Implementation of energy-efficient convolutional neural networks based on kernel-pruned silicon photonics.

Silicon-based optical neural networks offer the prospect of high-performance computing on integrated photonic circuits. However, the scalability of on-chip optical depth networks is restricted by the limited energy and space resources. Here, we present a silicon-based photonic convolutional neural network (PCNN) combined with the kernel pruning, in which the optical convolutional computing core of PCNN is a tunable micro-ring weight bank. Our numerical simulation demonstrates the effect of weight mapping accuracy on PCNN performance and we find that the performance of PCNN decreases significantly when the weight mapping accuracy is less than 4.3 bits. Additionally, the experimental demonstration shows that the accuracy of the PCNN on the MNIST dataset has a slight loss compared to the original CNN when 93.75 % of the convolutional kernels are pruned. By making use of kernel pruning, the energy saved by a convolutional kernel removal is about 202.3 mW, and the overall energy saved has a linear relationship with the number of kernels removed. The methodology is scalable and provides a feasible solution for implementing faster and more energy-efficient large-scale optical convolutional neural networks on photonic integrated circuits.

The prediction of dynamical quantities in granular avalanches based on graph neural networks.

The study of granular avalanches in rotating drums is not only essential to understanding various complex behaviors of interest in granular media from a scientific perspective; it also has valuable applications in regard to industrial processes and geological catastrophes. Despite decades of research studies on avalanches, a proper understanding of their dynamic properties still remains a great challenge to scientists due to a lack of state-of-the-art techniques. In this study, we accurately predict the avalanche dynamic features of three-dimensional granular materials in rotating drums, by using graph neural networks on the basis of their initial static microstructures alone. We find that our method is robust to changes in various model parameters, such as the interaction potential, size polydispersity, and noise in particle coordinates. In addition, with the grain-scale velocities obtained either from our network or from numerical simulations, we find an approximately equal and strong correlation between the global velocity and global velocity fluctuation in our 3D granular avalanche systems, which further demonstrates the predictive power of our trained graph neural networks to uncover the fundamental physics of granular avalanches. We expect our method to provide more insight into the avalanche dynamics of granular materials and other amorphous systems in the future.

The prediction of contact force networks in granular materials based on graph neural networks.

The contact force network, usually organized inhomogeneously by the inter-particle forces on the bases of the contact network topologies, is essential to the rigidity and stability in amorphous solids. How to capture such a "backbone" is crucial to the understanding of various anomalous properties or behaviors in those materials, which remains a central challenge presently in physics, engineering, or material science. Here, we use a novel graph neural network to predict the contact force network in two-dimensional granular materials under uniaxial compression. With the edge classification model in the framework of the deep graph library, we show that the inter-particle contact forces can be accurately estimated purely from the knowledge of the static microstructures, which can be acquired from a discrete element method or directly visualized from experimental methods. By testing the granular packings with different structural disorders and pressure, we further demonstrate the robustness of the optimized graph neural network to changes in various model parameters. Our research tries to provide a new way of extracting the information about the inter-particle forces, which substantially improves the efficiency and reduces the costs compared to the traditional experiments.

Thermo-Optic Response and Optical Bistablility of Integrated High-Index Doped Silica Ring Resonators.

The engineering of thermo-optic effects has found broad applications in integrated photonic devices, facilitating efficient light manipulation to achieve various functionalities. Here, we perform both an experimental characterization and a theoretical analysis of these effects in integrated microring resonators made from high-index doped silica, which have had many applications in integrated photonics and nonlinear optics. By fitting the experimental results with theory, we obtain fundamental parameters that characterize their thermo-optic performance, including the thermo-optic coefficient, the efficiency of the optically induced thermo-optic process, and the thermal conductivity. The characteristics of these parameters are compared to those of other materials commonly used for integrated photonic platforms, such as silicon, silicon nitride, and silica. These results offer a comprehensive insight into the thermo-optic properties of doped silica-based devices. Understanding these properties is essential for efficiently controlling and engineering them in many practical applications.

Boosted Binary Quantum Classifier via Graphical Kernel.

