Search

We present MathDSL, a Domain-Specific Language (DSL) for mathematical equation solving, which, when deployed in program synthesis models, outperforms state-of-the-art reinforcement-learning-based methods. We also introduce a quantitative metric for measuring the conciseness of a mathematical solution and demonstrate the improvement in the quality of generated solutions compared to other methods. Our system demonstrates that a program synthesis system (DreamCoder) using MathDSL can generate programs that solve linear equations with greater accuracy and conciseness than using reinforcement learning systems. Additionally, we demonstrate that if we use the action spaces of previous reinforcement learning systems as DSLs, MathDSL outperforms the action-space-DSLs. We use DreamCoder to store equation-solving strategies as learned abstractions in its program library and demonstrate that by using MathDSL, these can be converted into human-interpretable solution strategies that could have applications in mathematical education.

Flexoelectricity is a universal effect that generates electric polarization due to broken inversion symmetry caused by local strain gradient. The large strain gradient at nanoscale makes flexo-electric effects, especially in nanoscopic ferroelectric materials, promising in sensors, actuator, energy harvesting, and memory applications. In this work, we studied flexoelectricity in hexagonal ferrites h-YbFeO3, an improper ferroelectric expected to have weak piezoelectricity and low sensitivity to depolarization field, which are advantageous for studying flexoelectric effects. We show that in h-YbFeO3 epitaxial thin films, strain gradient on the order of 10^6 m-1 occurs near grain boundaries, which has a significant impact on the non-polar K3 structural distortion that induces spontaneous polarization. The phenomenological model based on the Landau theory of improper ferroelectricity suggests an indirect flexoelectric effect on the order of 10 nC/m in h-YbFeO3, which is substantially larger than the expectation from Kogan mechanism. These results reveal a novel microscopic mechanism of coupling between strain gradient and polarization mediated by structural distortion, which we call improper flexoelectricity.

We explore how III-V semiconductor microring resonators can efficiently generate photon pairs and squeezed vacuum states via spontaneous parametric down-conversion by utilizing their built-in quasi phase matching and modal dispersion. We present an analytic expression for the biphoton wave function of photon pairs generated by weak pump pulses, and characterize the squeezed states that result under stronger pumping conditions. Our model includes loss, and captures the statistics of the scattered photons. A detailed sample calculation shows that for low pump powers conversion efficiencies of 10$^{-5}$, corresponding to a rate of 39 MHz for a pump power of 1 $\mu$W, are attainable for rudimentary structures such as a simple microring coupled to a waveguide, in both the continuous wave and pulsed excitation regimes. Our results suggest that high levels of squeezing and pump depletion are attainable, possibly leading to the deterministic generation of non-Gaussian states, Comment: 17 pages, 9 figures

We present an exploratory restricted latent class model where response data is for a single time point, polytomous, and differing across items, and where latent classes reflect a multi-attribute state where each attribute is ordinal. Our model extends previous work to allow for correlation of the attributes through a multivariate probit and to allow for respondent-specific covariates. We demonstrate that the model recovers parameters well in a variety of realistic scenarios, and apply the model to the analysis of a particular dataset designed to diagnose depression. The application demonstrates the utility of the model in identifying the latent structure of depression beyond single-factor approaches which have been used in the past., Comment: 37 pages, 1 figure, 7 tables; changed title, fixed one typo, improved accessibility, and made stylistic adjustments

Swept laser interferometry is an extremely powerful solution embedded in several recent technologies such as absolute distance measurement, light detection and ranging, optical frequency domain reflectometry, optical coherence tomography, microresonator characterization, and gas spectroscopy. Nonlinearity in the optical frequency sweeping of tunable lasers is a fatal drawback in gaining the expected outcome from these technologies. Here, we introduce an onchip, millimeter scale, 7 m spiral resonator that is made of ultralow loss silicon nitride to act as a frequency ruler for correction of the tunable lasers sweeping nonlinearities. The sharp 2 MHz frequency lines of the 85 M high-quality resonator and the narrow spaced 25.57 MHz frequency ticks of the 7 m spiral allow unprecedented precise nonlinearity correction on an integrated photonics platform. Accurate measurements of the rulers frequency spacing, linewidth, and temperature and wavelength sensitivities of the frequency ticks are performed here to demonstrate the quality of the frequency ruler. In addition, the spiral resonator is implemented in an FMCW LiDAR experiment to demonstrate a potential application of the proposed onchip frequency ruler., Comment: 12 pages

