High-fidelity Measurements Research Articles

Virtual reality video game improves high-fidelity memory in older adults

Therapeutic interventions have not yet been shown to demonstrate restorative effects for declining long-term memory (LTM) that affects many healthy older adults. We developed a virtual reality (VR) spatial wayfinding game (Labyrinth-VR) as a cognitive intervention with the hypothesis that it could improve detailed, high-fidelity LTM capability. Spatial navigation tasks have been used as a means to achieve environmental enrichment via exposure to and learning about novel and complex information. Engagement has been shown to enhance learning and has been linked to the vitality of the LTM system in the brain. In the current study, 48 older adults (mean age 68.7 ± 6.4 years) with average cognitive abilities for their age were randomly assigned to 12 h of computer game play over four weeks in either the Labyrinth-VR or placebo control game arms. Promptly before and after each participant’s treatment regimen, high-fidelity LTM outcome measures were tested to assess mnemonic discrimination and other memory measures. The results showed a post-treatment gain in high-fidelity LTM capability for the Labyrinth-VR arm, relative to placebo, which reached the levels attained by younger adults in another experiment. This novel finding demonstrates generalization of benefits from the VR wayfinding game to important, and untrained, LTM capabilities. These cognitive results are discussed in the light of relevant research for hippocampal-dependent memory functions.

Optimal readout of superconducting qubits exploiting high-level states

High-fidelity initialization, manipulation, and measurement of qubits are important in quantum computing. For the Google’s Sycamore processor, the gate fidelity of single- and two-qubit logic operations has improved to>99.6%, whereas single-shot measurement fidelity remains at the level of 97%, which severely limits the application of the superconducting approach to large-scale quantum computing. The current measurement scheme relies on the dispersive interaction between the qubit and the readout resonator, which was proposed back in 2004. However, the measurement fidelity is limited by the trade-off between the state separation and relaxation time of the two-level system. Recently, an exciting phenomenon was observed experimentally, wherein the separation-decay limit could be alleviated by exploiting the cascade decay nature of the higher levels; however, the mechanism and effectiveness of this phenomenon are still unclear. Herein, we present a theoretical tool to extract different types of errors in high-level states encoding dispersive measurement. For the realistic parameters of Google’s Sycamore processor, the use of state |2〉 is sufficient to suppress 92% of the decay readout error on average, where the total readout error is dominated by the background thermal excitation. We also show counter-intuitively that, the assistance of high-level states is effective in the measurement of logic 0, where there is no decay process.

CoIL: Coordinate-Based Internal Learning for Tomographic Imaging

We propose Coordinate-based Internal Learning (CoIL) as a new deep-learning (DL) methodology for continuous representation of measurements. Unlike traditional DL methods that learn a mapping from the measurements to the desired image, CoIL trains a multilayer perceptron (MLP) to encode the complete measurement field by mapping the coordinates of the measurements to their responses. CoIL is a self-supervised method that requires no training examples besides the measurements of the test object itself. Once the MLP is trained, CoIL generates new measurements that can be used within most image reconstruction methods. We validate CoIL on sparse-view computed tomography using several widely-used reconstruction methods, including purely model-based methods and those based on DL. Our results demonstrate the ability of CoIL to consistently improve the performance of all the considered methods by providing high-fidelity measurement fields.

Ownership Patterns Drive Multi-Scale Forest Structure Patterns across a Forested Region in Southern Coastal Oregon, USA

Research Highlights: We used airborne lidar to assess the multi-scalar patterns of forest structure across a large (471,000 hectare), multi-owner landscape of the Oregon Coast Range, USA. The results of this study can be used in the development and evaluation of conservation strategies focused on forest management. Background and Objectives: Human management practices reflect policy and economic decisions and shape forest structure through direct management and modification of disturbance regimes. Previous studies have found that land ownership affects forest cover, patch dynamics, structure, and ecosystem function and services. However, prior assessments of forest structure across landscapes and ownerships have been limited by a lack of high-fidelity forest structure measurements across a large spatial extent. We addressed three research questions: (1) What distinct classes of forest structure exist across our study area? (2) How does the distribution and pattern of forest structure vary among types of owners at scales of patches, ownership types, and subregion, and is this independent of property size? and (3) What implications do the fine and sub-regional scale patterns have for landscape configuration goals under recent updates to the Northwest Forest Plan? Materials and Methods: We examined forest structure patterns by identifying six statistically distinct classes of forest structure and then examining their distribution across and within ownership types. We used these structure classes to examine their area within each ownership class, mean patch size, and intermixing at multiple scales. Results and Conclusion: We found that the six different forest structure classes in the study area can be interpreted as two assemblages: production-style forests, principally on private lands, and structurally complex forests, principally on public lands. We found that land ownership objectives resulted in distinct landscape patterns of forest structure as measured by mean structure class patch size and intermixing of different structure class patches. Finally, we found that forest structure differed between public and private lands but differed comparatively little among ownership types within those two broad categories.

