inputs.

We study the nonlinear dynamics of a surprising phenomenon arising in large networks of excitable elements in response to noise: while at low noise, solutions remain in the vicinity of the resting state and large-noise solutions show asynchronous activity, the network displays orderly, perfectly synchronized periodic responses at intermediate levels of noise. This noise-induced synchronization, distinct from classical stochastic resonance, is fundamentally collective in nature. Indeed, we show that, for noise and coupling within specific ranges, an asymmetry in the transition rates between a resting and an excited regime progressively builds up, leading to an increase in the fraction of excited neurons eventually triggering a chain reaction associated with a macroscopic synchronized excursion and a collective return to rest where this process starts afresh, thus yielding the observed periodic synchronized oscillations. We further uncover a novel antiresonance phenomenon in this regime: noise-induced synchronized oscillations disappear when the system is driven by periodic stimulation with frequency within a specific range (high relative to the spontaneous activity). In that antiresonance regime, the system is optimal for measures of information transmission. This observation provides a new hypothesis accounting for the efficiency of high-frequency stimulation therapies, known as deep brain stimulation, in Parkinson’s disease, a neurodegenerative disease characterized by an increased synchronization of brain motor circuits. We further discuss the universality of these phenomena in the class of stochastic networks of excitable elements with specific coupling and illustrate this universality by analyzing various classical models of neuronal networks. Altogether, these results uncover some universal mechanisms supporting a regularizing impact of noise in excitable systems, reveal a novel antiresonance phenomenon in these systems, and propose a new hypothesis for the efficiency of high-frequency stimulation in Parkinson’s disease.

Cellular mechanics drives epithelial morphogenesis, the process wherein cells collectively rearrange to produce tissue-scale deformations that determine organismal shape. However, quantitative understanding of tissue mechanics is impaired by the difficulty of direct measurement of stress in vivo. This difficulty has spurred the development of image-based inference algorithms that estimate stress from snapshots of epithelial geometry. Such methods are challenged by sensitivity to measurement error and thus require accurate geometric segmentation for practical use. We overcome this difficulty by introducing a novel approach—the variational method of stress inference (VMSI)—which exploits the fundamental duality between stress and geometry at equilibrium of discrete mechanical networks that model confluent cellular layers. We approximate the apical geometry of an epithelial tissue by a 2D tiling with circular arc polygons in which arcs represent intercellular interfaces defined by the balance of local line tension and pressure differentials between adjacent cells. The mechanical equilibrium of such networks imposes extensive local constraints on circular arc polygon geometry. These constraints provide the foundation of VMSI which, starting with images of epithelial monolayers, simultaneously approximates both tissue geometry and internal forces, subject to the constraint of equilibrium. We find VMSI to be more robust than previous methods. Specifically, the VMSI performance is validated by the comparison of the predicted cellular and mesoscopic scale stress with the measured myosin II patterns during early Drosophila embryogenesis. VMSI prediction of a mesoscopic stress tensor correlates at the 80% level with the measured myosin distribution and reveals that most of the myosin activity in that case is involved in a static internal force balance within the epithelial layer. In addition to insight into cell mechanics, this study provides a practical method for nondestructive estimation of stress in live epithelial tissue.

The operation of numerous physical systems and devices relies on concentrating their state space around a few carefully engineered eigenstates governing the dynamics. In photonics, these discrete degrees of freedom are typically the resonant modes of a structure. However, whenever a light source drives the structure, a continuum of additional nonmodal states generates its forced response, undermining its underlying physics and hindering control through discretization. Dealing with this nonmodal continuum poses a challenge to the design of nanophotonic systems aiming to combine compact sources and nanostructures into unified functional platforms. Here, we present a route to control forced nanostructures by engineering a discrete set of nonmodal degrees of freedom, originating from joint nanostructure-source antiresonances. We experimentally demonstrate that the forced response of ultrathin gold films is shaped by pairs of resonant-antiresonant plasmons, exhibiting joint creation and annihilation in momentum-energy space. Tuning their excitation, we show that 10 nm films can appear “black”: exhibiting strong spectroangular wideband absorption.

We consider cumulative merging percolation (CMP), a long-range percolation process describing the iterative merging of clusters in networks, depending on their mass and mutual distance. For a specific class of CMP processes, which represents a generalization of degree-ordered percolation, we derive a scaling solution on uncorrelated complex networks, unveiling the existence of diverse mechanisms leading to the formation of a percolating cluster. The scaling solution accurately reproduces universal properties of the transition. This finding is used to infer the critical properties of the susceptible-infected-susceptible model for epidemics in infinite and finite power-law distributed networks. Here, discrepancies between analytical approaches and numerical results regarding the finite-size scaling of the epidemic threshold are a crucial open issue in the literature. We find that the scaling exponent assumes a nontrivial value during a long preasymptotic regime. We calculate this value, finding good agreement with numerical evidence. We also show that the crossover to the true asymptotic regime occurs for sizes much beyond currently feasible simulations. Our findings allow us to rationalize and reconcile all previously published results (both analytical and numerical), thus ending a long-standing debate.

We employ machine learning techniques to provide accurate variational wave functions for matrix quantum mechanics, with multiple bosonic and fermionic matrices. The variational quantum Monte Carlo method is implemented with deep generative flows to search for gauge-invariant low-energy states. The ground state (and also long-lived metastable states) of an $\mathrm{SU}(N)$ matrix quantum mechanics with three bosonic matrices, and also its supersymmetric “mini-BMN” extension, are studied as a function of coupling and $N$. Known semiclassical fuzzy sphere states are recovered, and the collapse of these geometries in more strongly quantum regimes is probed using the variational wave function. We then describe a factorization of the quantum mechanical Hilbert space that corresponds to a spatial partition of the emergent geometry. Under this partition, the fuzzy sphere states show a boundary-law entanglement entropy in the large $N$ limit.