In the last decade, remarkable advances in integrated photonic technologies have enabled table-top experiments and instrumentation to be scaled down to compact chips with significant reduction in size, weight, power consumption, and cost. Here, we demonstrate an integrated continuously tunable laser in a heterogeneous gallium arsenide-on-silicon nitride (GaAs-on-SiN) platform that emits in the far-red radiation spectrum near 780 nm, with 20 nm tuning range, <6 kHz intrinsic linewidth, and a >40 dB side-mode suppression ratio. The GaAs optical gain regions are heterogeneously integrated with low-loss SiN waveguides. The narrow linewidth lasing is achieved with an extended cavity consisting of a resonator-based Vernier mirror and a phase shifter. Utilizing synchronous tuning of the integrated heaters, we show mode-hop-free wavelength tuning over a range larger than 100 GHz (200 pm). To demonstrate the potential of the device, we investigate two illustrative applications: (i) the linear characterization of a silicon nitride microresonator designed for entangled-photon pair generation, and (ii) the absorption spectroscopy and locking to the D1 and D2 transition lines of 87-Rb. The performance of the proposed integrated laser holds promise for a broader spectrum of both classical and quantum applications in the visible range, encompassing communication, control, sensing, and computing.

Correlated photon-pair sources are key components for quantum computing, networking, and sensing applications. Integrated photonics has enabled chip-scale sources using nonlinear processes, producing high-rate entanglement with sub-100 microwatt power at telecom wavelengths. Many quantum systems operate in the visible or near-infrared ranges, necessitating broadband visible-telecom entangled-pair sources for connecting remote systems via entanglement swapping and teleportation. This study evaluates broadband entanglement generation through spontaneous four-wave mixing in various nonlinear integrated photonic materials, including silicon nitride, lithium niobate, aluminum gallium arsenide, indium gallium phosphide, and gallium nitride. We demonstrate how geometric dispersion engineering facilitates phase-matching for each platform and reveals unexpected results, such as robust designs to fabrication variations and a Type-1 cross-polarized phase-matching condition for III-V materials that expands the operational bandwidth. With experimentally attainable parameters, integrated photonic microresonators with optimized designs can achieve pair generation rates greater than ~1 THz/mW$^2$.

Along the I24, I27 and I31 flybys of Io (1999-2001), the Energetic Particle Detector (EPD) onboard the Galileo spacecraft observed localised regions of energetic protons losses (155 keV-1250 keV). Using back-tracking particle simulations combined with a prescribed atmospheric distribution and a magnetohydrodynamics (MHD) model of the plasma/atmosphere interaction, we investigate the possible causes of these depletions. We focus on a limited region within two Io radii, which is dominated by Io's SO$_2$ atmosphere. Our results show that charge exchange of protons with the SO$_2$ atmosphere, absorption by the surface and the configuration of the electromagnetic field contribute to the observed proton depletion along the Galileo flybys. In the 155-240 keV energy range, charge exchange is either a major or the dominant loss process, depending on the flyby altitude. In the 540-1250 keV range, as the charge exchange cross sections are small, the observed decrease of the proton flux is attributed to absorption by the surface and the perturbed electromagnetic fields, which divert the protons away from the detector. From a comparison between the modelled losses and the data we find indications of an extended atmosphere on the day/downstream side of Io, a lack of atmospheric collapse on the night/upstream side as well as a more global extended atmospheric component ($> 1$ Io radius). Our results demonstrate that observations and modeling of proton depletion around the moon constitute an important tool to constrain the electromagnetic field configuration around Io and the radial and longitudinal atmospheric distribution, which is still poorly understood.

