Learning hard quantum distributions with variational autoencoders
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Simone Severini | Andrea Rocchetto | Edward Grant | Giuseppe Carleo | Sergii Strelchuk | S. Severini | G. Carleo | Andrea Rocchetto | S. Strelchuk | E. Grant | Giuseppe Carleo | Edward Grant | Sergii Strelchuk
[1] Andrew S. Darmawan,et al. Restricted-Boltzmann-Machine Learning for Solving Hubbard and Heisenberg Models , 2018 .
[2] Matthias Troyer,et al. Neural-network quantum state tomography , 2018 .
[3] J. Cirac,et al. Neural-Network Quantum States, String-Bond States, and Chiral Topological States , 2017, 1710.04045.
[4] Elham Kashefi,et al. Verification of Quantum Computation: An Overview of Existing Approaches , 2017, Theory of Computing Systems.
[5] Amnon Shashua,et al. Deep Learning and Quantum Entanglement: Fundamental Connections with Implications to Network Design , 2017, ICLR.
[6] J. Chen,et al. Equivalence of restricted Boltzmann machines and tensor network states , 2017, 1701.04831.
[7] Raphael Kaubruegger,et al. Chiral topological phases from artificial neural networks , 2017, 1710.04713.
[8] S. R. Clark,et al. Unifying neural-network quantum states and correlator product states via tensor networks , 2017, 1710.03545.
[9] Andrew S. Darmawan,et al. Restricted Boltzmann machine learning for solving strongly correlated quantum systems , 2017, 1709.06475.
[10] Aram W. Harrow,et al. Quantum computational supremacy , 2017, Nature.
[11] Roger G. Melko,et al. Deep Learning the Ising Model Near Criticality , 2017, J. Mach. Learn. Res..
[12] Richard Jozsa,et al. Efficient classical verification of quantum computations , 2017 .
[13] Lu-Ming Duan,et al. Efficient representation of quantum many-body states with deep neural networks , 2017, Nature Communications.
[14] D. Deng,et al. Quantum Entanglement in Neural Network States , 2017, 1701.04844.
[15] Matthias Troyer,et al. Solving the quantum many-body problem with artificial neural networks , 2016, Science.
[16] Dong-Ling Deng,et al. Exact Machine Learning Topological States , 2016 .
[17] H. Neven,et al. Characterizing quantum supremacy in near-term devices , 2016, Nature Physics.
[18] Tomaso Poggio,et al. Learning Functions: When Is Deep Better Than Shallow , 2016, 1603.00988.
[19] Matus Telgarsky,et al. Benefits of Depth in Neural Networks , 2016, COLT.
[20] Ole Winther,et al. Ladder Variational Autoencoders , 2016, NIPS.
[21] Ohad Shamir,et al. The Power of Depth for Feedforward Neural Networks , 2015, COLT.
[22] Bill Fefferman,et al. The Power of Quantum Fourier Sampling , 2015, TQC.
[23] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[24] Non-local propagation of correlations in long-range interacting quantum systems , 2014, 1401.5088.
[25] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[26] Roman Orus,et al. A Practical Introduction to Tensor Networks: Matrix Product States and Projected Entangled Pair States , 2013, 1306.2164.
[27] Andrew L. Maas. Rectifier Nonlinearities Improve Neural Network Acoustic Models , 2013 .
[28] Scott Aaronson,et al. The computational complexity of linear optics , 2010, STOC '11.
[29] D. Gross,et al. Efficient quantum state tomography. , 2010, Nature communications.
[30] F. Verstraete,et al. Matrix product states, projected entangled pair states, and variational renormalization group methods for quantum spin systems , 2008, 0907.2796.
[31] Frank Verstraete,et al. Matrix product state representations , 2006, Quantum Inf. Comput..
[32] Scott Aaronson,et al. The learnability of quantum states , 2006, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.
[33] Scott Aaronson,et al. Improved Simulation of Stabilizer Circuits , 2004, ArXiv.
[34] Naoki Kawashima,et al. Quantum Monte Carlo Methods , 2002 .
[35] C. Umrigar,et al. Quantum Monte Carlo methods in physics and chemistry , 1999 .
[36] Caves,et al. Ensemble-dependent bounds for accessible information in quantum mechanics. , 1994, Physical review letters.
[37] Masuo Suzuki,et al. Quantum Monte Carlo Methods in Condensed Matter Physics , 1993 .
[38] Yih-Fang Huang,et al. Bounds on the number of hidden neurons in multilayer perceptrons , 1991, IEEE Trans. Neural Networks.
[39] R Beltán,et al. [Learning about functions]. , 1976, ALAFO; revista de la Asociacion Latinoamericana de Facultades de Odontologia.