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On compression rate of quantum autoencoders: Control design, numerical and experimental realization
The upper bound of the compression rate is theoretically proven using eigen-decomposition and matrix differentiation, which is determined by the eigenvalues of the density matrix representation of the input states.
A comparative study on how neural networks enhance quantum state tomography
Numerical results demonstrate that DNN-QST exhibits a great potential to achieve high fidelity for quantum state tomography with limited measurement resources and can achieve improved estimation when tomographic measurements suffer from noise.
"Super-Heisenberg" and Heisenberg Scalings Achieved Simultaneously in the Estimation of a Rotating Field.
This study demonstrates the coexistence of two different scalings via the simultaneous estimation of the magnitude and frequency of a field where the best precisions, characterized by two Heisenberg uncertainty relations, scale as T-1 and T-2, respectively (in terms of the standard deviation).
Realization of a quantum autoencoder for lossless compression of quantum data
It is theoretically proved that a quantum autoencoder can losslessly compress high-dimensional quantum information into a low-dimensional space (also called latent space) if the number of maximum linearly independent vectors from input states is no more than the dimension of the latent space.
Nonlocality, Steering, and Quantum State Tomography in a Single Experiment.
This work identifies a simple and noise-robust correlation witness for entanglement detection, steering, and nonlocality that can be evaluated based on the outcome statistics obtained in the tomography experiment, and performs a photonics experiment to demonstrate quantum correlations under flexible assumptions.
Experimentally detecting a quantum change point via the Bayesian inference
This work considers a learning agent that applies Bayesian inference on experimental data to solve the problem of identifying the point where the change took place and shows that the local-detection success probability can be largely improved by using this machine learning technique.
Nonlocality, steering and entanglement detection via measurements for quantum state tomography.
This work identifies a simple and noise-robust correlation witness for entanglement detection, steering and nonlocality that can be evaluated based on the outcome statistics obtained in the tomography experiment.
Planar-Integrated Magneto-Optical Trap
- Liang Chen, Chang-Jiang Huang, Chang-ling Zou
- Materials Science, PhysicsPhysical Review Applied
- 15 July 2021
The magneto-optical trap (MOT) is an essential tool for collecting and preparing cold atoms with a wide range of applications. We demonstrate a planar-integrated MOT by combining an optical grating…
Experimental realization of a quantum autoencoder via a universal two-qubit unitary gate
A universal two-qubit unitary gate is experimentally realized and a quantum autoencoder is achieved which can be used to discriminate two groups of nonorthogonal states.