Turbulence structure of neutral and negatively buoyant jets

Abstract

Echocardiographic diagnosis of left ventricular diastolic dysfunction: Impact of coronary artery disease.

In 2016, the American Society of Echocardiography (ASE) released guidelines for identifying left ventricular (LV) diastolic dysfunction (DD), but its ability to detect early hemodynamic abnormalities is not well established, especially in the setting of subclinical coronary artery disease (CAD). We hypothesize that the accuracy of ASE categorization of early LVDD is affected by knowledge of whether CAD history is present. We studied 34 patients (age 62±7years) with NYHA class I to II symptoms and with transthoracic echocardiography without findings suggesting myocardial disease (all with preserved LV ejection fraction), who underwent cardiac catheterization with high-fidelity LV pressure measurement. Echocardiographic images were evaluated for LVDD using ASE algorithm without and with knowledge of CAD history and angiography findings. CAD was considered as having DD for the algorithm. CAD was identified in 22 patients at catheterization (65%). Using ASE guidelines without including history of CAD or angiographic results, 29 patients were DD-, 3 were DD+ (all grade II), and 2 were indeterminate. Inclusion of CAD history recategorized 59% (n=20) patients to DD+ (all grade I) from DD- (P<.0001). Nineteen of the recategorized patients (95%) had increased isovolumetric relaxation time (IVRT). The addition of echocardiographic IVRT improved discrimination between DD- and DD+, when the presence of CAD is unknown. 2016-ASE algorithm reasonably accurately identifies early LVDD at rest as reflected by LV catheterization when CAD is disclosed, but without knowledge of the presence of CAD, it underdiagnoses DD+grade I. The addition of IVRT may improve early LVDD diagnostics.

Development of wireless smart sensor network for vibration-based structural health monitoring of civil structures

This paper introduces a wireless smart sensor network to meet the requirements for measuring low-amplitude ambient and sudden event monitoring of civil structures. The developed sensor platform consists of a high-sensitive sensing component for high-fidelity vibration measurements and a reliable event detection switch through a trigger accelerometer to detect and record sudden events. Particularly, the hardware of the sensor nodes was designed to provide high resolution and sensitivity, low noise, low power consumption, and the ability to measure the structural vibration in ultralow-power in long term to record sudden events. Also, the software architecture of the smart nodes was implemented to provide accurate time synchronisation and reliable event-triggered sensing with high resolution. To test the sensor performance, a series of experimental and field tests was conducted on a bridge model using shake table and an in-service highway viaduct. Also, the dynamic performance of the viaduct was assessed using its modal parameters. The results showed that the wireless smart sensor node is able to detect and record sudden events and provide high-fidelity structural responses and synchronised wireless network for structural condition monitoring. In addition, it was observed that the overall dynamic condition of the viaduct has no significant changes.

MDM-PCCT: Multiple Dynamic Modulations for High-Performance Spectral PCCT Imaging

