Quantum Compressed Sensing with Unsupervised Tensor-Network Machine Learning

Abstract

We propose tensor-network compressed sensing (TNCS) by combining the ideas of compressed sensing, tensor network (TN), and machine learning, which permits novel and efficient quantum communications of realistic data. The strategy is to use the unsupervised TN machine learning algorithm to obtain the entangled state $|\Psi ⟩$ that describes the probability distribution of a huge amount of classical information considered to be communicated. To transfer a specific piece of information with $|\Psi ⟩$, our proposal is to encode such information in the separable state with the minimal distance to the measured state $|\Phi ⟩$ that is obtained by partially measuring on $|\Psi ⟩$ in a designed way. To this end, a measuring protocol analogous to the compressed sensing with neural-network machine learning is suggested, where the measurements are designed to minimize uncertainty of information from the probability distribution given by $|\Phi ⟩$. In this way, those who have $|\Phi ⟩$ can reliably access the information by simply measuring on $|\Phi ⟩$. We propose q-sparsity to characterize the sparsity of quantum states and the efficiency of the quantum communications by TNCS. The high q-sparsity is essentially due to the fact that the TN states describing nicely the probability distribution obey the area law of entanglement entropy. Testing on realistic datasets (hand-written digits and fashion images), TNCS is shown to possess high efficiency and accuracy, where the security of communications is guaranteed by the fundamental quantum principles.

arXiv comment5+6 pages, 3+6 figures. Essential changes and new data were added to this new version

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• Tensor-Networks-based Learning of Probabilistic Cellular Automata Dynamics
• Quantum Compressive Sensing: Mathematical Machinery, Quantum Algorithms, and Quantum Circuitry
• Using matrix-product states for time-series machine learning