We investigate the continuous variable quantum teleportation in atmosphere channels. The beam-wandering model is employed to analyze the teleportation of the unknown single-mode coherent state. Two methods, one is det...We investigate the continuous variable quantum teleportation in atmosphere channels. The beam-wandering model is employed to analyze the teleportation of the unknown single-mode coherent state. Two methods, one is deterministic by increasing the aperture size of the detecting device and one is probabilistic by entanglement distillation, are proposed to improve the teleportation fidelity in the presence of atmosphere noises.展开更多
In this paper, classical and continuous variable (CV) quantum neural network hybrid multi-classifiers are presented using the MNIST dataset. Currently available classifiers can classify only up to two classes. The pro...In this paper, classical and continuous variable (CV) quantum neural network hybrid multi-classifiers are presented using the MNIST dataset. Currently available classifiers can classify only up to two classes. The proposed architecture allows networks to classify classes up to n<sup>m</sup> classes, where n represents cutoff dimension and m the number of qumodes on photonic quantum computers. The combination of cutoff dimension and probability measurement method in the CV model allows a quantum circuit to produce output vectors of size n<sup>m</sup>. They are then interpreted as one-hot encoded labels, padded with n<sup>m</sup> - 10 zeros. The total of seven different classifiers is built using 2, 3, …, 6, and 8-qumodes on photonic quantum computing simulators, based on the binary classifier architecture proposed in “Continuous variable quantum neural networks” [1]. They are composed of a classical feed-forward neural network, a quantum data encoding circuit, and a CV quantum neural network circuit. On a truncated MNIST dataset of 600 samples, a 4-qumode hybrid classifier achieves 100% training accuracy.展开更多
基金Supported by the National Natural Science Foundation of China under Grant Nos 11574400,U1304613,11204197,11204379and 11074244
文摘We investigate the continuous variable quantum teleportation in atmosphere channels. The beam-wandering model is employed to analyze the teleportation of the unknown single-mode coherent state. Two methods, one is deterministic by increasing the aperture size of the detecting device and one is probabilistic by entanglement distillation, are proposed to improve the teleportation fidelity in the presence of atmosphere noises.
文摘In this paper, classical and continuous variable (CV) quantum neural network hybrid multi-classifiers are presented using the MNIST dataset. Currently available classifiers can classify only up to two classes. The proposed architecture allows networks to classify classes up to n<sup>m</sup> classes, where n represents cutoff dimension and m the number of qumodes on photonic quantum computers. The combination of cutoff dimension and probability measurement method in the CV model allows a quantum circuit to produce output vectors of size n<sup>m</sup>. They are then interpreted as one-hot encoded labels, padded with n<sup>m</sup> - 10 zeros. The total of seven different classifiers is built using 2, 3, …, 6, and 8-qumodes on photonic quantum computing simulators, based on the binary classifier architecture proposed in “Continuous variable quantum neural networks” [1]. They are composed of a classical feed-forward neural network, a quantum data encoding circuit, and a CV quantum neural network circuit. On a truncated MNIST dataset of 600 samples, a 4-qumode hybrid classifier achieves 100% training accuracy.
