Papers by Keyword: Quantum Computing

Abstract: Cursive Chinese calligraphy in Taiwan is a traditional yet still prevalent art form. Due to the high degree of variation and the fact that most cursive script is derived from historical documents, the available data is relatively limited, posing a significant challenge for AI (artificial intelligence) models. In this study, we combine quantum computing with diffusion models to generate images of Chinese cursive characters. Diffusion models have recently been proven to perform better than other generative models when working with small datasets. Moreover, the integration of quantum computing reduces training costs and enhances generation performance. The results in this paper demonstrate the potential of quantum computing in conjunction with generative AI, which is applicable to interdisciplinary needs in design, artistic creation, and cultural preservation. In future work, we will delve deeper into noise-related issues and propose possible solutions.

Abstract: The qubit technology using Trapped ions are taking the systems for practical quantum computing (QC). The normal requirements to achieve quantum supremacy have all been studied with ions, and quantum algorithms use ion-qubit systems have been implemented. I cover in this study many points regarding the concept of Qubit through Ion Trap, near application, and experiments also explore the Multi gates, Hybrid gates implementations

Abstract: In this work, quenching effect in the photoluminescence (PL) spectrum of divacancy defects in 4H SiC is investigated. Quenching in PL occurs when photoexcitation with an energy below a certain threshold is applied. In order to understand this phenomenon, we carried out Kohn-Sham density functional theory (DFT) calculations. In accordance with recent experimental results, we propose a physical model which explains the quenching phenomenon.

Abstract: Paramagnetic defects in solids have become attractive systems for quantum computing as well as magnetometry in recent years. One of the leading contenders is the negatively charged nitrogen-vacancy defect (NV center) in diamond proposed to be highly promising with respect the afore-mentioned applications. In our study we investigate the N_CV_Si defect in 3C, 4H and 6H SiC as alternative choices with superior properties. Electronic structure of NV center in SiC exhibits S = 1 triplet ground state with the possibility of optical spin polarization. On the other hand, our results obtained by density functional theory calculations may contribute to unambiguously identify the possible defect configurations.

Abstract: In this study, an important geometric transformation, multidimensional color image scaling based on an n-qubit normal arbitrary superposition state (NASS), is put forward. In order to reduce the complexity of implementation of image scaling in a quantum system, nearest neighbor interpolation algorithm is chosen to implement image scaling up. And the corresponding quantum circuit of implementation is proposed. Finally, we discuss measurements of the part qubits of a NASS state to realize image scaling down. The paper explores theoretical and practical aspects of image processing on a quantum computer.

Abstract: Combined with quantum computing and genetic algorithm, quantum genetic algorithm (QGA) shows considerable ability of parallelism. Experiments have shown that QGA performs quite well on TSP, job shop problem and some other typical combinatorial optimization problems. The other problems like nutritional diet which can be transformed into specific combinational optimization problem also can be solved through QGA smoothly. This paper sums up the main points of QGA for general combinatorial optimization problems. These points such as modeling of the problem, qubit decoding and rotation strategy are useful to enhance the convergence speed of QGA and avoid premature at the same time.

Abstract: A novel quantum multi-objective evolutionary algorithm is proposed that combine the quantum computing with multi-objective evolutionary algorithm, and the quantum chromosomes is updated with the chaos in order to enhance the optimization capability of the quantum population. To verify the performance of the proposed algorithm, the functions ZDT1 and ZDT2 are optimized by the proposed algorithm and NSGA-II. The results show that the quantum chaos multi-objective evolutionary algorithm has the more powerful capability. The new proposed algorithm is applied to the load distribution optimization of tandem cold mill, and the two-objective function modal is built based on the minimum energy consumption and rolling force equilibrium. Optimizing the modal with the new algorithm, the empirical data and method of weighting, the result of quantum chaos multi-objective evolutionary algorithm is more reasonable. Therefore, the quantum chaos multi-objective evolutionary algorithm is a practicable intelligent optimization method for the load distribution optimization of tandem cold mill.

Abstract: A quantum self-organization mapping networks model based on quantum neurons is presented in this paper. Both the input and the weight of the model are represented by the quantum bits, and the output of the model is represented by the real number. The model is composed of input layer and competitive layer. First, the samples are transformed into quantum states and are submitted to the input layer, and then the similar coefficients of quantum states are computed between the inputs and the weights. Secondly, the implicit pattern characters of the clustering samples are extracted in the competitive layer, and then the clustering results are showed. The quantum states of weights are updated by quantum rotation gates. The networks are trained by the algorithm combining the unsupervised learning and supervised learning together. Finally two experiments demonstrate that the model and algorithm are evidently superior to the general self-organization mapping networks.

Abstract: A hybrid algorithm for solving the vehicle routing problem is proposed based upon the combination of Ant Colony Optimization and quantum computing. The algorithm takes the advantage of the principles in quantum computing, such as the qubit, quantum gate, and the quantum superposition of states. It can search the best solution by quantum walk and can further improve the search capability of the algorithm for the best solution. Numerical examples are tested and verified, that show the good performances.

Abstract: A novel Ant Colony Optimization algorithm based on Quantum mechanism for Multi-objective traveling salesman problem (MQACO) is proposed. To improve algorithm performance we use self-adaptive operator, namely in prophase we use higher probability to explore more search space and to collect useful global information; otherwise in anaphase we use higher probability to accelerate convergence. We analyze the technology to improve algorithm performance. Self-adaptive algorithm has advantages in terms of the adaptability; reliability and the learning ability over traditional organizing algorithm. TSP benchmark instances Chn144 results demonstrate the superiority of MQACO by different parameter in this paper.