The development of manufacturable and scalable integrated nonlinear photonic materials is driving key technologies in diverse areas such as high-speed communications, signal processing, sensing, and quantum information. Here, we demonstrate a novel nonlinear platform -- InGaP-on-insulator -- optimized for visible-to-telecommunication wavelength $\chi^{\left(2\right)}$ nonlinear optical processes. In this work, we detail our 100-mm wafer-scale InGaP-on-insulator fabrication process realized via wafer bonding, optical lithography, and dry-etching techniques. The resulting wafers yield 1000s of components in each fabrication cycle, with initial designs that include chip-to-fiber couplers, 12.5-cm-long nested spiral waveguides, and arrays of microring resonators with free-spectral ranges spanning 400-900 GHz. We demonstrate intrinsic resonator quality factors as high as 324,000 (440,000) for single-resonance (split-resonance) modes near 1550 nm corresponding to 1.56 dB cm$^{-1}$ (1.22 dB cm$^{-1}$) propagation loss. We analyze the loss versus waveguide width and resonator radius to establish the operating regime for optimal 775-to-1550 nm phase matching. By combining the high $\chi^{\left(2\right)}$ and $\chi^{\left(3\right)}$ optical nonlinearity of InGaP with wafer-scale fabrication and low propagation loss, these results open promising possibilities for entangled-photon, multi-photon, and squeezed light generation.

High-performance, high-volume-manufacturing Si3N4 photonics requires extremely low waveguide losses augmented with heterogeneously integrated lasers for applications beyond traditional markets of high-capacity interconnects. State-of-the-art quality factors (Q) over 200 million at 1550 nm have been shown previously; however, maintaining high Qs throughout laser fabrication has not been shown. Here, Si3N4 resonator intrinsic Qs over 100 million are demonstrated on a fully integrated heterogeneous laser platform. Qi is measured throughout laser processing steps, showing degradation down to 50 million from dry etching, metal evaporation, and ion implant steps, and controllable recovery to over 100 million from annealing at 250C - 350C.

We consider max-weighted matching with costs for learning the weights, modeled as a "Pandora's Box" on each endpoint of an edge. Each vertex has an initially-unknown value for being matched to a neighbor, and an algorithm must pay some cost to observe this value. The goal is to maximize the total matched value minus costs. Our model is inspired by two-sided settings, such as matching employees to employers. Importantly for such settings, we allow for negative values which cause existing approaches to fail. We first prove upper bounds for algorithms in two natural classes. Any algorithm that "bundles" the two Pandora boxes incident to an edge is an $o(1)$-approximation. Likewise, any "vertex-based" algorithm, which uses properties of the separate Pandora's boxes but does not consider the interaction of their value distributions, is an $o(1)$-approximation. Instead, we utilize Pandora's Nested-Box Problem, i.e. multiple stages of inspection. We give a self-contained, fully constructive optimal solution to the nested-boxes problem, which may have structural observations of interest compared to prior work. By interpreting each edge as a nested box, we leverage this solution to obtain a constant-factor approximation algorithm. Finally, we show any ``edge-based'' algorithm, which considers the interactions of values along an edge but not with the rest of the graph, is also an $o(1)$-approximation.

Latent Class Models (LCMs) are used to cluster multivariate categorical data, commonly used to interpret survey responses. We propose a novel Bayesian model called the Equivalence Set Restricted Latent Class Model (ESRLCM). This model identifies clusters who have common item response probabilities, and does so more generically than traditional restricted latent attribute models. We verify the identifiability of ESRLCMs, and demonstrate the effectiveness in both simulations and real-world applications., Comment: 43 pages, 10 tables, 1 figure

