EQNAS: Evolutionary Quantum Neural Architecture Search for Image Classification

Abbas, 2021, The power of quantum neural networks, Nature Computational Science, 1, 403, 10.1038/s43588-021-00084-1 Bello, 2017, Neural optimizer search with reinforcement learning, 459 Bu, 2021 Cai, 2018 Chalumuri, 2021, A hybrid classical-quantum approach for multi-class classification, Quantum Information Processing, 20, 119, 10.1007/s11128-021-03029-9 Chu, 2020, Fair darts: Eliminating unfair advantages in differentiable architecture search, 465 Cong, 2019, Quantum convolutional neural networks, Nature Physics, 15, 1273, 10.1038/s41567-019-0648-8 Elsken, 2018 Farhi, 2018 Floreano, 2008, Neuroevolution: from architectures to learning, Evolutionary Intelligence, 1, 47, 10.1007/s12065-007-0002-4 Grover, L. K. (1996). A fast quantum mechanical algorithm for database search. In Proceedings of the twenty-eighth annual ACM symposium on theory of computing (pp. 212–219). Han, K.-H., & Kim, J. (2000). Genetic quantum algorithm and its application to combinatorial optimization problem. In Proceedings of the 2000 congress on evolutionary computation, CEC ’00 (Cat. No.00TH8512), vol. 2 (pp. 1354–1360). Han, 2002, Quantum-inspired evolutionary algorithm for a class of combinatorial optimization, IEEE Transactions on Evolutionary Computation, 6, 580, 10.1109/TEVC.2002.804320 Han, K.-H., & Kim, J. (2003). On setting the parameters of quantum-inspired evolutionary algorithm for practical application. In The 2003 congress on evolutionary computation (pp. 178–194). Jiao, 2008, Quantum-inspired immune clonal algorithm for global optimization, IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 38, 1234, 10.1109/TSMCB.2008.927271 Kak, 1995, Quantum neural computing, Systems Control & Information, 52, 143 Kouda, N., Matsui, N., Nishimura, H., & Peper, F. (2003). Qubit Neural Network and Its Efficiency. In International conference on knowledge-based and intelligent information and engineering systems (pp. 304–310). Le, 2011, A flexible representation of quantum images for polynomial preparation, image compression, and processing operations, Quantum Information Processing, 10, 63, 10.1007/s11128-010-0177-y Li, 2020, Parallel design of sparse deep belief network with multi-objective optimization, Information Sciences, 533, 24, 10.1016/j.ins.2020.03.084 Li, Y., & Jiao, L. (2005). Quantum-inspired immune clonal algorithm. In International conference on artificial immune systems (pp. 304–317). Li, 2015, Dynamic-context cooperative quantum-behaved particle swarm optimization based on multilevel thresholding applied to medical image segmentation, Information Sciences, 294, 408, 10.1016/j.ins.2014.10.005 Li, 2013, A hybrid quantum-inspired neural networks with sequence inputs, Neurocomputing, 117, 81, 10.1016/j.neucom.2013.01.029 Liang, 2021, A hybrid quantum–classical neural network with deep residual learning, Neural Networks, 143, 133, 10.1016/j.neunet.2021.05.028 Liang, 2019 Liu, 2018 Lloyd, 2013 Lu, Z., Whalen, I., Boddeti, V., Dhebar, Y., Deb, K., Goodman, E., et al. (2019). Nsga-net: neural architecture search using multi-objective genetic algorithm. In Proceedings of the genetic and evolutionary computation conference (pp. 419–427). McClean, 2018, Barren plateaus in quantum neural network training landscapes, Nature Communications, 9, 4812, 10.1038/s41467-018-07090-4 Mindspore, . (2020). https://www.mindspore.cn/. Narayanan, 2000, Quantum artificial neural network architectures and components, Information Sciences, 128, 231, 10.1016/S0020-0255(00)00055-4 Narayanan, A., & Moore, M. (1996). Quantum-inspired genetic algorithms. In Proceedings of IEEE international conference on evolutionary computation (pp. 61–66). Pavithr, 2016, Quantum inspired social evolution (QSE) algorithm for 0-1 knapsack problem, Swarm and Evolutionary Computation, 29, 33, 10.1016/j.swevo.2016.02.006 Pham, 2018, Efficient neural architecture search via parameters sharing, 4095 Real, E., Aggarwal, A., Huang, Y., & Le, Q. V. (2019). Regularized evolution for image classifier architecture search. In Proceedings of the AAAI conference on artificial intelligence, vol. 33, no. 01 (pp. 4780–4789). Rebentrost, 2014, Quantum support vector machine for big data classification, Physical Review Letters, 113, 10.1103/PhysRevLett.113.130503 Shor, P. W. (1994). Algorithms for Quantum Computation: Discrete Log and Factoring (Extended Abstract). In Proceedings of annual symposium on the foundations of computer science IEEE Computer Society Press Los Alamitos Ca (pp. 1–8). Tacchino, 2020, Quantum implementation of an artificial feed-forward neural network, Quantum Science and Technology, 5, 10.1088/2058-9565/abb8e4 Tacchino, 2019, An artificial neuron implemented on an actual quantum processor, npj Quantum Information, 5, 1, 10.1038/s41534-019-0140-4 Venegas-Andraca, 2003, Storing, processing, and retrieving an image using quantum mechanics, Quantum Information & Computation, 5105, 137, 10.1117/12.485960 Wang, 2007, A novel quantum swarm evolutionary algorithm and its applications, Neurocomputing, 70, 633, 10.1016/j.neucom.2006.10.001 Xie, L., & Yuille, A. (2017). Genetic cnn. In Proceedings of the IEEE international conference on computer vision (pp. 1379–1388). Xu, 2019 Yao, 2017, Quantum image processing and its application to edge detection: theory and experiment, Physical Review X, 7, 10.1103/PhysRevX.7.031041 Zhang, 2013, NEQR: a novel enhanced quantum representation of digital images, Quantum Information Processing, 12, 2833, 10.1007/s11128-013-0567-z Zoph, B., Vasudevan, V., Shlens, J., & Le, Q. V. (2018). Learning Transferable Architectures for Scalable Image Recognition. In 2018 IEEE/CVF conference on computer vision and pattern recognition (pp. 8697–8710).