Yuanlong Wang

Daoyi Dong, Yuanlong Wang

This paper summarizes several recent developments in the area of estimation and robust control of quantum systems and outlines several directions for future research. Quantum state tomography via linear regression estimation and adaptive quantum state estimation are introduced and a Hamiltonian identification algorithm is outlined. Two quantum robust control approaches including sliding mode control and sampling-based learning control are illustrated.

Dominick J. Joch, Sergei Slussarenko, Yuanlong Wang et al.

The ultimate random number generators are those certified to be unpredictable -- including to an adversary. The use of simple quantum processes promises to provide numbers that no physical observer could predict but, in practice, unwanted noise and imperfect devices can compromise fundamental randomness and protocol security. Certified randomness protocols have been developed which remove the need for trust in devices by taking advantage of nonlocality. Here, we use a photonic platform to implement our protocol, which operates in the quantum steering scenario where one can certify randomness in a one-sided device independent framework. We demonstrate an approach for a steering-based generator of public or private randomness, and the first generation of certified random bits, with the detection loophole closed, in the steering scenario.

Hailan Ma, Chang-Jiang Huang, Chunlin Chen et al.

Quantum autoencoders which aim at compressing quantum information in a low-dimensional latent space lie in the heart of automatic data compression in the field of quantum information. In this paper, we establish an upper bound of the compression rate for a given quantum autoencoder and present a learning control approach for training the autoencoder to achieve the maximal compression rate. 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. Numerical results on 2-qubit and 3-qubit systems are presented to demonstrate how to train the quantum autoencoder to achieve the theoretically maximal compression, and the training performance using different machine learning algorithms is compared. Experimental results of a quantum autoencoder using quantum optical systems are illustrated for compressing two 2-qubit states into two 1-qubit states.