Experimental quantum adversarial learning with programmable superconducting qubits

Quantum computing promises to enhance machine learning and artificial intelligence. Different quantum algorithms have been proposed to improve a wide spectrum of machine learning tasks. Yet, recent theoretical works show that, similar to traditional classifiers based on deep classical neural networks, quantum classifiers would suffer from the vulnerability problem: adding tiny carefully-crafted perturbations to the legitimate original data samples would facilitate incorrect predictions at a notably high confidence level. This will pose serious problems for future quantum machine learning applications in safety and security-critical scenarios. Here, we report the first experimental demonstration of quantum adversarial learning with programmable superconducting qubits. We train quantum classifiers, which are built upon variational quantum circuits consisting of ten transmon qubits featuring average lifetimes of 150 $\mu$s, and average fidelities of simultaneous single- and two-qubit gates above 99.94% and 99.4% respectively, with both real-life images (e.g., medical magnetic resonance imaging scans) and quantum data. We demonstrate that these well-trained classifiers (with testing accuracy up to 99%) can be practically deceived by small adversarial perturbations, whereas an adversarial training process would significantly enhance their robustness to such perturbations. Our results reveal experimentally a crucial vulnerability aspect of quantum learning systems under adversarial scenarios and demonstrate an effective defense strategy against adversarial attacks, which provide a valuable guide for quantum artificial intelligence applications with both near-term and future quantum devices.Comment: 26 pages, 17 figures, 8 algorithm

One-step Predictive Encoder - Gaussian Segment Model for Time Series Anomaly Detection

Unsupervised anomaly detection for time series is of great importance for various applications, such as Web monitoring, medical monitoring, and device fault diagnosis. Time series anomaly detection (TSAD) aims to find the observations that most different from others in a sequence of observations. With the development of deep learning, deep-autoencoder-based methods achieve state-of-the-art performance. These methods are usually able to find single anomaly points but fail to detect the anomaly segment and the change point. To tackle this problem, this paper proposes a novel TSAD method, which consists of a bidirectional LSTM (BiLSTM) autoencoder and a subsequent Gaussian segmentation model. BiLSTM encodes a time series in a predictive format from both positive and negative time directions, then outputs the latent feature vectors and restructured errors. After that, the latent features are used to find anomaly segments by the Gaussian segment model; the restructured errors are used to find change points and extreme single anomaly by a scoring function. In this way, our method can find all three kinds of anomaly points. Experiments on two real-world datasets demonstrate the effectiveness of the proposed method

Modular Synthesis of Functionalized Butenolides by Oxidative Furan Fragmentation

The development of new chemical transformations to simplify the synthesis of valuable building blocks is a challenging task in organic chemistry and has been the focus of considerable research effort. From a synthetic perspective, it would be ideal if the natural reactivities of feedstock chemicals could be diverted to the production of high value-added compounds which are otherwise tedious to prepare. Here we report a chemical transformation that enables facile and modular synthesis of synthetically challenging yet biologically important functionalized butenolides from easily accessible furans. Specifically, Diels–Alder reactions between furans and singlet oxygen generate versatile hydroperoxide intermediates, which undergo iron(II)-mediated radical fragmentation in the presence of Cu(OAc)2 or various radical trapping reagents to afford butenolides bearing a wide variety of appended remote functional groups, including olefins, halides, azides and aldehydes. The practical utility of this transformation is demonstrated by easy diversification of the products by means of cross-coupling reactions and, most importantly, by its ability to simplify the syntheses of known building blocks of eight biologically active natural products

Study of Temperature Effects on the Design of Active Region for 808 nm High-Power Semiconductor Laser

High-power, broad-area, semiconductor lasers are attractive sources for material processing, aerospace, and laser pumping. The design of the active region is crucial to achieve the required high power and electro-optical conversion efficiency, since the temperature significantly affects the performance of the quantum well, including the internal quantum efficiency and mode gain. In this work, the temperature effects on the active region of a 808 nm high-power semiconductor laser were investigated theoretically and experimentally. The simulations were performed with a Quasi-3D model, which involved complete steady-state semiconductor and carrier confinement efficiency combined with a new mathematical method. The critical aluminum content of the quantum barrier was proposed and the relationship between temperature and various loss sources was disclosed in the temperature range of 213 to 333 K, which provides a reliable reference for the design of epitaxial structures of high-power semiconductor lasers in different operating conditions. Subsequently, the optimized epitaxial structure was determined and used to fabricate standard laser bar chips with a cavity length of 2 mm. The experimental electro-optical conversion efficiency of 71% was demonstrated with a slope efficiency of 1.34 W/A and an injection current of 600 A at the heatsink temperature of 223 K. A record high electro-optical conversion efficiency of 73.5% was reached at the injection current of 400 A, while the carrier confinement efficiency was as high as 98%

Design of Backstepping Control Based on a Softsign Linear–Nonlinear Tracking Differentiator for an Electro-Optical Tracking System

To address the problems of a low tracking accuracy and slow error convergence in high-order single-input, single-output electro-optical tracking systems, a backstepping control method based on a Softsign linear–nonlinear tracking differentiator is proposed. First, a linear–nonlinear tracking differentiator is designed in conjunction with the Softsign excitation function, using its output as an approximate replacement for the conventional differentiation process. Then, this is combined with backstepping control to eliminate the “explosion of complexity” problem in conventional backstepping procedures due to repeated derivation of virtual control quantities. This reduces the workload of parameter tuning, takes into account the rapidity and stability of signal convergence, and improves the trajectory tracking performance. This method can ensure the boundedness of the system signal. The effectiveness and superiority of this control method are verified through simulations and experiments

Fuzzy Synthetic Evaluation of the Critical Success Factors for the Sustainability of Public Private Partnership Projects in China

Public Private Partnership (PPP) projects have attracted wide attention from academia and industry over the past 20 years, however, they have been plagued by certain factors. This study identified, classified, and evaluated the success factors that may affect PPP projects for achieving sustainability. First, a list of 32 critical success factors were categorized into 3 groups, then a questionnaire survey was conducted, with 108 responses received from experts, researchers, and PPP project managers in China. Second, using a fuzzy synthetic evaluation (FSE) method, stakeholder relationships (A1–A10), external environmental (B1–B8), and project management of a special purpose vehicle (C1–C14) collected data at three different factor group locations in PPP projects were used in this evaluation. The results obtained nine top factors: private sector financing capacity, government credit, government commitment or guarantee, completeness of legal framework, available financial markets, the feasibility study report and implementation, effectiveness of risk management, project investment, and cost control and revenue distribution. It was demonstrated that fuzzy synthetic evaluation techniques are quite appropriate techniques for PPP projects. The research findings should impact on policy development towards PPP and Private Finance Initiative (PFI) project governance