Quantum walks are powerful kernels in quantum computing protocols that possess strong capabilities in speeding up various simulation and optimisation tasks. One striking example is given by quantum walkers evolving on glued trees for their faster hitting performances than in the case of classical random walks. However, its experimental implementation is challenging as it involves highly complex arrangements of exponentially increasing number of nodes. Here we propose an alternative structure with a polynomially increasing number of nodes. We successfully map such graphs on quantum photonic chips using femtosecond laser direct writing techniques in a geometrically scalable fashion. We experimentally demonstrate quantum fast hitting by implementing two-dimensional quantum walks on these graphs with up to 160 nodes and a depth of 8 layers, achieving a linear relationship between the optimal hitting time and the network depth. Our results open up a scalable way towards quantum speed-up in complex problems classically intractable.Adapting well-known classical mathematical models in a way to include quantum mechanical laws has shown the emergence of new interesting behaviors. In some cases, the modified protocols have revealed an advantage with respect to the original ones in solving specific problems. This has clearly triggered the interest of the scientific community in the quest for a better understanding and exploitation of these new tools 1 . A striking example is given by quantum walks, the adaptation of the classical random walk to the world of quantum mechanics 2 . Quantum walks have already found applications in several scenarios, including spatial search problems 3,4 , the element distinctness problem 5 , testing matrix identities 6 , evaluating Boolean formulas 7 , judging graph isomorphism 8,9 , which all theoretically promise quantum speed-up and may inspire the breakthrough in real-life applications.One feature of quantum walks on complex graphs that is key in quantum algorithms is their ability to propagate from a node to a distant one in an efficient way. This is often denoted as fast hitting. In particular, fast hitting on a structure known as glued tree is extremely charming due to its exponential speed-up over its classical counterpart 10,11 . A glued tree is obtained by connecting the "final leaves" of two binary tree graphs 12 of the same depth, as shown in Fig.1(a). The process assumes a particle starting in the left-most vertex (called the Entry site), evolving through the graph, and finally hitting the right-most vertex (called the Exit site). It has been shown that, in a scenario where the central connections are randomly chosen, any algorithm exploiting a classical walker (i.e., a particle following the laws of classical mechanics) would require on average a time scaling exponentially with the graph depth to reach the Exit. On the other hand, a quantum walker will require a time that scales only linearly 11,13,14 . Due to the close relation between binary trees and decision trees in computer s...

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With the increasing crossover between quantum information and machine learning, quantum simulation of neural networks has drawn unprecedentedly strong attention, especially for the simulation of associative memory in Hopfield neural networks due to their wide applications and relatively simple structures that allow for easier mapping to the quantum regime. Quantum stochastic walk, a strikingly powerful tool to analyze quantum dynamics, has been recently proposed to simulate the firing pattern and associative memory with a dependence on Hamming Distance. We successfully map the theoretical scheme into a three-dimensional photonic quantum chip and realize quantum stochastic walk evolution through well-controlled detunings of the propagation constant. We demonstrate a good match rate of the associative memory between the experimental quantum scheme and the expected result for Hopfield neural networks. The ability of quantum simulation for an important feature of a neural network, combined with the scalability of our approach through low-loss integrated chip and straightforward Hamiltonian engineering, provides a primary but steady step towards photonic artificial intelligence devices for optimization and computation tasks of greatly improved efficiencies.

CAPTCHA, or Completely Automated Public Turing Tests to Tell Computers and Humans Apart, is a common mechanism used to protect commercial accounts from malicious computer bots, and the most widely used scheme is text-based CAPTCHA. In recent years, newly emerged deep learning techniques have achieved high accuracy and speed in attacking text-based CAPTCHAs. However, most of the existing attacks have various disadvantages, the attack process made high complexity or manually collecting and labeling a large number of samples to train a deep learning recognition model is time-consuming and expensive. In this paper, we propose a transfer learning-based approach that greatly reduces the attack complexity and the cost of labeling samples, specifically, by pre-training the model with randomly generated samples and fine-tuning the pre-trained model with a small number of real-world samples. To evaluate our attack, we tested 25 online CAPTCHAs achieving success rates ranging from 36.3% to 96.9%. To further explore the effect of the training sample characteristics on the attack accuracy, we elaborately imitate some samples and apply a generative adversarial network to refine the samples, sequentially we use these two kinds of generated samples to pre-train the models, respectively. The experimental results demonstrate that the similarity between randomly generated samples and elaborately imitated samples has a negligible impact on the attack accuracy. Instead, transfer learning is the key factor; it reduces the cost of data preparation while preserving the model's attack accuracy. INDEX TERMS CAPTCHA, security, deep learning, transfer learning. I. INTRODUCTION