In terms of the logical structure of data in machine learning (ML), we apply a novel graphical encoding method in quantum computing to build the mapping between feature space of sample data and two-level nested graph state that presents a kind of multi-partite entanglement state. By implementing swap-test circuit on the graphical training states, a binary quantum classifier to large-scale test states is effectively realized in this paper. In addition, for the error classification caused by noise, we further explored the subsequent processing scheme by adjusting the weights so that a strong classifier is formed and its accuracy is greatly boosted. In this paper, the proposed boosting algorithm demonstrates superiority in certain aspects as demonstrated via experimental investigation. This work further enriches the theoretical foundation of quantum graph theory and quantum machine learning, which may be exploited to assist the classification of massive-data networks by entangling subgraphs.

Dictionary Learning Based Scheme for Adversarial Defense in Continuous-Variable Quantum Key Distribution.

There exist various attack strategies in continuous-variable quantum key distribution (CVQKD) system in practice. Due to the powerful information processing ability of neural networks, they are applied to the detection and classification of attack strategies in CVQKD systems. However, neural networks are vulnerable to adversarial attacks, resulting in the CVQKD system using neural networks also having security risks. To solve this problem, we propose a defense scheme for the CVQKD system. We first perform low-rank dimensionality reduction on the CVQKD system data through regularized self-representation-locality preserving projects (RSR-LPP) to filter out some adversarial disturbances, and then perform sparse coding reconstruction through dictionary learning to add data details and filter residual adversarial disturbances. We test the proposed defense algorithm in the CVQKD system. The results indicate that our proposed scheme has a good monitoring and alarm effect on CVQKD adversarial disturbances and has a better effect than other compared defense algorithms.

Multi-UAV Cooperative Trajectory Planning Based on the Modified Cheetah Optimization Algorithm.

The capacity for autonomous functionality serves as the fundamental ability and driving force for the cross-generational upgrading of unmanned aerial vehicles (UAVs). With the disruptive transformation of artificial intelligence technology, autonomous trajectory planning based on intelligent algorithms has emerged as a key technique for enhancing UAVs' capacity for autonomous behavior, thus holding significant research value. To address the challenges of UAV trajectory planning in complex 3D environments, this paper proposes a multi-UAV cooperative trajectory-planning method based on a Modified Cheetah Optimization (MCO) algorithm. Firstly, a spatiotemporal cooperative trajectory planning model is established, incorporating UAV-cooperative constraints and performance constraints. Evaluation criteria, including fuel consumption, altitude, and threat distribution field cost functions, are introduced. Then, based on its parent Cheetah Optimization (CO) algorithm, the MCO algorithm incorporates a logistic chaotic mapping strategy and an adaptive search agent strategy, thereby improving the home-returning mechanism. Finally, extensive simulation experiments are conducted using a considerably large test dataset containing functions with the following four characteristics: unimodal, multimodal, separable, and inseparable. Meanwhile, a strategy for dimensionality reduction searching is employed to solve the problem of autonomous trajectory planning in real-world scenarios. The results of a conducted simulation demonstrate that the MCO algorithm outperforms several other related algorithms, showcasing smaller trajectory costs, a faster convergence speed, and stabler performance. The proposed algorithm exhibits a certain degree of correctness, effectiveness, and advancement in solving the problem of multi-UAV cooperative trajectory planning.

Kalman filter-enabled parameter estimation for simultaneous quantum key distribution and classical communication scheme over a satellite-mediated link.

An accurate estimation of system parameters is of significance for the practical implementation of the simultaneous quantum key distribution and classical communication (SQCC) over a satellite-mediated link when considering the finite-size effect. In this paper, we propose a Kalman filter (KF)-enabled parameter estimation method for the SQCC over a satellite-mediated link. The fast and slow phase drift can be both estimated by using the improved vector KF carrier phase estimation algorithm, and thus the phase estimation error can be tracked in real time and be almost approximate to the theoretical mean square error limit. Taking advantage of the achieved phase estimation and the dual modulation of the SQCC scheme, the excess noise can be estimated with not only a higher precise but also a lower sacrificing rate of raw keys. Numerical simulations demonstrate the feasibility of the SQCC in both the downlink and uplink in terms of the finite-size effect. As a comparison of the Mth-power algorithm, we find that the secret key rate and achievable zenith angle perform better by using the vector KF algorithm. It paves the way of practical implementations for the SQCC system.