Next-generation communications, radar and navigation systems will extend and exploit the higher bandwidth of the millimeter-wave domain for increased communication data rates as well as radar with higher sensitivity and increased spatial resolution. However, realizing these advantages will require the generation of millimeter-wave signals with low phase noise in simple and compact form-factors. The rapidly developing field of photonic integration addresses this challenge and provides a path toward simplified and portable, low-noise mm-wave generation for these applications. We leverage these advances by heterodyning two silicon photonic chip lasers, phase-locked to the same miniature Fabry-Perot (F-P) cavity to demonstrate a simple framework for generating low-noise millimeter-waves with phase noise below the thermal limit of the F-P cavity. Specifically, we generate 94.5 GHz and 118.1 GHz millimeter-wave signals with phase noise of -117 dBc/Hz at 10 kHz offset, decreasing to -120 dBc/Hz at 40 kHz offset, a record low value for such photonic devices. We achieve this with existing technologies that can be integrated into a platform less than $\approx$ 10 mL in volume. Our work illustrates the significant potential and advantages of low size, weight, and power (SWaP) photonic-sourced mm-waves for communications and sensing., Comment: 5 pages, 4 figures

Brains have evolved a diverse set of neurons with varying morphologies, physiological properties and rich dynamics that impact their processing of temporal information. By contrast, most neural network models include a homogeneous set of units that only vary in terms of their spatial parameters (weights and biases). To investigate the importance of temporal parameters to neural function, we trained spiking neural networks on tasks of varying temporal complexity, with different subsets of parameters held constant. We find that in a tightly resource constrained setting, adapting conduction delays is essential to solve all test conditions, and indeed that it is possible to solve these tasks using only temporal parameters (delays and time constants) with weights held constant. In the most complex spatio-temporal task we studied, we found that an adaptable bursting parameter was essential. More generally, allowing for adaptation of both temporal and spatial parameters increases network robustness to noise, an important feature for both biological brains and neuromorphic computing systems. In summary, our findings highlight how rich and adaptable dynamics are key to solving temporally structured tasks at a low neural resource cost, which may be part of the reason why biological neurons vary so dramatically in their physiological properties., Comment: 14 pages, 6 figures

Recently, ACM FAccT published an article by Kwegyir-Aggrey and colleagues (2023), critiquing the use of AUC ROC in predictive analytics in several domains. In this article, we offer a critique of that article. Specifically, we highlight technical inaccuracies in that paper's comparison of metrics, mis-specification of the interpretation and goals of AUC ROC, the article's use of the accuracy metric as a gold standard for comparison to AUC ROC, and the article's application of critiques solely to AUC ROC for concerns that would apply to the use of any metric. We conclude with a re-framing of the very valid concerns raised in that article, and discuss how the use of AUC ROC can remain a valid and appropriate practice in a well-informed predictive analytics approach taking those concerns into account. We conclude by discussing the combined use of multiple metrics, including machine learning bias metrics, and AUC ROC's place in such an approach. Like broccoli, AUC ROC is healthy, but also like broccoli, researchers and practitioners in our field shouldn't eat a diet of only AUC ROC.

Multiple benchmarks have been developed to assess the alignment between deep neural networks (DNNs) and human vision. In almost all cases these benchmarks are observational in the sense they are composed of behavioural and brain responses to naturalistic images that have not been manipulated to test hypotheses regarding how DNNs or humans perceive and identify objects. Here we introduce the toolbox MindSet: Vision, consisting of a collection of image datasets and related scripts designed to test DNNs on 30 psychological findings. In all experimental conditions, the stimuli are systematically manipulated to test specific hypotheses regarding human visual perception and object recognition. In addition to providing pre-generated datasets of images, we provide code to regenerate these datasets, offering many configurable parameters which greatly extend the dataset versatility for different research contexts, and code to facilitate the testing of DNNs on these image datasets using three different methods (similarity judgments, out-of-distribution classification, and decoder method), accessible at https://github.com/MindSetVision/mindset-vision. We test ResNet-152 on each of these methods as an example of how the toolbox can be used.

