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Machine learning with artificial neural networks is revolutionizing science.
Function optimization using connectionist reinforcement learning algorithms
Ronald J Williams and Jing Peng · 1991
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Function optimization using connectionist reinforcement learning algorithms
Ronald J Williams and Jing Peng · 1991
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J. Williams · 1992
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J. Williams · 1992
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Scheme for reducing decoherence in quantum computer memory
Peter W. Shor · 1995
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Perfect quantum error correcting code
Raymond Laflamme, Cesar Miquel, Juan Pablo Paz, and Wojciech Hubert Zurek · 1996
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Long Short-Term Memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Long-Short Term Memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Efficient computations of encodings for quantum error correction
Richard Cleve and Daniel Gottesman · 1997
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 1998
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Natural Gradient Works Efficiently in Learning
Shun-ichi Amari · 1998
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 1998
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Natural Gradient Works Efficiently in Learning
Shun-ichi Amari · 1998
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The heisenberg representation of quantum computers
Daniel Gottesman · 1998
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A natural policy gradient
Sham M Kakade · 2002
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A natural policy gradient
Sham M Kakade · 2002
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Reinforcement learning for humanoid robotics
Jan Peters, Sethu Vijayakumar, and Stefan Schaal · 2003
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Realization of quantum error correction
J. Chiaverini, D. Leibfried, T. Schaetz, M. D. Barrett, R. B. Blakestad, J. Britton, W. M. Itano, J. D. Jost, E. Knill, C. Langer, R. Ozeri, and D. J. Wineland · 2004
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Improved simulation of stabilizer circuits
Scott Aaronson and Daniel Gottesman · 2004
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Optimal control of coupled spin dynamics: design of NMR pulse sequences by gradient ascent algorithms
Navin Khaneja, Timo Reiss, Cindie Kehlet, Thomas Schulte-Herbrüggen, and Steffen J. Glaser · 2005
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Visualizing Data using t-SNE
Laurens van der Maaten and Geoffrey Hinton · 2008
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Natural actor-critic
Jan Peters and Stefan Schaal · 2008
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Natural actor-critic
Jan Peters and Stefan Schaal · 2008
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Visualizing data using t-SNE
Laurens van der Maaten and Geoffrey Hinton · 2008
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The Automation of Science
Ross D. King, Jem Rowland, Stephen G. Oliver, Michael Young, Wayne Aubrey, Emma Byrne, Maria Liakata, Magdalena Markham, Pinar Pir, Larisa N. Soldatova, Andrew Sparkes, Kenneth E. Whelan, and Amanda Clare · 2009
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Distilling Free-Form Natural Laws from Experimental Data
Michael Schmidt and Hod Lipson · 2009
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Cavity grid for scalable quantum computation with superconducting circuits
F. Helmer, M. Mariantoni, A. G. Fowler, J. von Delft, E. Solano, and F. Marquardt · 2009
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Machine Learning for Precise Quantum Measurement
Alexander Hentschel and Barry C. Sanders · 2010
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Experimental Repetitive Quantum Error Correction
Philipp Schindler, Julio T. Barreiro, Thomas Monz, Volckmar Nebendahl, Daniel Nigg, Michael Chwalla, Markus Hennrich, and Rainer Blatt · 2011
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Comparing, optimizing, and benchmarking quantum-control algorithms in a unifying programming framework
S. Machnes, U. Sander, S. J. Glaser, P. de Fouquières, A. Gruslys, S. Schirmer, and T. Schulte-Herbrüggen · 2011
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Efficient Algorithm for Optimizing Adaptive Quantum Metrology Processes
Alexander Hentschel and Barry C. Sanders · 2011
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Quantum Computation and Quantum Information: 10th Anniversary Edition
Michael A. Nielsen and Isaac L. Chuang · 2011
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Quantum Computation and Quantum Information: 10th Anniversary Edition
Michael A. Nielsen and Isaac L. Chuang · 2011
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Scikit-learn: Machine Learning in Python
Digital feedback in superconducting quantum circuits
D. Ristè and L. DiCarlo · 2016
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Learning in Quantum Control: High-Dimensional Global Optimization for Noisy Quantum Dynamics
Pantita Palittapongarnpim, Peter Wittek, Ehsan Zahedinejad, Shakib Vedaie, and Barry C. Sanders · 2016
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Deep Learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
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Theano: A Python framework for fast computation of mathematical expressions
Rami Al-Rfou et al · 2016
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Deep Learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
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Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, and Édouard Duchesnay · 2011
Cited alongside, same era.
Feedback Control of a Solid-State Qubit Using High-Fidelity Projective Measurement
D. Ristè, C. C. Bultink, K. W. Lehnert, and L. DiCarlo · 2012
Cited alongside, same era.
Improving neural networks by preventing co-adaptation of feature detectors
Geoffrey E Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, and Ruslan R Salakhutdinov · 2012
Cited alongside, same era.
