Learning hard quantum distributions with variational autoencoders

[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.