Photon counting computed tomography (PCCT) has the ability to identify individual photons, resulting in quantitative material identification. Meanwhile, several technical challenges still exist in current PCCT imaging systems, including increased noise and suboptimal bin selection. These nonideal effects can substantially degrade the reconstruction performance and material estimation accuracy. To address these issues, in this work, we present a novel system for high-performance spectral PCCT imaging, which is a combination of multiple dynamic modulations, interpolation-based measurements processing strategy and advanced reconstruction method. For simplicity, this new PCCT imaging system is referred to as "MDM-PCCT". Specifically, the multiple dynamic modulations consist of dynamic kVp modulation, dynamic spectrum modulation and dynamic energy threshold modulation. In the dynamic kVp modulation, three kVp values, i.e., 80, 110 and 140, are included, and the tube voltage waveform follows a sinusoidal curve which is more practical than the rectangular curve in the fast kV switching mode. In the dynamic spectrum modulation, the X-ray spectra are processed by selective spatial-spectral filters to balance the X-ray fluxes and increase the spectral separation. In the dynamic energy threshold modulation, the energy threshold is adaptively changed to determine the optimal bin selection. Furthermore, we propose an energy threshold determination method and interpolation-based measurements processing strategy to address the issue of non-uniform and sparse-view PCCT measurements, respectively. In addition, by considering the intrinsic characteristics of the MDM-PCCT images, we utilize an enhanced total variation regularized model for images reconstruction. Finally, numerical and preclinical studies demonstrate that the presented MDM-PCCT imaging system is capable of yielding uniform and high-fidelity PCCT measurements with noise consistency, and the presented reconstruction method further improves the image quality and material decomposition accuracy.

Speckle correlation as a monitor of X-ray free-electron laser induced crystal lattice deformation

X-ray free-electron lasers (X-FELs) present new opportunities to study ultrafast lattice dynamics in complex materials. While the unprecedented source brilliance enables high fidelity measurement of structural dynamics, it also raises experimental challenges related to the understanding and control of beam-induced irreversible structural changes in samples that can ultimately impact the interpretation of experimental results. This is also important for designing reliable high performance X-ray optical components. In this work, X-FEL beam-induced lattice alterations are investigated by measuring the shot-to-shot evolution of near-Bragg coherent scattering from a single crystalline germanium sample. It is shown that X-ray photon correlation analysis of sequential speckle patterns measurements can be used to monitor the nature and extent of lattice rearrangements. Abrupt, irreversible changes are observed following intermittent high-fluence monochromatic X-ray pulses, thus revealing the existence of a threshold response to X-FEL pulse intensity.

Bayesian Information-Theoretic Calibration of Radiotherapy Sensitivity Parameters for Informing Effective Scanning Protocols in Cancer

With new advancements in technology, it is now possible to collect data for a variety of different metrics describing tumor growth, including tumor volume, composition, and vascularity, among others. For any proposed model of tumor growth and treatment, we observe large variability among individual patients’ parameter values, particularly those relating to treatment response; thus, exploiting the use of these various metrics for model calibration can be helpful to infer such patient-specific parameters both accurately and early, so that treatment protocols can be adjusted mid-course for maximum efficacy. However, taking measurements can be costly and invasive, limiting clinicians to a sparse collection schedule. As such, the determination of optimal times and metrics for which to collect data in order to best inform proper treatment protocols could be of great assistance to clinicians. In this investigation, we employ a Bayesian information-theoretic calibration protocol for experimental design in order to identify the optimal times at which to collect data for informing treatment parameters. Within this procedure, data collection times are chosen sequentially to maximize the reduction in parameter uncertainty with each added measurement, ensuring that a budget of n high-fidelity experimental measurements results in maximum information gain about the low-fidelity model parameter values. In addition to investigating the optimal temporal pattern for data collection, we also develop a framework for deciding which metrics should be utilized at each data collection point. We illustrate this framework with a variety of toy examples, each utilizing a radiotherapy treatment regimen. For each scenario, we analyze the dependence of the predictive power of the low-fidelity model upon the measurement budget.

Joint Routing and Sketch Configuration in Software-Defined Networking

Traffic measurement is very important for various applications, such as traffic engineering and attack detection, in software-defined networks. Due to limited resources ( e.g. , computing, memory) on SDN switches, sketches have been widely used for efficient traffic measurement. Meanwhile, multiple independent sketches are required for different application requirements, such as heavy hitter detection and flow size distribution estimation. To avoid the measurement redundancy, we configure which sketch(es) will measure flow on each switch. If traffic measurement is performed on each switch without considering network routing, due to traffic dynamics, it will cause massive measurement overhead on a switch, which may exceed the switch’s computing capacity. In this paper, we study the joint optimization of flow routing and sketch configuration in SDNs. We formulate this problem as an integer linear programming and prove its NP-hardness. To solve this problem, we propose a rounding-based offline algorithm and a primal-dual-based online algorithm for different application scenarios. To deal with bursty traffic, we also design an adaptive sampling mechanism for high-fidelity traffic measurement. We formally analyze the approximation performance or competitive ratio of the proposed algorithms. The extensive simulation results show the high efficiency of our proposed algorithms. For example, the online algorithm can improve the network throughput 30% compared with the state-of-the-art.

