Reinforcement Learning with Neural Networks for Quantum Feedback
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Florian Marquardt | Talitha Weiss | Petru Tighineanu | Thomas Fosel | F. Marquardt | P. Tighineanu | T. Fosel | Talitha Weiss | Petru Tighineanu
[1] Alex W Chin,et al. Reply to Reviewers Comments , 2018 .
[2] Tzyh Jong Tarn,et al. Fidelity-Based Probabilistic Q-Learning for Control of Quantum Systems , 2014, IEEE Transactions on Neural Networks and Learning Systems.
[3] A. Gruslys,et al. Comparing, optimizing, and benchmarking quantum-control algorithms in a unifying programming framework , 2010, 1011.4874.
[4] Ievgeniia Oshurko. Quantum Machine Learning , 2020, Quantum Computing.
[5] Liang Jiang,et al. Implementing a universal gate set on a logical qubit encoded in an oscillator , 2016, Nature Communications.
[6] Y. Wang,et al. Quantum error correction in a solid-state hybrid spin register , 2013, Nature.
[7] Nitish Srivastava,et al. Improving neural networks by preventing co-adaptation of feature detectors , 2012, ArXiv.
[8] Demis Hassabis,et al. Mastering the game of Go without human knowledge , 2017, Nature.
[9] David J. Schwab,et al. A high-bias, low-variance introduction to Machine Learning for physicists , 2018, Physics reports.
[10] Y. Salathe,et al. Deterministic quantum teleportation with feed-forward in a solid state system , 2013, Nature.
[11] Alexander Hentschel,et al. Machine learning for precise quantum measurement. , 2009, Physical review letters.
[12] Hans-J. Briegel,et al. Machine learning \& artificial intelligence in the quantum domain , 2017, ArXiv.
[13] Barry C. Sanders,et al. Learning in quantum control: High-dimensional global optimization for noisy quantum dynamics , 2016, Neurocomputing.
[14] Giacomo Torlai,et al. Neural Decoder for Topological Codes. , 2016, Physical review letters.
[15] Shun-ichi Amari,et al. Natural Gradient Works Efficiently in Learning , 1998, Neural Computation.
[16] Matthias Troyer,et al. Solving the quantum many-body problem with artificial neural networks , 2016, Science.
[17] S. Huber,et al. Learning phase transitions by confusion , 2016, Nature Physics.
[18] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[19] Stefan Schaal,et al. Natural Actor-Critic , 2003, Neurocomputing.
[20] Timo O. Reiss,et al. Optimal control of coupled spin dynamics: design of NMR pulse sequences by gradient ascent algorithms. , 2005, Journal of magnetic resonance.
[21] Mazyar Mirrahimi,et al. Extending the lifetime of a quantum bit with error correction in superconducting circuits , 2016, Nature.
[22] Ken E. Whelan,et al. The Automation of Science , 2009, Science.
[23] R. Schoelkopf,et al. Superconducting Circuits for Quantum Information: An Outlook , 2013, Science.
[24] Alán Aspuru-Guzik,et al. Quantum autoencoders for efficient compression of quantum data , 2016, 1612.02806.
[25] Robert H. Hadfield,et al. Superconducting Devices in Quantum Optics , 2016 .
[26] Richard S. Sutton,et al. Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.
[27] Isaac L. Chuang,et al. Quantum Computation and Quantum Information (10th Anniversary edition) , 2011 .
[28] Roger G. Melko,et al. Machine learning phases of matter , 2016, Nature Physics.
[29] Peter D. Johnson,et al. QVECTOR: an algorithm for device-tailored quantum error correction , 2017, 1711.02249.
[30] B. Terhal. Quantum error correction for quantum memories , 2013, 1302.3428.
[31] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[32] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[33] Sham M. Kakade,et al. A Natural Policy Gradient , 2001, NIPS.
[34] Austin G. Fowler,et al. Cavity grid for scalable quantum computation with superconducting circuits , 2007, 0706.3625.
