As an important branch of quantum secure multiparty computation, quantum private comparison (QPC) has attracted more and more attention recently. In this paper, according to the quantum implementation mechanism that these protocols used, we divide these protocols into three categories: The quantum cryptography QPC, the superdense coding QPC, and the entanglement swapping QPC. And then, a more in-depth analysis on the research progress, design idea, and substantive characteristics of corresponding QPC categories is carried out, respectively. Finally, the applications of QPC and quantum secure multi-party computation issues are discussed and, in addition, three possible research mainstream directions are pointed out

A quantum approximate optimization algorithm (QAOA) is a polynomial-time approximate optimization algorithm used to solve combinatorial optimization problems. However, the existing QAOA algorithms have poor generalization performance in finding an optimal solution from a feasible solution set of combinatorial problems. In order to solve this problem, a quantum approximate optimization algorithm with metalearning for the MaxCut problem (MetaQAOA) is proposed. Specifically, a quantum neural network (QNN) is constructed in the form of the parameterized quantum circuit to detect different topological phases of matter, and a classical long short-term memory (LSTM) neural network is used as a black-box optimizer, which can quickly assist QNN to find the approximate optimal QAOA parameters. The experiment simulation via TensorFlow Quantum (TFQ) shows that MetaQAOA requires fewer iterations to reach the threshold of the loss function, and the threshold of the loss value after training is smaller than comparison methods. In addition, our algorithm can learn parameter update heuristics which can generalize to larger system sizes and still outperform other initialization strategies of this scale.

Searchable encryption (SE) is a positive way to protect users sensitive data in cloud computing setting, while preserving search ability on the server side. SE allows the server to search encrypted data without leaking information about the plaintext data. In this paper, a multi-client universal circuit-based full-blind quantum computation (FBQC) model is proposed. In order to meet the requirements of multi-client accessing or computing encrypted cloud data, all clients with limited quantum ability outsource the key generation to a trusted key center and upload their encrypted data to the data center. Considering the feasibility of physical implementation , all quantum gates in the circuit are replaced with the combination of π/8 rotation operator set {Rz(π/4), Ry(π/4), CRz(π/4), CRy(π/4), CCRz(π/4), CCRy(π/4)}. In addition, the data center is only allowed to perform one π/8 rotation operator each time, but does not know the structure of the circuit (i.e., quantum computation), so it can guarantee the blindness of computation. Then, through combining this multi-client FBQC model and Grover searching algorithm, we continue to propose a quantum searchable encryption scheme for cloud data. It solves the problem of multi-client access mode under searchable encryption in the cloud environment, and has the ability to resist against some quantum attacks. To better demonstrate our scheme, an example of our scheme to search on encrypted 2-qubit state is given in detail. Furthermore, the security of our scheme is analysed from two aspects: external attacks and internal attacks, and the result indicates that it can resist against such kinds of attacks and also guarantee the blindness of data and computation.

In recent years, large language models have demonstrated strong sequence modeling capabilities in time seriesforecasting (TSF) across domains such as energy, traffic, andmeteorology. However, these models fail to capture the periodicityinherent in timestamp sequences, and do not consider resolutionaware weighting when aggregating multi-scale predictions, limiting their ability to capture complex temporal dynamics. Toaddress these challenges, we propose a large language modelbased time series predictor that integrates relative-periodic jointtemporal encoding with multi-resolution dynamic aggregation,enabling more accurate and robust temporal modeling acrossdiverse time scales. Specifically, we design a relative-periodictime encoding that integrates periodic patterns extracted throughautocorrelation analysis with relative temporal features, thus capturing both periodicity and temporal dependencies. Meanwhile,we apply two-stage non-overlapping window pooling to producemulti-resolution inputs and feed them into the LLM for forecasting. To effectively utilize the impact of different resolutions,a resolution-aware dynamic weight aggregation mechanism isproposed to adaptively adjust the contribution of each temporalresolution in the final prediction. Finally, the experiment resultsdemonstrate that our predictor achieves consistent improvementsover AutoTimes on seven datasets: ETTh1 (MSE reduced by3.9%, MAE by 1.4%), ETTh2 (MSE reduced by 5.0%, MAEby 1.5%), ETTm1 (MSE reduced by 0.6%, MAE by 1.1%),ETTm2 (MSE reduced by 2.2%, MAE by 1.2%), ECL (MSEreduced by 3.1%, MAE by 2.0%), Weather (MSE reduced by6.0%, MAE by 3.7%), and Solar (MSE reduced by 3.6%, MAEby 2.5%). Our predictor not only advances temporal encodingand multi-resolution forecasting with LLMs, but also points tonew directions for their application in time series prediction.

Quantum Secure Multi-party Summation (QSMS) is a fundamental primitive of Quantum Secure Multi-party Computation (QSMC), enabling multiple participants to jointly compute the sum of their private inputs without disclosing individual data. However, most existing QSMS protocols require all participants to possess full quantum capabilities and often rely on pre-shared keys, auxiliary mask transmission, or multiple trusted third parties, resulting in high communication overhead and limited practicality. To address these limitations, we propose an efficient Semi-Quantum Secure Multi-party Summation (SQSMS) protocol based on d-dimensional n-particle entangled states. By exploiting the global correlation properties of high-dimensional entangled states, the proposed protocol generates correlated random masks directly from quantum measurement outcomes. These masks cancel automatically during the aggregation process, eliminating the need for additional mask distribution and transmission. Compared with existing QSMS schemes, the proposed protocol reduces communication overhead, improves quantum efficiency, and avoids reliance on pre-shared keys or multiple trusted third parties. Moreover, only simple measurement operations are required from classical participants, making the protocol more practical for semi-quantum environments. We further provide formal correctness and security analyses of the proposed protocol and conduct quantum circuit simulations using the IBM Qiskit platform to demonstrate its feasibility. Moreover, based on the proposed summation protocol, we design several extended application protocols, including anonymous voting, anonymous auction, and anonymous ranking, which further illustrate the scalability and practical applicability of the proposed scheme.