High-fidelity flame-front wrinkling measurements derived from fractal analysis of turbulent premixed flames with large Reynolds numbers

This work assesses the level of wrinkling that premixed flames exhibit when subjected to turbulence levels in excess of ten times that of previous experimental studies of this nature. This assessment was facilitated by performing fractal analysis of isocontours extracted from simultaneously acquired planar Rayleigh scattering (PRS) and planar laser-induced fluorescence (PLIF) images of OH. Images were collected from 21 separate flames with turbulent Karlovitz (KaT,P) and Reynolds numbers (ReT,0) as large as 210 and 99,000, respectively. Values of the inner-cutoff scale (εi) and fractal dimension (FD) were extracted from two separate isocontours associated with each imaging technique. Results indicate that values of εi normalized by the laminar flame thickness (δL,P) follow the scaling: εi/δL,P∼KaT,P−0.22, which holds for results from prior studies but deviates from those derived from theoretical principles. Cases with main-flame equivalence ratios (ϕm) substantially less than that of their pilot flame (ϕp) did not follow the aforementioned scaling; however, adjusting δL,P and SL values such that they corresponded to the average of ϕm and ϕp was observed to reconcile this discrepancy. Yet, cases with ϕm ∼ ϕp did not deviate much from the scaling above. The influence of isocontour choice and targeted scalar on values of εi and FD was assessed and found to be much more significant for the former than the latter. Additionally, εi and FD exhibit a modest increase with axial distance from the flame base, corroborating prior results.

Influence of Capacitive Coupling on High-Fidelity Non-Contact ECG Measurement

Non-contact electrocardiogram (ECG) measurement is an advanced sensing technique that uses capacitive electrodes to detect cardiac signals through non-conductive fabrics. However, the capacitive coupling is a significant factor that affects signal-to-noise ratio (SNR) of non-contact ECG, including skin-electrode active area, material, and thickness of the non-conductive fabric. This study aims to develop a high-fidelity non-contact ECG system to evaluate the influence of capacitive coupling on ECG measurement. In this study, a polymer foam with low surface resistance ( $0.05\Omega $ /inch2) was designed for improving the capacitive-coupling interface between the curved body and electrode sensing surface. The system recorded excellent non-contact ECG of 29.8dB, and the accuracy of heart rate was 99.5% compared to wet-contact ECG measurement. The SNR exponentially attenuated with decreasing skin-electrode capacitance by the combined evidence of theoretical calculation and experimental results. The proposed system generates distinguishable ECG signals (SNR>0dB) at the skin-electrode capacitance above 85pF and maximum through-thickness of cotton-based cloth of 1.2mm. In conclusion, this study evaluated the influence of capacitive coupling on non-contact ECG measurements and established a lower bound of the coupling capacitance for satisfactory signal quality. Future studies may investigate whether the coupling capacitance can be further reduced.

Pedestrian orientation dynamics from high-fidelity measurements

We investigate in real-life conditions and with very high accuracy the dynamics of body rotation, or yawing, of walking pedestrians—a highly complex task due to the wide variety in shapes, postures and walking gestures. We propose a novel measurement method based on a deep neural architecture that we train on the basis of generic physical properties of the motion of pedestrians. Specifically, we leverage on the strong statistical correlation between individual velocity and body orientation: the velocity direction is typically orthogonal with respect to the shoulder line. We make the reasonable assumption that this approximation, although instantaneously slightly imperfect, is correct on average. This enables us to use velocity data as training labels for a highly-accurate point-estimator of individual orientation, that we can train with no dedicated annotation labor. We discuss the measurement accuracy and show the error scaling, both on synthetic and real-life data: we show that our method is capable of estimating orientation with an error as low as 7.5^circ. This tool opens up new possibilities in the studies of human crowd dynamics where orientation is key. By analyzing the dynamics of body rotation in real-life conditions, we show that the instantaneous velocity direction can be described by the combination of orientation and a random delay, where randomness is provided by an Ornstein–Uhlenbeck process centered on an average delay of 100,hbox {ms}. Quantifying these dynamics could have only been possible thanks to a tool as precise as that proposed.

