Tensor network compressed sensing with unsupervised machine learning
Shi-Ju Ran, Zheng-Zhi Sun, Shao-Ming Fei, Gang Su, Maciej Lewenstein
Abstract
The authors incorporate the ideas of compressed sensing, tensor network, and machine learning, and propose the tensor-network compressed sensing that permits compressed samplings and efficient communications of real-life data by implementing designed projections on the generative tensor network states.
Topics & Concepts
Compressed sensingArtificial intelligenceComputer scienceTensor (intrinsic definition)Pattern recognition (psychology)Unsupervised learningMachine learningArtificial neural networkGenerative modelConstruct (python library)Generative grammarTensor decompositionData compressionSupport vector machineData-drivenSignal processingSparse and Compressive Sensing TechniquesTensor decomposition and applicationsQuantum many-body systems