Quantum walks on graphs play an important role in the field of quantum algorithms. Fast hitting is one of the properties that quantum walk algorithms can utilize to outperform classical random walk algorithms. Fast hitting refers to a particle starting from the entrance node on a graph and trying to hit the exit node quickly. Especially, continuous-time quantum walks on random glued binary trees have been investigated in theories extensively for their exponentially faster hitting speed over classical random walks. Here, using heralded single photons to represent quantum walkers and laser-written waveguide arrays to simulate the theoretical graph, we are able to demonstrate the hitting efficiency of quantum walks with tree depth as high as 16 layers for the first time. Furthermore, we expand the graph’s branching rate from 2 to 5, revealing that quantum walks can exhibit more superiority over classical random walks as the branching rate increases. Our results may shed light on the physical implementation of quantum walk algorithms as well as quantum computation and quantum simulation.

Propaganda is a rhetorical technique designed to serve a specific topic, which is often used purposefully in news article to achieve our intended purpose because of its specific psychological effect. Therefore, it is significant to be clear where and what propaganda techniques are used in the news for people to understand its theme efficiently during our daily lives. Recently, some relevant researches are proposed for propaganda detection but unsatisfactorily. As a result, detection of propaganda techniques in news articles is badly in need of research. In this paper, we are going to introduce our systems for detection of propaganda techniques in news articles, which is split into two tasks, Span Identification and Technique Classification. For these two tasks, we design a system based on the popular pretrained BERT model, respectively. Furthermore, we adopt the over-sampling and EDA strategies, propose a sentence-level feature concatenating method in our systems. Experiments on the dataset of about 550 news articles offered by SEMEVAL show that our systems perform state-of-the-art.

Dynamic localization, which originates from the phenomena of particle evolution suppression under an externally applied AC electric field, has been simulated by suppressed light evolution in periodically curved photonic arrays. However, experimental studies on their quantitative dynamic transport properties and application for quantum information processing are rare. Here we fabricate one-dimensional and hexagonal two-dimensional arrays both with sinusoidal curvatures. We successfully observe the suppressed single-photon evolution patterns, and for the first time, to the best of our knowledge, measure the variances to study their transport properties. For one-dimensional arrays, the measured variances match both the analytical electric-field calculation and the quantum walk Hamiltonian engineering approach. For hexagonal arrays as anisotropic effective couplings in four directions are mutually dependent, the analytical approach suffers, whereas quantum walk conveniently incorporates all anisotropic coupling coefficients in the Hamiltonian and solves its exponential as a whole, yielding consistent variances with our experimental results. Furthermore, we implement a nearly complete localization to show that it can preserve both the initial injection and the wave packet after some evolution, acting as a memory of a flexible time scale in integrated photonics. We demonstrate a useful quantum simulation of dynamic localization for studying their anisotropic transport properties and a promising application of dynamic localization as a building block for quantum information processing in integrated photonics.

With the development of deep learning technologies, object detection algorithms have made significant progress in terms of detection speed and detection performance. However, the detection speed of current detection networks still does not meet the requirements of real-world applications in some scenarios. In this paper, we propose a faster non-maximum suppression (FNMS) algorithm that reduces the processing time by a large margin while achieving the same detection precision compared with the traditional non-maximum suppression (NMS) algorithm. Moreover, an attempt is made to adopt additional lightweight network structures to improve the speed of the detection network. By combining our FNMS algorithm with other network optimization strategies, we are able to improve the detection speed of YOLO v3 on the DOTA dataset by 165%.