Quantum Secure Multi-Party Summation with Graph State.

Quantum secure multi-party summation (QSMS) is a fundamental problem in quantum secure multi-party computation (QSMC), wherein multiple parties compute the sum of their data without revealing them. This paper proposes a novel QSMS protocol based on graph state, which offers enhanced security, usability, and flexibility compared to existing methods. The protocol leverages the structural advantages of graph state and employs random graph state structures and random encryption gate operations to provide stronger security. Additionally, the stabilizer of the graph state is utilized to detect eavesdroppers and channel noise without the need for decoy bits. The protocol allows for the arbitrary addition and deletion of participants, enabling greater flexibility. Experimental verification is conducted to demonstrate the security, effectiveness, and practicality of the proposed protocols. The correctness and security of the protocols are formally proven. The QSMS method based on graph state introduces new opportunities for QSMC. It highlights the potential of leveraging quantum graph state technology to securely and efficiently solve various multi-party computation problems.

Role of Phosphorus-Containing Molecules on the Formation of Nano-Sized Calcium Phosphate for Bone Therapy.

Calcium phosphate (CaP) is the principal inorganic constituent of bone and teeth in vertebrates and has various applications in biomedical areas. Among various types of CaPs, amorphous calcium phosphate (ACP) is considered to have superior bioactivity and biodegradability. With regard to the instability of ACP, the phosphorus-containing molecules are usually adopted to solve this issue, but the specific roles of the molecules in the formation of nano-sized CaP have not been clearly clarified yet. Herein, alendronate, cyclophosphamide, zoledronate, and foscarnet are selected as the model molecules, and theoretical calculations were performed to elucidate the interaction between calcium ions and different model molecules. Subsequently, CaPs were prepared with the addition of the phosphorus-containing molecules. It is found that cyclophosphamide has limited influence on the generation of CaPs due to their weak interaction. During the co-precipitation process of Ca2+ and PO4 3-, the competitive relation among alendronate, zoledronate, and foscarnet plays critical roles in the produced inorganic-organic complex. Moreover, the biocompatibility of CaPs was also systematically evaluated. The DFT calculation provides a convincing strategy for predicting the structure of CaPs with various additives. This work is promising for designing CaP-based multifunctional drug delivery systems and tissue engineering materials.

Quantum-Based Creative Generation Method for a Dancing Robot.

In this paper, we propose a creative generation process model based on the quantum modeling simulation method. This model is mainly aimed at generating the running trajectory of a dancing robot and the execution plan of the dancing action. First, we used digital twin technology to establish data mapping between the robot and the computer simulation environment to realize intelligent controllability of the robot's trajectory and the dance movements described in this paper. Second, we conducted many experiments and carried out a lot of research into information retrieval, information fidelity, and result evaluation. We constructed a multilevel three-dimensional spatial quantum knowledge map (M-3DQKG) based on the coherence and entangled states of quantum modeling and simulation. Combined with dance videos, we used regions with convolutional neural networks (R-CNNs) to extract character bones and movement features to form a movement library. We used M-3DQKG to quickly retrieve information from the knowledge base, action library, and database, and then the system generated action models through a holistically nested edge detection (HED) network. The system then rendered scenes that matched the actions through generative adversarial networks (GANs). Finally, the scene and dance movements were integrated, and the creative generation process was completed. This paper also proposes the creativity generation coefficient as a means of evaluating the results of the creative process, combined with artificial brain electroenchalographic data to assist in evaluating the degree of agreement between creativity and needs. This paper aims to realize the automation and intelligence of the creative generation process and improve the creative generation effect and usability of dance movements. Experiments show that this paper has significantly improved the efficiency of knowledge retrieval and the accuracy of knowledge acquisition, and can generate unique and practical dance moves. The robot's trajectory is novel and changeable, and can meet the needs of dance performances in different scenes. The creative generation process of dancing robots combined with deep learning and quantum technology is a required field for future development, and could provide a considerable boost to the progress of human society.

Key Technology of Real-Time Road Navigation Method Based on Intelligent Data Research.

The effect of traffic flow prediction plays an important role in routing selection. Traditional traffic flow forecasting methods mainly include linear, nonlinear, neural network, and Time Series Analysis method. However, all of them have some shortcomings. This paper analyzes the existing algorithms on traffic flow prediction and characteristics of city traffic flow and proposes a road traffic flow prediction method based on transfer probability. This method first analyzes the transfer probability of upstream of the target road and then makes the prediction of the traffic flow at the next time by using the traffic flow equation. Newton Interior-Point Method is used to obtain the optimal value of parameters. Finally, it uses the proposed model to predict the traffic flow at the next time. By comparing the existing prediction methods, the proposed model has proven to have good performance. It can fast get the optimal value of parameters faster and has higher prediction accuracy, which can be used to make real-time traffic flow prediction.