As the traditional semiconductor complementary metal oxide semiconductor (CMOS) integrated circuit technology gradually approaches the limit of Moore's Law, quantum computing, as a new system computing technology,with the potential for higher computing speed and lower power consumption, is getting more and more attention from governments and research institutions around the world. For instance, the United States (US) government is adopting a bunch of bills to deploy new quantum information processing technology. The European Union's “quantum declaration" plans to realize customized quantum computers with more than 100 qubits in the next 10 years. The Chinese government also provides strong support for quantum computer research through the project of National Science and Technology Major Projects. In the business field, major companies such as International Business Machines (IBM) Corporation, Intel, Google, Alibaba, Huawei, Baidu, etc. , have joined the “quantum supremacy" competition in order to seize the initiative in the future information field. Facing the rapid development trend of quantum computing, we believe that we should refer to the classical computer industry in the early stage, form an industrial system, and develop the quantum computing industry. We should also use the scientific system engineering method to carry out the research and development of the quantum computer and establish the ecological environment of the quantum computer industry with the demand as the traction, so as to better serve the development of the national economy.

To solve polynomial systems, Harrow, Hassidim, and Lloyd (HHL) proposed a quantum algorithm called HHL algorithm. Based on the HHL algorithm, Chen et al. presented an algorithm, the solving the Boolean solutions of polynomial systems (PoSSoB) algorithm. Furthermore, Ding et al. introduced the Boolean Macaulay matrix and analyzed the lower bound on the condition number. Inspired by Ding et al. 's research, several related algorithms are proposed in this paper. First, the improved PoSSoB algorithm using the Boolean Macaulay matrix is proved to have lower complexity. Second, for solving equations with errors, a quantum algorithm for the max-polynomial system solving (Max-PoSSo) problem is proposed based on the improved PoSSoB algorithm. Besides, the Max-PoSSo algorithm is extended to the learning with errors (LWE) problem and its special case, the learning parity with noise (LPN) problem, providing a quantitative criterion, the condition number, for the security of these basic problems.

It is a critical challenge for quantum machine learning to classify the datasets accurately. This article develops a quantum classifier based on the isolated quantum system (QC-IQS) to classify nonlinear and multidimensional datasets. First, a model of QC-IQS is presented by creating parameterized quantum circuits (PQCs) based on the decomposing of unitary operators with the Hamiltonian in the isolated quantum system. Then, a parameterized quantum classification algorithm (QCA) is designed to calculate the classification results by updating the loss function until it converges. Finally, the experiments on nonlinear random number datasets and Iris datasets are designed to demonstrate that the QC-IQS model can handle and generate accurate classification results on different kinds of datasets. The experimental results reveal that the QC-IQS is adaptive and learnable to handle different types of data. Moreover, QC-IQS compensates the issue that the accuracy of previous quantum classifiers declines when dealing with diverse datasets. It promotes the process of novel data processing with quantum machine learning and has the potential for more comprehensive applications in the future.

Soft margin support vector machine (SVM) with hinge loss function is an important classification algorithm, which has been widely used in image recognition, text classification and so on. However, solving soft margin SVM with hinge loss function generally entails the sub-gradient projection algorithm, which is very time-consuming when processing big training data set. To achieve it, an efficient quantum algorithm is proposed. Specifically, this algorithm implements the key task of the sub-gradient projection algorithm to obtain the classical sub-gradients in each iteration, which is mainly based on quantum amplitude estimation and amplification algorithm and the controlled rotation operator. Compared with its classical counterpart, this algorithm has a quadratic speedup on the number of training data points. It is worth emphasizing that the optimal model parameters obtained by this algorithm are in the classical form rather than in the quantum state form. This enables the algorithm to classify new data at little cost when the optimal model parameters are determined.

The controlled quantum key agreement (CQKA) protocol requires a controller to oversee the process of all participants negotiating a key, which can satisfy the needs of certain specific scenarios. Existing CQKA protocols are mostly two-party or three-party, and they do not entirely meet the actual needs. To address this problem, this paper proposes new CQKA protocols based on Bell states and Bell measurements. The new CQKA protocols can be successfully implemented for any N-party, not just two-party. Furthermore, the security and efficiency analyses demonstrate that the new CQKA protocols are not only secure but also more efficient in terms of quantum bit.