Continuous-variable quantum key distribution coexisting with classical signals on few-mode fiber.

Continuous-variable quantum key distribution (CVQKD) holds an advantage of well compatibility with classical coherent optical communications. However, there exists a performance trade-off between CVQKD and classical communication on single-mode fiber (SMF) because of the spontaneous Raman scattering. Space-division multiplexing (SDM) technique may provide a feasible way to mitigate this performance trade-off in short-distance communication while CVQKD coexisting with classical signals on few-mode fiber (FMF). Here, we examine the feasibility of CVQKD coexisting with classical signals on FMF and analyze the noise impact in weak coupling regime. We find that the inter-mode crosstalk generated from the mode coupling and re-coupling between modes and the group delay spread originated from the differential group delay (DGD) contribute the main noise sources. DGD may become one of the main limits for FMF-based CVQKD towards high-speed system. In addition, a well channel wavelength management is needed to suppress the inter-mode four-wave-mixing for achieving the positive secret key rates. The numerical simulations identify the key parameters for CVQKD system, enabling a helpful insight for realizing security analysis of the Gaussian modulated coherent state protocol. It shows that CVQKD coexisting with high power classical signals on FMF is feasible to implement with standard telecommunication components and able to operate at higher secret key rates. The results may provide a potential guideline for the practical high-rate CVQKD integrating with the FMF-based configuration.

A structural approach to vibrational properties ranging from crystals to disordered systems.

Many scientists generally attribute the vibrational anomalies of disordered solids to the structural disorder, which, however, is still under intense debate. Here we conduct simulations on two-dimensional packings with a finite temperature, whose structure is tuned from a crystalline configuration to an amorphous one, then the amorphous from very dense state to a relatively loose state. By measuring the vibrational density of states and the reduced density of states, we clearly observe the evolution of the boson peak with the change of the disorder and volume fractions. Meanwhile, to understand the structural origin of this anomaly, we identify the soft regimes of all systems with a novel machine-learning method, where the "softness", a local structural quantity, is defined. Interestingly, we find a strong monotonic relationship between the shape of the boson peak and the softness as well as its spatial heterogeneity, suggesting that the softness of a system may be a new structural approach to the anomalous vibrational properties of amorphous solids.

Indoor channel modeling for continuous variable quantum key distribution in the terahertz band.

Continuous-variable quantum key distribution (CVQKD) in an indoor scenario can provide secure wireless access for practical short-distance communications with high rates. However, a suitable channel model for implementing the indoor CVQKD system has not been considered before. Here, we establish an indoor channel model to show the feasibility of CVQKD in terahertz (THz) band. We adopt both active and passive state preparation schemes to demonstrate the performance of the indoor CVQKD system involving multi-path propagation. We achieve the channel transmittance characterized by frequency, water-vapor density, antenna gain, reflection loss and the surrounding itself. The ray-tracing based numerical simulations show that the multi-path propagation can degrade the performance of the indoor CVQKD system. The maximum transmission distance is two meters at 410 GHz for both active and passive state preparations, and it can be extended to 35 and 20 meters respectively by using high gain antenna to combat the multi-path propagation.

3D Hierarchical Nanorod@Nanobowl Array Photoanode with a Tunable Light-Trapping Cutoff and Bottom-Selective Field Enhancement for Efficient Solar Water Splitting.

Constructing 3D nanophotonic structures is regarded as an effective means to realize both efficient light absorption and efficient charge separation. However, most of the 3D structures reported so far enhance light trapping beyond the absorption onset wavelength, and thus greatly attentuate or even completely block the long-wavelength light, which could otherwise be efficiently absorbed by narrow-bandgap materials in a Z-scheme or tandem device. In addition, constructing a 3D conductive substrate often involves complex processes causing increased cost and upscaling problems. To overcome these shortcomings, a novel 3D hematite nanorod@nanobowl array nanophotonic structure is designed and fabricated by a low-cost method. This unique structure can enhance light absorption with tunable cutoffs and rationally concentrate photons right above the bowl bottom, enabling efficient charge separation. By loading NiFeOx as a cocatalyst, a high photocurrent density of 3.41 ± 0.2 mA cm-2 at 1.23 V versus reversible hydrogen electrode (RHE) can be obtained, which is 2.35 times that with a planar structure in otherwise the same system.