Learning and meta-learning of stochastic advection–diffusion–reaction systems from sparse measurements

Physics-informed neural networks (PINNs) were recently proposed in [18] as an alternative way to solve partial differential equations (PDEs). A neural network (NN) represents the solution, while a PDE-induced NN is coupled to the solution NN, and all differential operators are treated using automatic differentiation. Here, we first employ the standard PINN and a stochastic version, sPINN, to solve forward and inverse problems governed by a non-linear advection–diffusion–reaction (ADR) equation, assuming we have some sparse measurements of the concentration field at random or pre-selected locations. Subsequently, we attempt to optimise the hyper-parameters of sPINN by using the Bayesian optimisation method (meta-learning) and compare the results with the empirically selected hyper-parameters of sPINN. In particular, for the first part in solving the inverse deterministic ADR, we assume that we only have a few high-fidelity measurements, whereas the rest of the data is of lower fidelity. Hence, the PINN is trained using a composite multi-fidelity network, first introduced in [12], that learns the correlations between the multi-fidelity data and predicts the unknown values of diffusivity, transport velocity and two reaction constants as well as the concentration field. For the stochastic ADR, we employ a Karhunen–Loève (KL) expansion to represent the stochastic diffusivity, and arbitrary polynomial chaos (aPC) to represent the stochastic solution. Correspondingly, we design multiple NNs to represent the mean of the solution and learn each aPC mode separately, whereas we employ a separate NN to represent the mean of diffusivity and another NN to learn all modes of the KL expansion. For the inverse problem, in addition to stochastic diffusivity and concentration fields, we also aim to obtain the (unknown) deterministic values of transport velocity and reaction constants. The available data correspond to 7spatial points for the diffusivity and 20 space–time points for the solution, both sampled 2000 times. We obtain good accuracy for the deterministic parameters of the order of 1–2% and excellent accuracy for the mean and variance of the stochastic fields, better than three digits of accuracy. In the second part, we consider the previous stochastic inverse problem, and we use Bayesian optimisation to find five hyper-parameters of sPINN, namely the width, depth and learning rate of two NNs for learning the modes. We obtain much deeper and wider optimal NNs compared to the manual tuning, leading to even better accuracy, i.e., errors less than 1% for the deterministic values, and about an order of magnitude less for the stochastic fields.

Repeated quantum error detection in a surface code

The realization of quantum error correction is an essential ingredient for reaching the full potential of fault-tolerant universal quantum computation. Using a range of different schemes, logical qubits can be redundantly encoded in a set of physical qubits. One such scalable approach is based on the surface code. Here we experimentally implement its smallest viable instance, capable of repeatedly detecting any single error using seven superconducting qubits, four data qubits and three ancilla qubits. Using high-fidelity ancilla-based stabilizer measurements we initialize the cardinal states of the encoded logical qubit with an average logical fidelity of 96.1%. We then repeatedly check for errors using the stabilizer readout and observe that the logical quantum state is preserved with a lifetime and coherence time longer than those of any of the constituent qubits when no errors are detected. Our demonstration of error detection with its resulting enhancement of the conditioned logical qubit coherence times in a 7-qubit surface code is an important step indicating a promising route towards the realization of quantum error correction in the surface code.

Metal-embedded fiber optic sensor packaging and signal demodulation scheme towards high-frequency dynamic measurements in harsh environments

Optical fiber-based sensors are commonly used for industrial monitoring and control processes due to their small size, resistance to electromagnetic interference, and ability to perform a wide variety of high-precision measurements. However, implementing optical fiber sensors in harsh environments is challenging because they are small and fragile. Proper packaging of fiber-optic sensors could extend their use to harsh environments, including at high temperature and under high radiation. Furthermore, conventional fiber optic-based measurement systems often use computationally expensive signal processing algorithms which hinder their use in high-frequency dynamic applications. This work reports on the design of an optical fiber-pressure sensor system based on low-coherence interferometry that uses a metal-embedded optical fiber to provide a robust sensor package. A novel phase demodulation scheme is proposed to extract length changes from the deformation of a thin diaphragm within a Fabry–Pérot cavity to measure pressure. The sensor has been tested to ±100kPa, has a theoretical linear response over a ±270kPa dynamic range (24dB maximum signal-to-noise ratio), and can resolve pressure transients up to 3910kPa/s. Sampling at 100kHz, the present sensor can resolve 2kPa dynamic pressures at frequencies up to 2kHz. Faster transients on the order of tens to hundreds of kHz can theoretically be resolved at the expense of decreasing the maximum resolvable amplitude. A methodology for designing a LCI pressure sensor for a given application is outlined based on the limitations imposed by the Nyquist criterion, the diaphragm's resonant frequency, the LCI optoelectronics, and the phase demodulation scheme. The sensor is the first to implement a low-coherence light source and a Fabry–Pérot interferometer designed to provide real-time high-fidelity pressure measurements using a metal-embedded optical fiber. The demonstrated sensor provides a platform for sensing in harsh conditions such as in nuclear and energy applications.