[35] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[36] Shor,et al. Scheme for reducing decoherence in quantum computer memory. , 1995, Physical review. A, Atomic, molecular, and optical physics.
[37] Mario Krenn,et al. Active learning machine learns to create new quantum experiments , 2017, Proceedings of the National Academy of Sciences.
[38] José Miguel Hernández-Lobato,et al. Taking gradients through experiments: LSTMs and memory proximal policy optimization for black-box quantum control , 2018, ISC Workshops.
[39] R. N. Schouten,et al. Unconditional quantum teleportation between distant solid-state quantum bits , 2014, Science.
[40] G. G. Stokes. "J." , 1890, The New Yale Book of Quotations.
[41] Daniel Nigg,et al. Experimental Repetitive Quantum Error Correction , 2011, Science.
[42] P. Baireuther,et al. Machine-learning-assisted correction of correlated qubit errors in a topological code , 2017, 1705.07855.
[44] Barry C. Sanders,et al. An efficient algorithm for optimizing adaptive quantum metrology processes , 2011, 2011 International Quantum Electronics Conference (IQEC) and Conference on Lasers and Electro-Optics (CLEO) Pacific Rim incorporating the Australasian Conference on Optics, Lasers and Spectroscopy and the Australian Conference on Optical Fibre Technology.
[45] P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .
[46] S. Debnath,et al. Demonstration of a small programmable quantum computer with atomic qubits , 2016, Nature.
[47] Ronald J. Williams,et al. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning , 2004, Machine Learning.
[48] Andrew W. Cross,et al. Experimental Demonstration of Fault-Tolerant State Preparation with Superconducting Qubits. , 2017, Physical review letters.
[49] C. C. Bultink,et al. Feedback control of a solid-state qubit using high-fidelity projective measurement. , 2012, Physical review letters.
[50] John Salvatier,et al. Theano: A Python framework for fast computation of mathematical expressions , 2016, ArXiv.
[51] J. P. Dehollain,et al. A two-qubit logic gate in silicon , 2014, Nature.
[52] Moritz August,et al. Using Recurrent Neural Networks to Optimize Dynamical Decoupling for Quantum Memory , 2016, ArXiv.
[53] Shane Legg,et al. Human-level control through deep reinforcement learning , 2015, Nature.
[54] E. Knill,et al. Realization of quantum error correction , 2004, Nature.
[55] Liang Jiang,et al. Deep Neural Network Probabilistic Decoder for Stabilizer Codes , 2017, Scientific Reports.
[56] John M. Martinis,et al. State preservation by repetitive error detection in a superconducting quantum circuit , 2015, Nature.
[57] Victor V. Albert,et al. Dynamically protected cat-qubits: a new paradigm for universal quantum computation , 2013, 1312.2017.
[58] Hod Lipson,et al. Distilling Free-Form Natural Laws from Experimental Data , 2009, Science.
[59] Mazyar Mirrahimi,et al. Persistent control of a superconducting qubit by stroboscopic measurement feedback , 2012, 1301.6095.
[60] H. J. Briegel,et al. Adaptive quantum computation in changing environments using projective simulation , 2014, Scientific Reports.
[61] Marin Bukov,et al. Machine Learning Meets Quantum State Preparation. The Phase Diagram of Quantum Control , 2017 .
[62] Jing Peng,et al. Function Optimization using Connectionist Reinforcement Learning Algorithms , 1991 .
[63] D. E. Savage,et al. A programmable two-qubit quantum processor in silicon , 2017, Nature.
[64] Jonas Helsen,et al. A crossbar network for silicon quantum dot qubits , 2017, Science Advances.
[65] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[66] C. Monroe,et al. Scaling the Ion Trap Quantum Processor , 2013, Science.
[67] L. DiCarlo,et al. Digital feedback in superconducting quantum circuits , 2015, 1508.01385.