Optical frequency comb-based multichannel parallel continuous-variable quantum key distribution.

Continuous-variable quantum key distribution (CVQKD) provides an approach for secure communication in optical fiber communication systems. However, its practical implementation has been hindered by low secret key bit rates that are usually limited to several bits/s to hundreds of kbits/s at distances of more than 25 kilometers. In this paper, we use a pair of optical frequency combs (OFCs) for both multiple parallel transmission and coherent reception, which assign multiple sub-channels involving multiple independent secret keys in a single fiber to increase the key bit rate. The first and last sub-channels are selected for propagating phase references to compensate the phase offset between two free-running combs. We analyze possible excess noise caused by dispersive walk-off in the transmission, imperfect phase compensation in the reception and photon leakage from the phase references. Compared to the previous single-channel CVQKD method, simulation results show more than a factor of 20 increase in the secret key rate at a transmission distance of 35 km and the number of comb lines of 35.

Phase noise estimation using Bayesian inference for continuous-variable quantum key distribution.

Excess noise induced by the phase drifts is a serious impairment for the continuous-variable quantum key distribution with locally generated local oscillator scheme, which is recently proposed to avoid the side channel attacks due to the transmitted local oscillator. Theoretical and experimental studies on the phase estimation have been widely reported, while two frequency-locked laser sources are indispensable to achieve quantum coherent detection. Moreover, the self-referenced phase estimation scheme requires to propagate the strong reference pulse through optical fiber, which opens a security loophole through the manipulation of the reference pulse amplitude. Based on the theoretical security and Bayes' theorem, we propose a phase estimation protocol, which does not require propagating the strong reference pulse for performing phase estimation. Compared to the other related work, the protocol can avoid the security problem caused by strong reference pulse. Moreover, this algorithm is an iterative progress for each of experiment to obtain the phase estimation and its uncertainty. We hope the proposed scheme could further promote the performance of continuous-variable quantum key distribution.

Improvement of self-referenced continuous-variable quantum key distribution with quantum photon catalysis.

Quantum photon-catalysis operations can be utilized for improving the performance of continuous-variable quantum key distribution (CVQKD) systems. Motivated by characteristics of quantum photon-catalysis operations that can be implemented by the existing technologies, we consider the performance improvement of self-referenced (SR) CVQKD involving zero-photon catalysis operation. We find that the zero-photon catalysis can be regarded as a noiseless attenuation, and the numerical simulations show that the zero-photon catalysis (ZPC)-based SR-CVQKD scheme outperforms the original SR-CVQKD scheme. In addition, to highlight the advantage of applying zero-photon catalysis operation into SR-CVQKD systems, we make a comparison about the performances between the ZPC-based SR-CVQKD scheme and the previous single-photon subtraction (SPS)-based SR-CVQKD scheme. Numerical simulations show that the ZPC-based SR-CVQKD is superior to the single-photon subtraction case with respect to the transmission distance and the tolerable excess noise. Especially, the ZPC-based SR-CVQKD allows the lower quantum detection efficiency and the higher electronic noise to achieve the same performance. These results show that the proposed protocol is expected to provide theoretical reference for the practical application of SR-CVQKD in metropolitan areas.

Freeing the Polarons to Facilitate Charge Transport in BiVO4 from Oxygen Vacancies with an Oxidative 2D Precursor.

The BiVO4 photoelectrochemical (PEC) electrode in tandem with a photovoltaic (PV) cell has shown great potential to become a compact and cost-efficient device for solar hydrogen generation. However, the PEC part is still facing problems such as the poor charge transport efficiency owing to the drag of oxygen vacancy bound polarons. In the present work, to effectively suppress oxygen vacancy formation, a new route has been developed to synthesize BiVO4 photoanodes by using a highly oxidative two-dimensional (2D) precursor, bismuth oxyiodate (BiOIO3 ), as an internal oxidant. With the reduced defects, namely the oxygen vacancies, the bound polarons were released, enabling a fast charge transport inside BiVO4 and doubling the performance in tandem devices based on the oxygen vacancy eliminated BiVO4 . This work is a new avenue for elaborately designing the precursor and breaking the limitation of charge transport for highly efficient PEC-PV solar fuel devices.

