Gaussian states provide universal and versatile quantum reservoir computing
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M. C. Soriano | Roberta Zambrini | Gian Luca Giorgi | Valentina Parigi | Johannes Nokkala | Rodrigo Mart'inez-Pena | Miguel C. Soriano | R. Zambrini | G. Giorgi | V. Parigi | J. Nokkala | R. Mart'inez-Pena
[1] Mantas Lukosevicius,et al. A Practical Guide to Applying Echo State Networks , 2012, Neural Networks: Tricks of the Trade.
[2] Herbert Jaeger,et al. Reservoir computing approaches to recurrent neural network training , 2009, Comput. Sci. Rev..
[3] Jiayin Chen,et al. Learning nonlinear input–output maps with dissipative quantum systems , 2019, Quantum Information Processing.
[4] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[5] Benjamin Schrauwen,et al. Information Processing Capacity of Dynamical Systems , 2012, Scientific Reports.
[6] Changpeng Shao,et al. A quantum model of feed-forward neural networks with unitary learning algorithms , 2020, Quantum Information Processing.
[7] Su Yang,et al. An echo state network architecture based on quantum logic gate and its optimization , 2020, Neurocomputing.
[8] Serge Massar,et al. All-optical Reservoir Computing , 2012, Optics express.
[9] Laurent Larger,et al. High-Speed Photonic Reservoir Computing Using a Time-Delay-Based Architecture: Million Words per Second Classification , 2017 .
[10] John Preskill,et al. Quantum Computing in the NISQ era and beyond , 2018, Quantum.
[11] Tara N. Sainath,et al. Deep Neural Networks for Acoustic Modeling in Speech Recognition , 2012 .
[12] E. Torrontegui,et al. Unitary quantum perceptron as efficient universal approximator , 2018, EPL (Europhysics Letters).
[13] Kae Nemoto,et al. Efficient classical simulation of continuous variable quantum information processes. , 2002, Physical review letters.
[14] Hitoshi Kubota,et al. Role of non-linear data processing on speech recognition task in the framework of reservoir computing , 2019, Scientific Reports.
[15] Naoki Yamamoto,et al. Temporal Information Processing on Noisy Quantum Computers , 2020, ArXiv.
[16] Herbert Jaeger,et al. A tutorial on training recurrent neural networks , covering BPPT , RTRL , EKF and the " echo state network " approach - Semantic Scholar , 2005 .
[17] Andrzej Opala,et al. Quantum reservoir processing , 2018, npj Quantum Information.
[18] Davide Pierangeli,et al. Programming multi-level quantum gates in disordered computing reservoirs via machine learning , 2019, Optics express.
[19] Mário Ziman,et al. Programmable Quantum Gate Arrays , 2001 .
[20] M. C. Soriano,et al. Advances in photonic reservoir computing , 2017 .
[21] Scott Aaronson,et al. The computational complexity of linear optics , 2010, STOC '11.
[22] Debanjan Bhowmik,et al. Supervised learning with a quantum classifier using multi-level systems , 2019, Quantum Inf. Process..
[23] Tobias J. Osborne,et al. Training deep quantum neural networks , 2020, Nature Communications.
[24] Herbert Jaeger,et al. The''echo state''approach to analysing and training recurrent neural networks , 2001 .
[25] Henry Markram,et al. Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations , 2002, Neural Computation.
[26] Peter Wittek,et al. Quantum Machine Learning: What Quantum Computing Means to Data Mining , 2014 .
[27] Geert Morthier,et al. Experimental demonstration of reservoir computing on a silicon photonics chip , 2014, Nature Communications.
[28] Jean-Pierre Martens,et al. Large Vocabulary Continuous Speech Recognition With Reservoir-Based Acoustic Models , 2014, IEEE Signal Processing Letters.
[29] Leon O. Chua,et al. Fading memory and the problem of approximating nonlinear operators with volterra series , 1985 .
[30] Harald Haas,et al. Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication , 2004, Science.
[31] T. Ralph,et al. Universal quantum computation with continuous-variable cluster states. , 2006, Physical review letters.
[32] Wei Lu,et al. The future of electronics based on memristive systems , 2018, Nature Electronics.
[33] I. Fuentes,et al. Generating Multimode Entangled Microwaves with a Superconducting Parametric Cavity , 2017, Physical Review Applied.
[34] Nicolas Treps,et al. Reconfigurable optical implementation of quantum complex networks , 2017, 1708.08726.
[35] Juan-Pablo Ortega,et al. Universal discrete-time reservoir computers with stochastic inputs and linear readouts using non-homogeneous state-affine systems , 2017, J. Mach. Learn. Res..
[36] Timothy C. H. Liew,et al. Universal quantum reservoir computing , 2020 .
[37] Jonathan Dong,et al. Optical Reservoir Computing Using Multiple Light Scattering for Chaotic Systems Prediction , 2019, IEEE Journal of Selected Topics in Quantum Electronics.
[38] Keisuke Fujii,et al. Boosting Computational Power through Spatial Multiplexing in Quantum Reservoir Computing , 2018, Physical Review Applied.
[39] Shane Legg,et al. Human-level control through deep reinforcement learning , 2015, Nature.
[40] Ievgeniia Oshurko. Quantum Machine Learning , 2020, Quantum Computing.
[41] Nils Bertschinger,et al. Real-Time Computation at the Edge of Chaos in Recurrent Neural Networks , 2004, Neural Computation.
[42] Kohei Nakajima,et al. Machine learning with controllable quantum dynamics of a nuclear spin ensemble in a solid , 2018, 1806.10910.
[43] Olivier Pfister,et al. Experimental realization of multipartite entanglement of 60 modes of a quantum optical frequency comb. , 2013, Physical review letters.
[44] Jeffrey L. Elman,et al. Finding Structure in Time , 1990, Cogn. Sci..
[45] E. Knill,et al. A scheme for efficient quantum computation with linear optics , 2001, Nature.
[46] Jason Cong,et al. Scaling for edge inference of deep neural networks , 2018 .
[47] Daniel Brunner,et al. Parallel photonic information processing at gigabyte per second data rates using transient states , 2013, Nature Communications.
[48] Warit Asavanant,et al. Generation of time-domain-multiplexed two-dimensional cluster state , 2019, Science.
[49] U. Böttger,et al. Beyond von Neumann—logic operations in passive crossbar arrays alongside memory operations , 2012, Nanotechnology.
[50] Wojciech M. Czarnecki,et al. Grandmaster level in StarCraft II using multi-agent reinforcement learning , 2019, