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The intrinsic probabilistic nature of quantum mechanics invokes endeavors of designing quantum generative learning models (QGLMs).
The sizes of compact subsets of hilbert space and continuity of gaussian processes
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The helmholtz machine
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The nature of statistical learning theory
Vladimir Vapnik · 1999
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Quantum fingerprinting
Harry Buhrman, Richard Cleve, John Watrous, and Ronald De Wolf · 2001
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Quantum merlin-arthur proof systems: Are multiple merlins more helpful to arthur?
Hirotada Kobayashi, Keiji Matsumoto, and Tomoyuki Yamakami · 2003
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A few notes on statistical learning theory
Shahar Mendelson · 2003
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Convex optimization
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The complexity of the local hamiltonian problem
Julia Kempe, Alexei Kitaev, and Oded Regev · 2006
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Representational power of restricted boltzmann machines and deep belief networks
Nicolas Le Roux and Yoshua Bengio · 2008
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Quantum-state preparation with universal gate decompositions
Martin Plesch and Časlav Brukner · 2011
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A kernel two-sample test
Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
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A practical guide to training restricted boltzmann machines
Geoffrey E Hinton · 2012
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Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 2014
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Conditional generative adversarial nets
Mehdi Mirza and Simon Osindero · 2014
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Deep learning in neural networks: An overview
Jürgen Schmidhuber · 2015
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Training generative neural networks via maximum mean discrepancy optimization
GK Dziugaite, DM Roy, and Z Ghahramani · 2015
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Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, Ian Goodfellow, and Brendan Frey · 2015
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Generating images with perceptual similarity metrics based on deep networks
Alexey Dosovitskiy and Thomas Brox · 2016
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Mode regularized generative adversarial networks
Tong Che, Yanran Li, Athul Paul Jacob, Yoshua Bengio, and Wenjie Li · 2016
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Deep learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
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Energy-based generative adversarial networks
Junbo Zhao, Michael Mathieu, and Yann LeCun · 2017
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Quantum mechanical computers
Richard P Feynman · 2017
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Quantum machine learning
Jacob Biamonte, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe, and Seth Lloyd · 2017
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Quantum computational supremacy
Aram W Harrow and Ashley Montanaro · 2017
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Generalization and equilibrium in generative adversarial nets (gans)
Sanjeev Arora, Rong Ge, Yingyu Liang, Tengyu Ma, and Yi Zhang · 2017
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Complexity-theoretic foundations of quantum supremacy experiments
Scott Aaronson and Lijie Chen · 2017
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Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks
Han Zhang, Tao Xu, Hongsheng Li, Shaoting Zhang, Xiaogang Wang, Xiaolei Huang, and Dimitris N Metaxas · 2017
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Efficient representation of quantum many-body states with deep neural networks
Xun Gao and Lu-Ming Duan · 2017
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Learning hard quantum distributions with variational autoencoders
Andrea Rocchetto, Edward Grant, Sergii Strelchuk, Giuseppe Carleo, and Simone Severini · 2018
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Constructing exact representations of quantum many-body systems with deep neural networks
Giuseppe Carleo, Yusuke Nomura, and Masatoshi Imada · 2018
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Do GANs learn the distribution? some theory and empirics
Sanjeev Arora, Andrej Risteski, and Yi Zhang · 2018
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Which training methods for gans do actually converge?
Lars Mescheder, Andreas Geiger, and Sebastian Nowozin · 2018
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A quantum machine learning algorithm based on generative models
Xun Gao, Z-Y Zhang, and L-M Duan · 2018
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Quantum generative adversarial learning
Seth Lloyd and Christian Weedbrook · 2018
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Quantum computing in the nisq era and beyond
John Preskill · 2018
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Differentiable learning of quantum circuit born machines
Jin-Guo Liu and Lei Wang · 2018
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Bias and generalization in deep generative models: An empirical study
Shengjia Zhao, Hongyu Ren, Arianna Yuan, Jiaming Song, Noah Goodman, and Stefano Ermon · 2018
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Barren plateaus in quantum neural network training landscapes
Jarrod R McClean, Sergio Boixo, Vadim N Smelyanskiy, Ryan Babbush, and Hartmut Neven · 2018
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Strong quantum computational advantage using a superconducting quantum processor
Yulin Wu, Wan-Su Bao, Sirui Cao, Fusheng Chen, Ming-Cheng Chen, Xiawei Chen, Tung-Hsun Chung, Hui Deng, Yajie Du, Daojin Fan, et al · 2021
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Quantum advantage in learning from experiments
Hsin-Yuan Huang, Michael Broughton, Jordan Cotler, Sitan Chen, Jerry Li, Masoud Mohseni, Hartmut Neven, Ryan Babbush, Richard Kueng, John Preskill, et al · 2021
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Power of data in quantum machine learning
Hsin-Yuan Huang, Michael Broughton, Masoud Mohseni, Ryan Babbush, Sergio Boixo, Hartmut Neven, and Jarrod R McClean · 2021
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Towards understanding the power of quantum kernels in the NISQ era
Xinbiao Wang, Yuxuan Du, Yong Luo, and Dacheng Tao · 2021
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On exploring practical potentials of quantum auto-encoder with advantages
Yuxuan Du and Dacheng Tao · 2021
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Characterizing quantum supremacy in near-term devices
Sergio Boixo, Sergei V Isakov, Vadim N Smelyanskiy, Ryan Babbush, Nan Ding, Zhang Jiang, Michael J Bremner, John M Martinis, and Hartmut Neven · 2018
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Foundations of Machine Learning
Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar · 2018
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Fundamental limitations for measurements in quantum many-body systems
Thomas Barthel and Jianfeng Lu · 2018
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Ran Yi, Yong-Jin Liu, Yu-Kun Lai, and Paul L Rosin · 2019
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Tero Karras, Samuli Laine, and Timo Aila · 2019
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Semantic image synthesis with spatially-adaptive normalization
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Variational quantum algorithms
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Quantum versus classical generative modelling in finance
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Experimental quantum generative adversarial networks for image generation
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Quantum maximum mean discrepancy gan
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Variational quantum generators: Generative adversarial quantum machine learning for continuous distributions
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Quantum semi-supervised generative adversarial network for enhanced data classification
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Noise robustness and experimental demonstration of a quantum generative adversarial network for continuous distributions
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Quantum generative models for small molecule drug discovery
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Learnability of quantum neural networks
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The power of quantum neural networks
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Generalization in quantum machine learning: A quantum information standpoint
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Generalization in quantum machine learning from few training data
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Information-theoretic bounds on quantum advantage in machine learning
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The dilemma of quantum neural networks
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On the quantum versus classical learnability of discrete distributions
Ryan Sweke, Jean-Pierre Seifert, Dominik Hangleiter, and Jens Eisert · 2021
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Learnability of the output distributions of local quantum circuits
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Quantum generative training using r \ \backslash ’enyi divergences
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Style-based quantum generative adversarial networks for monte carlo events
Carlos Bravo-Prieto, Julien Baglio, Marco Cè, Anthony Francis, Dorota M Grabowska, and Stefano Carrazza · 2021
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A semi-agnostic ansatz with variable structure for quantum machine learning
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Quantum architecture search via deep reinforcement learning
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Universal variational quantum computation
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