Rapid progress in photonics has led to an explosion of integrated devices that promise to deliver the same performance as table-top technology at the nanoscale; heralding the next generation of optical communications, sensing and metrology, and quantum technologies. However, the challenge of co-integrating the multiple components of high-performance laser systems has left application of these nanoscale devices thwarted by bulky laser sources that are orders of magnitude larger than the devices themselves. Here we show that the two main ingredients for high-performance lasers -- noise reduction and isolation -- currently requiring serial combination of incompatible technologies, can be sourced simultaneously from a single, passive, CMOS-compatible nanophotonic device. To do this, we take advantage of both the long photon lifetime and the nonreciprocal Kerr nonlinearity of a high quality factor silicon nitride ring resonator to self-injection lock a semiconductor laser chip while also providing isolation. Additionally, we identify a previously unappreciated power regime limitation of current on-chip laser architectures which our system overcomes. Using our device, which we term a unified laser stabilizer, we demonstrate an on-chip integrated laser system with built-in isolation and noise reduction that operates with turnkey reliability. This approach departs from efforts to directly miniaturize and integrate traditional laser system components and serves to bridge the gap to fully integrated optical technologies.

Modern communication, navigation, and radar systems rely on low noise and frequency-agile microwave sources. In this application space, photonic systems provide an attractive alternative to conventional microwave synthesis by leveraging high spectral purity lasers and optical frequency combs to generate microwaves with exceedingly low phase noise. However, these photonic techniques suffer from a lack of frequency tunability, and also have substantial size, weight, and power requirements that largely limit their use to laboratory settings. In this work, we address these shortcomings with a hybrid opto-electronic approach that combines simplified optical frequency division with direct digital synthesis to produce tunable low-phase-noise microwaves across the entire X-band. This results in exceptional phase noise at 10 GHz of -156 dBc/Hz at 10 kHz offset and fractional frequency instability of 1x10^-13 at 0.1 s. Spot tuning away from 10 GHz by 500 MHz, 1 GHz, and 2 GHz, yields phase noise at 10 kHz offset of -150 dBc/Hz, -146 dBc/Hz, and -140 dBc/Hz, respectively. The synthesizer architecture is fully compatible with integrated photonic implementations that will enable a versatile microwave source in a chip-scale package. Together, these advances illustrate an impactful and practical synthesis technique that shares the combined benefits of low timing noise provided by photonics and the frequency agility of established digital synthesis.

We propose a new approach for modeling and reconciling conflicting data cleaning actions. Such conflicts arise naturally in collaborative data curation settings where multiple experts work independently and then aim to put their efforts together to improve and accelerate data cleaning. The key idea of our approach is to model conflicting updates as a formal \emph{argumentation framework}(AF). Such argumentation frameworks can be automatically analyzed and solved by translating them to a logic program $P_{AF}$ whose declarative semantics yield a transparent solution with many desirable properties, e.g., uncontroversial updates are accepted, unjustified ones are rejected, and the remaining ambiguities are exposed and presented to users for further analysis. After motivating the problem, we introduce our approach and illustrate it with a detailed running example introducing both well-founded and stable semantics to help understand the AF solutions. We have begun to develop open source tools and Jupyter notebooks that demonstrate the practicality of our approach. In future work we plan to develop a toolkit for conflict resolution that can be used in conjunction with OpenRefine, a popular interactive data cleaning tool., Comment: Accepted to IDCC 2024. Source code is available at https://github.com/idaks/Games-and-Argumentation/tree/idcc

The remarkable frequency stability of resonant systems in the optical domain (optical cavities and atomic transitions) can be harnessed at frequency scales accessible by electronics using optical frequency division. This capability is revolutionizing technologies spanning time keeping to high-performance electrical signal sources. A version of the technique called 2-point optical frequency division (2P-OFD) is proving advantageous for application to high-performance signal sources. In 2P-OFD, an optical cavity anchors two spectral endpoints defined by lines of a frequency comb. The comb need not be self-referenced, which greatly simplifies the system architecture and reduces power requirements. Here, a 2P-OFD microwave signal source is demonstrated with record-low phase noise using a microcomb. Key to this advance is a spectral endpoint defined by a frequency agile single-mode dispersive wave that is emitted by the microcomb soliton. Moreover, the system frequency reference is a compact all-solid-state optical cavity with a record Q-factor. The results advance integrable microcomb-based signal sources into the performance realm of much larger microwave sources.