Quantum computing with multidimensional continuous-variable cluster states in a scalable photonic platform

Quantum computing is a disruptive paradigm widely believed to be capable of solving classically intractable problems. However, the route toward full-scale quantum computers is obstructed by immense challenges associated with the scalability of the platform, the connectivity of qubits, and the required fidelity of various components. One-way quantum computing is an appealing approach that shifts the burden from high-fidelity quantum gates and quantum memories to the generation of high-quality entangled resource states and high fidelity measurements. Cluster states are an important ingredient for one-way quantum computing, and a compact, portable, and mass producible platform for large-scale cluster states will be essential for the widespread deployment of one-way quantum computing. Here, we bridge two distinct fields---Kerr microcombs and continuous-variable (CV) quantum information---to formulate a one-way quantum computing architecture based on programmable large-scale CV cluster states. The architecture can accommodate hundreds of simultaneously addressable entangled optical modes multiplexed in the frequency domain and an unlimited number of sequentially addressable entangled optical modes in time domain. One-dimensional, two-dimensional, and three-dimensional CV cluster states can be deterministically produced. We note cluster states of at least three dimensions are required for fault-tolerant one-way quantum computing with known error-correction strategies. This architecture can be readily implemented with silicon photonics, opening a promising avenue for quantum computing at a large scale.

Predicting Excavation-Induced Tunnel Response by Process-Based Modelling

Potential damages to existing tunnels represent a major concern for constructing deep excavations in urban areas. The uncertainty of subsurface conditions and the nonlinear interactions between multiple agents (e.g., soils, excavation support structures, and tunnel structures) make the prediction of the response of tunnel induced by adjacent excavations a rather difficult and complex task. This paper proposes an initiative to solve this problem by using process-based modelling, where information generated from the interaction processes between soils, structures, and excavation activities is utilized to gradually reduce uncertainty related to soil properties and to learn the interaction patterns through machine learning techniques. To illustrate such a concept, this paper presents a simple process-based model consisting of artificial neural network (ANN) module, inverse modelling module, and mechanistic module. The ANN module is trained to learn and recognize the patterns of the complex interactions between excavation deformations, its geometries and support structures, and soil properties. The inverse modelling module enables a gradual reduction of uncertainty associated with soil characterizations by accumulating field observations during the construction processes. Based on the inputs provided by the former two modules, the mechanistic module computes the response of tunnel. The effectiveness of the proposed process-based model is evaluated against high-fidelity numerical simulations and field measurements. These evaluations suggest that the strategy of combining artificial intelligence techniques with information generated during interaction processes can represent a promising approach to solve complex engineering problems in conventional industries.

Experimental demonstration of memory-enhanced quantum communication.

The ability to communicate quantum information over long distances is of central importance in quantum science and engineering1. Although some applications of quantum communication such as secure quantum key distribution2,3 are already being successfully deployed4-7, their range is currently limited by photon losses and cannot be extended using straightforward measure-and-repeat strategies without compromising unconditional security8. Alternatively, quantum repeaters9, which utilize intermediate quantum memory nodes and error correction techniques, can extend the range of quantum channels. However, their implementation remains an outstanding challenge10-16, requiring a combination of efficient and high-fidelity quantum memories, gate operations, and measurements. Here we use a single solid-state spin memory integrated in a nanophotonic diamond resonator17-19 to implement asynchronous photonic Bell-state measurements, which are a key component of quantum repeaters. In a proof-of-principle experiment, we demonstrate high-fidelity operation that effectively enables quantum communication at a rate that surpasses the ideal loss-equivalent direct-transmission method while operating at megahertz clock speeds. These results represent a crucial step towards practical quantum repeaters and large-scale quantum networks20,21.