Machine learning (ML) has recently facilitated many advances in solving problems related to many-body physical systems. Given the intrinsic quantum nature of these problems, it is natural to speculate that quantum-enhanced machine learning will enable us to unveil even greater details than we currently have. With this motivation, this paper examines a quantum machine learning approach based on shallow variational ansatz inspired by tensor networks for supervised learning tasks. In particular, we first look at the standard image classification tasks using the Fashion-MNIST dataset and study the effect of repeating tensor network layers on ansatz's expressibility and performance. Finally, we use this strategy to tackle the problem of quantum phase recognition for the transverse-field Ising and Heisenberg spin models in one and two dimensions, where we were able to reach $\geq 98\%$ test-set accuracies with both multi-scale entanglement renormalization ansatz (MERA) and tree tensor network (TTN) inspired parametrized quantum circuits.
HQTN-SER: Speech Emotion Recognition with Hybrid Quantum Tensor Networks
School of Electrical Engineering and Computer Science, National University of Sciences & Technology (NUST)（电气与计算机工程学院，国立科学与技术大学）；eBRAIN Lab, Division of Engineering, New York University Abu Dhabi (NYUAD)（工程分院，纽约大学阿布扎比分校）；Center for Quantum and Topological Systems (CQTS), NYUAD Research Institute（纽约大学阿布扎比研究院，量子与拓扑系统中心）
Quantum Phase Recognition via Quantum Attention Mechanism
Center for Quantum Technology Research（量子技术研究中心）；Key Laboratory of Advanced Optoelectronic Quantum Architecture（先进光电量子架构重点实验室）；Measurements (MOE), School of Physics, Beijing Institute of Technology, Beijing 100081, China（测量（MOE），物理学院，北京理工大学，北京100081，中国）
Detecting and identifying 2D symmetry-protected topological, symmetry-breaking and intrinsic topological phases with modular matrices via tensor-network methods
