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[1] Jacob biamonte,et al. Quantum machine learning , 2016, Nature.
[2] M. Benedetti,et al. Estimation of effective temperatures in quantum annealers for sampling applications: A case study with possible applications in deep learning , 2015, 1510.07611.
[3] Daniel A. Lidar,et al. Quantum adiabatic Markovian master equations , 2012, 1206.4197.
[4] Daniel A. Lidar,et al. Optimally Stopped Optimization , 2016, 1608.05764.
[5] Wojciech Zaremba,et al. Improved Techniques for Training GANs , 2016, NIPS.
[6] Simone Severini,et al. Quantum machine learning: a classical perspective , 2017, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.
[7] Terrence J. Sejnowski,et al. A Learning Algorithm for Boltzmann Machines , 1985, Cognitive Sciences.
[8] Asli Çelikyilmaz,et al. Associative Adversarial Networks , 2016, ArXiv.
[9] Alexei A. Efros,et al. Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[10] Ashley Montanaro,et al. Average-case complexity versus approximate simulation of commuting quantum computations , 2015, Physical review letters.
[11] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[12] Alejandro Perdomo-Ortiz,et al. Quantum-assisted Helmholtz machines: A quantum–classical deep learning framework for industrial datasets in near-term devices , 2017, ArXiv.
[13] Eleanor G. Rieffel,et al. Thermalization, Freeze-out, and Noise: Deciphering Experimental Quantum Annealers , 2017, 1703.03902.
[14] Walter Vinci,et al. Quantum variational autoencoder , 2018, Quantum Science and Technology.
[15] Jack Raymond,et al. Global Warming: Temperature Estimation in Annealers , 2016, Front. ICT.
[16] M. Amin. Searching for quantum speedup in quasistatic quantum annealers , 2015, 1503.04216.
[17] Shakir Mohamed,et al. Learning in Implicit Generative Models , 2016, ArXiv.
[18] Truyen Tran,et al. On catastrophic forgetting and mode collapse in Generative Adversarial Networks , 2018, ArXiv.
[19] Eleanor G. Rieffel,et al. Perils of embedding for sampling problems , 2020, Physical Review Research.
[20] Nicolas Le Roux,et al. Representational Power of Restricted Boltzmann Machines and Deep Belief Networks , 2008, Neural Computation.
[21] Ben Taskar,et al. Graphical Models in a Nutshell , 2007 .
[22] Rupak Biswas,et al. A NASA perspective on quantum computing: Opportunities and challenges , 2017, Parallel Comput..
[23] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[24] Rupak Biswas,et al. Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models , 2016, 1609.02542.
[25] Andrew McCallum,et al. Introduction to Statistical Relational Learning , 2007 .
[26] E. Rieffel,et al. Power of Pausing: Advancing Understanding of Thermalization in Experimental Quantum Annealers , 2018, Physical Review Applied.
[27] Soumith Chintala,et al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.
[28] Daniel A. Lidar,et al. Defining and detecting quantum speedup , 2014, Science.
[29] Miguel Á. Carreira-Perpiñán,et al. On Contrastive Divergence Learning , 2005, AISTATS.
[30] Steven H. Adachi,et al. Application of Quantum Annealing to Training of Deep Neural Networks , 2015, ArXiv.
[31] Christian Ledig,et al. Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[32] Roger Melko,et al. Quantum Boltzmann Machine , 2016, 1601.02036.
[33] Tom White,et al. Sampling Generative Networks: Notes on a Few Effective Techniques , 2016, ArXiv.
[34] Fei-Yue Wang,et al. Generative adversarial networks: introduction and outlook , 2017, IEEE/CAA Journal of Automatica Sinica.
[35] Michael J. Bremner,et al. Quantum sampling problems, BosonSampling and quantum supremacy , 2017, npj Quantum Information.
[36] Dong Yu,et al. Deep Learning: Methods and Applications , 2014, Found. Trends Signal Process..
[37] Samuel A. Barnett,et al. Convergence Problems with Generative Adversarial Networks (GANs) , 2018, ArXiv.
[38] Shing-Chow Chan,et al. A cumulant-based approach for direction finding in the presence of mutual coupling , 2014, Signal Process..
[39] Hilbert J Kappen,et al. Learning quantum models from quantum or classical data , 2018, Journal of Physics A: Mathematical and Theoretical.
[40] Vicky Choi,et al. Minor-embedding in adiabatic quantum computation: II. Minor-universal graph design , 2010, Quantum Inf. Process..
[41] Demis Hassabis,et al. Mastering the game of Go with deep neural networks and tree search , 2016, Nature.
[42] Rupak Biswas,et al. Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers , 2017, Quantum Science and Technology.
[43] Aaron C. Courville,et al. Improved Training of Wasserstein GANs , 2017, NIPS.
[44] Carl Doersch,et al. Tutorial on Variational Autoencoders , 2016, ArXiv.
[45] Léon Bottou,et al. Wasserstein GAN , 2017, ArXiv.
[46] Firas Hamze,et al. Glassy Chimeras could be blind to quantum speedup: Designing better benchmarks for quantum annealing machines , 2014, 1401.1546.