1,358 research outputs found

    Photon-assisted Landau-Zener transition: Role of coherent superposition states

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    We investigate a Landau-Zener (LZ) transition process modeled by a quantum two-level system (TLS) coupled to a photon mode when the bias energy is varied linearly in time. The initial state of the photon field is assumed to be a superposition of coherent states, leading to a more intricate LZ transition. Applying the rotating-wave approximation (RWA), analytical results are obtained revealing the enhancement of the LZ probability by increasing the average photon number. We also consider the creation of entanglement and the change of photon statistics during the LZ process. Without the RWA, we find some qualitative differences of the LZ dynamics from the RWA results, e.g., the average photon number no longer monotonically enhances the LZ probability. The ramifications and implications of these results are explored.Comment: 9 pages, 7 figure

    EIGEN: Ecologically-Inspired GENetic Approach for Neural Network Structure Searching from Scratch

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    Designing the structure of neural networks is considered one of the most challenging tasks in deep learning, especially when there is few prior knowledge about the task domain. In this paper, we propose an Ecologically-Inspired GENetic (EIGEN) approach that uses the concept of succession, extinction, mimicry, and gene duplication to search neural network structure from scratch with poorly initialized simple network and few constraints forced during the evolution, as we assume no prior knowledge about the task domain. Specifically, we first use primary succession to rapidly evolve a population of poorly initialized neural network structures into a more diverse population, followed by a secondary succession stage for fine-grained searching based on the networks from the primary succession. Extinction is applied in both stages to reduce computational cost. Mimicry is employed during the entire evolution process to help the inferior networks imitate the behavior of a superior network and gene duplication is utilized to duplicate the learned blocks of novel structures, both of which help to find better network structures. Experimental results show that our proposed approach can achieve similar or better performance compared to the existing genetic approaches with dramatically reduced computation cost. For example, the network discovered by our approach on CIFAR-100 dataset achieves 78.1% test accuracy under 120 GPU hours, compared to 77.0% test accuracy in more than 65, 536 GPU hours in [35].Comment: CVPR 201
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