Universality is an important property in software and hardware design. This paper concentrates on the universality of quantum secure multi-party computation (SMC) protocol. First of all, an in-depth study of universality has been onducted, and then a nearly universal protocol is proposed by using the Greenberger-Horne-Zeilinger (GHZ)-like state and stabilizer formalism. The protocol can resolve the quantum SMC problem which can be deduced as modulo subtraction, and the steps are simple and effective. Secondly, three quantum SMC protocols based on the proposed universal protocol: Quantum private comparison (QPC) protocol, quantum millionaire (QM) protocol, and quantum multi-party summation (QMS) protocol are presented. These protocols are given as examples to explain universality. Thirdly, analyses of the example protocols are shown. Concretely, the correctness, fairness, and efficiency are confirmed. And the proposed universal protocol meets security from the perspective of preventing inside attacks and outside attacks. Finally, the experimental results of the example protocols on the International Business Machines (IBM) quantum platform are consistent with the theoretical results. Our research indicates that our protocol is universal to a certain degree and easy to perform.

In modern society, it is necessary to perform some secure computations for private sets between different entities. For instance, two merchants desire to calculate the number of common customers and the total number of users without disclosing their own privacy. In order to solve the referred problem, a semi-quantum protocol for private computation of cardinalities of set based on Greenberger-Horne-Zeilinger (GHZ) states is proposed for the first time in this paper, where all the parties just perform single-particle measurement if necessary. With the assistance of semi-honest third party (TP), two semi-quantum participants can simultaneously obtain intersection cardinality and union cardinality. Furthermore, security analysis shows that the presented protocol can stand against some well-known quantum attacks, such as intercept measure resend attack, entangle measure attack. Compared with the existing quantum protocols of Private Set Intersection Cardinality (PSI-CA) and Private Set Union Cardinality (PSU-CA), the complicated oracle operations and powerful quantum capacities are not required in the proposed protocol. Therefore, it seems more appropriate to implement this protocol with current technology.

Adaptive learning paths provide individual learning objectives that best match a learner's characteristics. This is especially helpful when learners need to balance limited available learning time and multiple learning objectives. The automatic generation of personalized learning paths to improve learning efficiency has therefore attracted significant interest. However, most current research only focuses on providing learners with adaptive objects and sequences according to their own interests or learning goals given a normal amount of time or ordinary conditions. There is little research that can help learners to obtain the most important knowledge for a test in the shortest time possible, which is a typical scenario in exanimation-oriented education systems. This study aims to solve this problem by introducing a new approach that builds on existing methods. First, the eight properties in Gardner's multiple intelligence theory are introduced into the present knowledge and learner models to define the relationship between learning objects (LOs) and learners, thereby improving recommendation accuracy rates. Then, a novel adaptive learning path recommendation model is presented where viable knowledge topologies, knowledge bases and the previously-established properties relating to a learner's ability are combined by Dempster-Shafer (D-S) evidence theory. A series of practical experiments were performed to assess the approach's adaptability, the appropriateness of the selected evidence and the effectiveness of the recommendations. In the results, it was found that the proposed learning path recommendation model helped learners learn the most important elements and obtain superior test grades when confronted with limited time for learning.

Personalized recommender systems provide various personalized recommendations for different users through the analysis of their respective historical data. Currently, the problem of the “filter bubble" which has to do with over-specialization persists. Serendipity (SRDP), one of the evaluation indicators, can provide users with unexpected and useful recommendations, and help to successfully mitigate the filter bubble problem, and enhance users' satisfaction levels and provide them with diverse recommendations. Since SRDP is highly subjective and challenging to study, only a few studies have focused on it in recent years. In this study, the research results on SRDP were summarized, the various definitions of SRDP and its applications were discussed, the specific SRDP calculation process from qualitative to quantitative perspectives was presented, the challenges and the development directions were outlined to provide a framework for further research.

This paper focuses on gesture recognition and interactive lighting control. The collection of gesture data adopts the Myo armband to obtain surface electromyography (sEMG). Considering that many factors affect sEMG, a customized classifier based on user calibration data is used for gesture recognition. In this paper, machine learning classifiers k-nearest neighbor (KNN), support vector machines (SVM), and naive Bayesian (NB) classifier, which can be used in small sample sets, are selected to classify four gesture actions. The performance of the three classifiers under different training parameters, different input features, including root mean square (RMS), mean absolute value (MAV), waveform length (WL), slope sign change (SSC) number, zero crossing (ZC) number, and variance (VAR) are tested, and different input channels are also tested. Experimental results show that: The NB classifier, which assumes that the prior probability of features is polynomial distribution, has the best performance, reaching more than 95% accuracy. Finally, an interactive stage lighting control system based on Myo armband gesture recognition is implemented.