We present a quantuminspired image augmentation protocol which is applicable to classical images and, in principle, due to its known quantum formulation applicable to quantum systems and quantum machine learning in t...
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ISBN:
(纸本)9798350344868;9798350344851
We present a quantuminspired image augmentation protocol which is applicable to classical images and, in principle, due to its known quantum formulation applicable to quantum systems and quantum machine learning in the future. The augmentation technique relies on the phenomenon Anderson localization. As we will illustrate by numerical examples the technique changes classical wave properties by interference effects resulting from scatterings at impurities in the material. We explain that the augmentation can be understood as multiplicative noise, which counter-intuitively averages out, by sampling over disorder realizations. Furthermore, we show how the augmentation can be implemented in arrays of disordered waveguides with direct implications for an efficient optical image transfer.
Large-scale complex systems, such as social networks, electrical power grid, database structure, consumption pattern or brain connectivity, are often modelled using network graphs. Valuable insight can be gained by me...
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Large-scale complex systems, such as social networks, electrical power grid, database structure, consumption pattern or brain connectivity, are often modelled using network graphs. Valuable insight can be gained by measuring similarity between network graphs in order to make quantitative comparisons. Since these networks can be very large, scalability and efficiency of the algorithm are key concerns. More importantly, for graphs with unknown labelling, this graph similarity problem requires exponential time to solve using existing algorithms. In this paper, we propose a quantum walk inspired algorithm, which provides a solution to the graph similarity problem without prior knowledge on graph labelling. This algorithm is capable of distinguishing between minor structural differences, such as between strongly regular graphs with the same parameters. The algorithm has a polynomial complexity, scaling with O(n9).
Deep neural networks have gained attention in the last decade as significant progress has been made in a variety of tasks thanks to these new architectures. Most of the time, hand-designed networks are responsible for...
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ISBN:
(纸本)9781728119854
Deep neural networks have gained attention in the last decade as significant progress has been made in a variety of tasks thanks to these new architectures. Most of the time, hand-designed networks are responsible for this incredible success. However, this engineering process demands considerable time and expert knowledge, which leads to an increasing interest in automating the design of deep architectures. Several new algorithms have been proposed to address the neural architecture search problem, but many of them require significant computational resources. quantum-inspired evolutionary algorithms (QIEA) have their roots on quantum computing principles and present promising results in respect to faster convergence. In this work, we propose Q-NAS (quantum-inspired Neural Architecture Search): a quantum-inspired algorithm to search for deep neural architectures by assembling substructures and optimizing some numerical hyperparameters. We present the first results applying Q-NAS on the CIFAR-10 dataset using only 20 K80 GPUs for about 50 hours. The obtained networks are relatively small (less than 20 layers) compared to other state-of-the-art models and achieve promising accuracies with considerably less computational cost than other NAS algorithms.
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