Achieving visual reasoning is a long-term goal of artificial intelligence. In the last decade, several studies have applied deep neural networks (DNNs) to the task of learning visual relations from images, with modest results in terms of generalization of the relations learned. However, in recent years, object-centric representation learning has been put forward as a way to achieve visual reasoning within the deep learning framework. Object-centric models attempt to model input scenes as compositions of objects and relations between them. To this end, these models use several kinds of attention mechanisms to segregate the individual objects in a scene from the background and from other objects. In this work we tested relation learning and generalization in several object-centric models, as well as a ResNet-50 baseline. In contrast to previous research, which has focused heavily in the same-different task in order to asses relational reasoning in DNNs, we use a set of tasks -- with varying degrees of difficulty -- derived from the comparative cognition literature. Our results show that object-centric models are able to segregate the different objects in a scene, even in many out-of-distribution cases. In our simpler tasks, this improves their capacity to learn and generalize visual relations in comparison to the ResNet-50 baseline. However, object-centric models still struggle in our more difficult tasks and conditions. We conclude that abstract visual reasoning remains an open challenge for DNNs, including object-centric models., Comment: 16 pages, 14 figures

Optical frequency division (OFD) has revolutionized microwave and mmWave generation and set spectral purity records owing to its unique capability to transfer high fractional stability from optical to electronic frequencies. Recently, rapid developments in integrated optical reference cavities and microresonator-based optical frequency combs (microcombs) have created a path to transform OFD technology to chip scale. Here, we demonstrate an ultra-low phase noise mmWave oscillator by leveraging integrated photonic components and Kerr optical frequency division. The oscillator derives its stability from an integrated CMOS-compatible SiN coil cavity, and the optical frequency division is achieved spontaneously through Kerr interaction between the injected reference lasers and soliton microcombs in the integrated SiN microresonator. Besides achieving record-low phase noise for integrated mmWave oscillators, our demonstration greatly simplifies the implementation of integrated OFD oscillators and could be useful in applications of Radar, spectroscopy, and astronomy., Comment: 8 pages, 5 figures

Single spacecraft missions do not measure the pristine solar wind continuously because of the spacecrafts' orbital trajectory. The infrequent spatiotemporal cadence of measurement fundamentally limits conclusions about solar wind-magnetosphere coupling throughout the solar system. At Mars, such single spacecraft missions result in limitations for assessing the solar wind's role in causing lower altitude observations such as auroral dynamics or atmospheric loss. In this work, we detail the development of a virtual solar wind monitor from the Mars Atmosphere and Volatile Evolution (MAVEN) mission; a single spacecraft. This virtual solar wind monitor provides a continuous estimate of the solar wind upstream from Mars with uncertainties. We specifically employ Gaussian process regression to estimate the upstream solar wind and uncertainty estimations that scale with the data sparsity of our real observations. This proxy enables continuous solar wind estimation at Mars with representative uncertainties for the majority of the time since since late 2014. We conclude by discussing suggested uses of this virtual solar wind monitor for statistical studies of the Mars space environment and heliosphere., Comment: published in JGR: Machine Learning and Computation

Ultra-low-noise laser sources are crucial for a variety of applications, including microwave synthesizers, optical gyroscopes, and the manipulation of quantum systems. Silicon photonics has emerged as a promising solution for high-coherence applications due to its ability to reduce system size, weight, power consumption, and cost (SWaP-C). Semiconductor lasers based on self-injection locking (SIL) have reached fiber laser coherence, but typically require a high-Q external cavity to suppress coherence collapse through frequency-selective feedback. Lasers based on external-cavity locking (ECL) are a low-cost and turnkey operation option, but their coherence is generally inferior to SIL lasers. In this work, we demonstrate quantum-dot (QD) lasers grown directly on Si that achieve SIL laser coherence under turnkey ECL. The high-performance QD laser offers a scalable and low-cost heteroepitaxial integration platform. Moreover, the QD laser's chaos-free nature enables a 16 Hz Lorentzian linewidth under ECL using a low-Q external cavity, and improves the frequency noise by an additional order of magnitude compared to conventional quantum-well lasers.

