Data-driven generation of mixed X-anion perovskite properties.

Mixed X-anion perovskites, such as CsPbX3 (X = Cl, Br, or I), play an important role in photovoltaic applications. The massive disordered structures associated with mixed anions produce the need for property calculations. However, traditional density functional theory (DFT) computational tools are limited by their computational efficiency to generate the properties of a large number of structures quickly. Researchers have proposed supervised deep learning to forecast crystal properties. For such a supervised convolutional neural network (CNN), we introduce an adversarial loss function that allows for consistent or lower errors with a fewer samples. Meanwhile, we have trained parameterized quantum circuits (PQCs) of CNNs and auto-encoder networks for extracting structural representations. PQCs of deep learning, also named quantum deep learning or quantum machine learning, have been first applied in the research of perovskites and obtained an RMSE (root mean squared error) of less than 1 meV. Our work demonstrates that adversarial learning training mechanisms and PQC-based quantum deep learning will emerge for extensive and deep exploration of data-driven material formation prediction tasks.

Reconstruction of quantum channel via convex optimization.

Quantum process tomography is often used to completely characterize an unknown quantum process. However, it may lead to an unphysical process matrix, which will cause the loss of information with respect to the tomography result. Convex optimization, widely used in machine learning, is able to generate a global optimum that best fits the raw data while keeping the process tomography in a legitimate region. Only by correctly revealing the original action of the process can we seek deeper into its properties like its phase transition and its Hamiltonian. Here, we reconstruct the seawater channel using convex optimization and further test it on the seven fundamental gates. We compare our method to the standard-inversion and norm-optimization approaches using the cost function value and our proposed state deviation. The advantages convince that our method enables a more precise and robust estimation of the elements of the process matrix with less demands on preliminary resources. In addition, we examine on a set of non-unitary channels and the reconstructions reach up to 99.5% accuracy. Our method offers a more universal tool for further analyses on the components of the quantum channels and we believe that the crossover between quantum process tomography and convex optimization may help us move forward to machine learning of quantum channels.

Polarization-dependent photon switch in a one-dimensional coupled-resonator waveguide.

Polarization-dependent photon switch is one of the most important ingredients in building future large-scale all-optical quantum network. We present a scheme for a single-photon switch in a one-dimensional coupled-resonator waveguide, where N(a) Λ-type three-level atoms are individually embedded in each of the resonator. By tuning the interaction between atom and field, we show that an initial incident photon with a certain polarization can be transformed into its orthogonal polarization state. Finally, we use the fidelity as a figure of merit and numerically evaluate the performance of our photon switch scheme in varieties of system parameters, such as number of atoms, energy detuning and dipole couplings.