Scaling the Ion Trap Quantum Processor
C. Monroe and J. Kim · 2013
Cited alongside, same era.
Superconducting Circuits for Quantum Information: An Outlook
M. H. Devoret and R. J. Schoelkopf · 2013
Cited alongside, same era.
Persistent Control of a Superconducting Qubit by Stroboscopic Measurement Feedback
P. Campagne-Ibarcq, E. Flurin, N. Roch, D. Darson, P. Morfin, M. Mirrahimi, M. H. Devoret, F. Mallet, and B. Huard · 2013
Cited alongside, same era.
Deterministic quantum teleportation with feed-forward in a solid state system
L. Steffen, Y. Salathe, M. Oppliger, P. Kurpiers, M. Baur, C. Lang, C. Eichler, G. Puebla-Hellmann, A. Fedorov, and A. Wallraff · 2013
Cited alongside, same era.
Rami Al-Rfou et al · 2016
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Solving the quantum many-body problem with artificial neural networks
Giuseppe Carleo and Matthias Troyer · 2017
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Machine learning phases of matter
Juan Carrasquilla and Roger G. Melko · 2017
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Learning phase transitions by confusion
Evert P. L. van Nieuwenburg, Ye-Hua Liu, and Sebastian D. Huber · 2017
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Neural Decoder for Topological Codes
Giacomo Torlai and Roger G. Melko · 2017
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Using Recurrent Neural Networks to Optimize Dynamical Decoupling for Quantum Memory
Moritz August and Xiaotong Ni · 2017
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Machine learning and artificial intelligence in the quantum domain
Vedran Dunjko and Hans J. Briegel · 2017
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Machine-learning-assisted correction of correlated qubit errors in a topological code
P. Baireuther, T. E. O’Brien, B. Tarasinski, and C. W. J. Beenakker · 2017
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Deep Neural Network Probabilistic Decoder for Stabilizer Codes
Stefan Krastanov and Liang Jiang · 2017
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Mastering the game of Go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, Yutian Chen, Timothy Lillicrap, Fan Hui, Laurent Sifre, George van den Driessche, Thore Graepel, and Demis Hassabis · 2017
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Machine Learning Meets Quantum State Preparation. The Phase Diagram of Quantum Control
Marin Bukov, Alexandre G. R. Day, Dries Sels, Phillip Weinberg, Anatoli Polkovnikov, and Pankaj Mehta · 2017
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Studying Light-Harvesting Models with Superconducting Circuits
Anton Potocnik, Arno Bargerbos, Florian A. Y. N. Schröder, Saeed A. Khan, Michele C. Collodo, Simone Gasparinetti, Yves Salathé, Celestino Creatore, Christopher Eichler, Hakan E. Türeci, Alex W. Chin, and Andreas Wallraff · 2017
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Experimental Demonstration of Fault-Tolerant State Preparation with Superconducting Qubits
Maika Takita, Andrew W. Cross, A. D. Córcoles, Jerry M. Chow, and Jay M. Gambetta · 2017
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A programmable two-qubit quantum processor in silicon
T. F. Watson, S. G. J. Philips, E. Kawakami, D. R. Ward, P. Scarlino, M. Veldhorst, D. E. Savage, M. G. Lagally, Mark Friesen, S. N. Coppersmith, M. A. Eriksson, and L. M. K. Vandersypen · 2017
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QVECTOR: an algorithm for device-tailored quantum error correction
Peter D. Johnson, Jonathan Romero, Jonathan Olson, Yudong Cao, and Alán Aspuru-Guzik · 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 autoencoders for efficient compression of quantum data
Jonathan Romero, Jonathan P. Olson, and Alan Aspuru-Guzik · 2017
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Implementing a universal gate set on a logical qubit encoded in an oscillator
Reinier W. Heeres, Philip Reinhold, Nissim Ofek, Luigi Frunzio, Liang Jiang, Michel H. Devoret, and Robert J. Schoelkopf · 2017
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A Crossbar Network for Silicon Quantum Dot Qubits
R. Li, L. Petit, D. P. Franke, J. P. Dehollain, J. Helsen, M. Steudtner, N. K. Thomas, Z. R. Yoscovits, K. J. Singh, S. Wehner, L. M. K. Vandersypen, J. S. Clarke, and M. Veldhorst · 2017
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A high-bias, low-variance introduction to Machine Learning for physicists
Pankaj Mehta, Marin Bukov, Ching-Hao Wang, Alexandre G. R. Day, Clint Richardson, Charles K. Fisher, and David J. Schwab · 2018
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Active learning machine learns to create new quantum experiments
Alexey A. Melnikov, Hendrik Poulsen Nautrup, Mario Krenn, Vedran Dunjko, Markus Tiersch, Anton Zeilinger, and Hans J. Briegel · 2018
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Moritz August and José Miguel Hernández-Lobato · 2018
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