Dual-phase-modulated plug-and-play measurement-device-independent continuous-variable quantum key distribution.

We suggest a novel scheme for measurement-device-independent (MDI) continuous-variable quantum key distribution (CVQKD) by integrating plug-and-play (PP) configuration with dual-phase modulation (DPM). With these techniques, MDI-CVQKD system has the ability to overcome a number of impractical problems with no extra performance penalty. In particular, the synchronous loophole of different lasers from Alice and Bob can be elegantly eliminated in the plug-and-play configuration, which gives birth to the convenient implementation when comparing to the Gaussian-modulated coherent-state protocol. Moreover, All LO-aimed attacks can be well defended since the local oscillator (LO) is locally generated. By taking advantage of DPM, the performance degeneration caused by the practical polarization-sensitive amplitude modulator can be eliminated. We also derive the security bounds for the proposed scheme against optimal Gaussian collective attacks. By taking the finite-size effect into account, we show that almost all raw keys generated by the proposed scheme can be exploited for the final secret key generation so that the secret key rate can be increased without sacrificing a part of raw keys for parameter estimation. In addition, we give an experimental concept of the proposed scheme which can be deemed guideline for final implementation.

Improving the Maximum Transmission Distance of Self-Referenced Continuous-Variable Quantum Key Distribution Using a Noiseless Linear Amplifier.

We show that a noiseless linear amplifier (NLA) can be placed properly at the receiver's end to improve the performance of self-referenced (SR) continuous variable quantum key distribution (CV-QKD) when the reference pulses are weak. In SR CV-QKD, the imperfections of the amplitude modulator limit the maximal amplitude of the reference pulses, while the performance of SR CV-QKD is positively related to the amplitude of the reference pulses. An NLA can compensate the impacts of large phase noise introduced by the weak reference pulses. Simulation results derived from collective attacks show that this scheme can improve the performance of SR CV-QKD with weak reference pulses, in terms of extending maximum transmission distance. An NLA with a gain of g can increase the maximum transmission distance by the equivalent of 20log10g dB of losses.

Monitoring of continuous-variable quantum key distribution system in real environment.

How to guarantee the practical security of continuous-variable quantum key distribution (CVQKD) system has been an important issue in the quantum cryptography applications. In contrast to the previous practical security strategies, which focus on the intercept-resend attack or the Gaussian attack, we investigate the practical security strategy based on a general attack, i.e., an arbitrated individual attack or collective attack on the system by Eve in this paper. The low bound of intensity disturbance of the local oscillator signal for eavesdropper successfully concealing herself is obtained, considering all noises can be used by Eve in the practical environment. Furthermore, we obtain an optimal monitoring condition for the practical CVQKD system so that legitimate communicators can monitor the general attack in real-time. As examples, practical security of two special systems, i.e., the Gaussian modulated coherent state CVQKD system and the middle-based CVQKD system, are investigated under the intercept-resend attacks.

Practical continuous-variable quantum key distribution without finite sampling bandwidth effects.

In a practical continuous-variable quantum key distribution system, finite sampling bandwidth of the employed analog-to-digital converter at the receiver's side may lead to inaccurate results of pulse peak sampling. Then, errors in the parameters estimation resulted. Subsequently, the system performance decreases and security loopholes are exposed to eavesdroppers. In this paper, we propose a novel data acquisition scheme which consists of two parts, i.e., a dynamic delay adjusting module and a statistical power feedback-control algorithm. The proposed scheme may improve dramatically the data acquisition precision of pulse peak sampling and remove the finite sampling bandwidth effects. Moreover, the optimal peak sampling position of a pulse signal can be dynamically calibrated through monitoring the change of the statistical power of the sampled data in the proposed scheme. This helps to resist against some practical attacks, such as the well-known local oscillator calibration attack.