Entanglement-guided architectures of machine learning by quantum tensor network

It is a fundamental, but still elusive question whether the schemes based on quantum mechanics, in particular on quantum entanglement, can be used for classical information processing and machine learning. Even partial answer to this question would bring important insights to both fields of machine learning and quantum mechanics. In this work, we implement simple numerical experiments, related to pattern/images classification, in which we represent the classifiers by many-qubit quantum states written in the matrix product states (MPS). Classical machine learning algorithm is applied to these quantum states to learn the classical data. We explicitly show how quantum entanglement (i.e., single-site and bipartite entanglement) can emerge in such represented images. Entanglement characterizes here the importance of data, and such information are practically used to guide the architecture of MPS, and improve the efficiency. The number of needed qubits can be reduced to less than 1/10 of the original number, which is within the access of the state-of-the-art quantum computers. We expect such numerical experiments could open new paths in charactering classical machine learning algorithms, and at the same time shed lights on the generic quantum simulations/computations of machine learning tasks.Comment: 10 pages, 5 figure

Characterizing the quantum field theory vacuum using temporal Matrix Product states

In this paper we construct the continuous Matrix Product State (MPS) representation of the vacuum of the field theory corresponding to the continuous limit of an Ising model. We do this by exploiting the observation made by Hastings and Mahajan in [Phys. Rev. A \textbf{91}, 032306 (2015)] that the Euclidean time evolution generates a continuous MPS along the time direction. We exploit this fact, together with the emerging Lorentz invariance at the critical point in order to identify the matrix product representation of the quantum field theory (QFT) vacuum with the continuous MPS in the time direction (tMPS). We explicitly construct the tMPS and check these statements by comparing the physical properties of the tMPS with those of the standard ground MPS. We furthermore identify the QFT that the tMPS encodes with the field theory emerging from taking the continuous limit of a weakly perturbed Ising model by a parallel field first analyzed by Zamolodchikov.Comment: The results presented in this paper are a significant expansion of arXiv:1608.0654

Information and Communication Technology, Uncertainty Reduction, and Dual Identification in Chinese Organizations

By employing Chinese sample, this study examined the relationship between organizational members’ use of information and communication technologies (ICT) and their identification with the immediate workgroup and the overall organization. Employees’ uncertainty level was proposed as a mediating factor in these relationships. Participants N=336 completed an online survey. Results indicated that workgroup identification (WID) was positively predicted by members’ use of organizational social media to seek work-related information, and organizational identification (OID) was positively predicted by organizational social media and intranet for the same purpose. These relationships were either partially or fully mediated by employee’s uncertainty level. Results contributed to our understanding of ICT’s role in modern collocated work settings and shed lights on ICT and identification in a non-Euro-American context