Randomized benchmarking (RB) is a widely used strategy to assess the quality of available quantum gates in a computational context. RB involves applying known random sequences of gates to an initial state and using the statistics of a final measurement step to determine an effective depolarizing error per step of the sequence, which is a metric of gate quality. Here we investigate the advantages of fully randomized benchmarking, where a new random sequence is drawn for each experimental trial. The advantages of full randomization include smaller confidence intervals on the inferred step error, the ability to use maximum likelihood analysis without heuristics, straightforward optimization of the sequence lengths, and the ability to model and measure behaviors that go beyond the typical assumption of time-independent error rates. We discuss models of time-dependent or non-Markovian errors that generalize the basic RB model of a single exponential decay of the success probability. For any of these models, we implement a concrete protocol to minimize the uncertainty of the estimated parameters given a fixed time constraint on the complete experiment, and we implement a maximum likelihood analysis. We consider several previously published experiments and determine the potential for improvements with optimized full randomization. We experimentally observe such improvements in Clifford randomized benchmarking experiments on a single trapped ion qubit at the National Institute of Standards and Technology (NIST). For an experiment with uniform lengths and intentionally repeated sequences the step error was $2.42^{+0.30}_{-0.22}\times 10^{-5}$, and for an optimized fully randomized experiment of the same total duration the step error was $2.57^{+0.07}_{-0.06}\times 10^{-5}$. We find a substantial decrease in the uncertainty of the step error as a result of optimized fully randomized benchmarking.

Coherent optics has profoundly impacted diverse applications ranging from communications, LiDAR to quantum computations. However, building coherent systems in integrated photonics previously came at great expense in hardware integration and energy efficiency: the lack of a power-efficient way to generate highly coherent light necessitates bulky lasers and amplifiers, while frequency and phase recovery schemes require huge digital signal processing resources. In this work, we demonstrate a high-coherence parallelization strategy that facilitates advanced integrated coherent systems at a minimum price. Using a self-injection locked microcomb to injection lock a distributed feedback laser array, we boost the microcomb power by a record high gain of up to 60 dB on chip with no degradation in coherence. This strategy enables tens of highly coherent channels with an intrinsic linewidth down to the 10 Hz level and power of more than 20 dBm. The overall electrical to optical wall-plug efficiency reaches 19%, comparable with that of the state-of-the-art semiconductor lasers. Driven by this parallel source, we demonstrate a silicon photonic communication link with an unprecedented data rate beyond 60 Tbit/s. Importantly, the high coherence we achieve reduces the coherent-related DSP consumption by 99.999% compared with the traditional III-V laser pump scheme. This work paves a way to realizing scalable, high-performance coherent integrated photonic systems, potentially benefiting numerous applications.

Numerous modern technologies are reliant on the low-phase noise and exquisite timing stability of microwave signals. Substantial progress has been made in the field of microwave photonics, whereby low noise microwave signals are generated by the down-conversion of ultra-stable optical references using a frequency comb. Such systems, however, are constructed with bulk or fiber optics and are difficult to further reduce in size and power consumption. Our work addresses this challenge by leveraging advances in integrated photonics to demonstrate low-noise microwave generation via two-point optical frequency division. Narrow linewidth self-injection locked integrated lasers are stabilized to a miniature Fabry-P\'{e}rot cavity, and the frequency gap between the lasers is divided with an efficient dark-soliton frequency comb. The stabilized output of the microcomb is photodetected to produce a microwave signal at 20 GHz with phase noise of -96 dBc/Hz at 100 Hz offset frequency that decreases to -135 dBc/Hz at 10 kHz offset--values which are unprecedented for an integrated photonic system. All photonic components can be heterogeneously integrated on a single chip, providing a significant advance for the application of photonics to high-precision navigation, communication and timing systems.