No abstract

We propose a scalable scheme to generate a multiqubit conditional phase gate by using a basic building block, i.e., a weak coherent optical pulse |𝛼⟩ reflected successively from a cavity with trapped atoms. In the scheme, we use a coherent state of light instead of a single photon source, homodyne measurement on a coherent light field instead of single photon detection, which reduces the complexity of the practical experiment. The outcomes of these measurements indicate either completion of the gate or the presence of the original qubits such that the operation can be repeated until it is successful.

This paper proposes a scalable scheme to generate n-atom GHZ states and cluster states by using the basic building block, i.e., a weak coherent optical pulse |α being reflected successively from a single-atom cavity. In the schemes, coherent state of light is used instead of single photon source, homodyne measurement on coherent light is done instead of single photon detection, and no need for individually addressing keeps the schemes easy to implement from the experimental point of view. The successful probabilities of our protocols approach unity in the ideal case.

This paper presents a direct implementation scheme of the non-local multi-qubit controlled phase gate by using optical fibres and adiabatic passage. The smaller operation number for implementing the multi-qubit controlled phase gate and needlessness for addressing individually save physical resource and lower the difficulties of experiment. Meanwhile, the scheme is immune from some decoherence effects such as the atomic spontaneous emission and fibre loss. In principle, it is scalable.

To achieve successful investments, in addition to financial expertise and knowledge of market information, a further critical factor is an individual’s personality. Decisive people tend to be able to quickly judge when to invest, while calm people can analyze the current situation more carefully and make appropriate decisions. Therefore, in this study, we developed a multimodal personality-recognition system to understand investors’ personality traits. The system analyzes the personality traits of investors when they share their investment experiences and plans, allowing them to understand their own personality traits before investing. To perform system functions, we collected digital human behavior data through video-recording devices and extracted human behavior features using video, speech, and text data. We then used data fusion to fuse human behavior features from heterogeneous data to address the problem of learning only one-sided information from a single modality. Through several experiments, we demonstrated that multimodal (i.e., three different signal inputs) personality trait analysis is more accurate than unimodal models. We also used statistical methods and questionnaires to evaluate the correlation between the investor’s personality traits and risk tolerance. It was found that investors with higher openness, extraversion, and lower neuroticism personality traits took higher risks, which is similar to research findings in the field of behavioral finance. Experimental results show that, in a case study, our multimodal personality prediction system exhibits high performance with highly accurate prediction scores in various metrics.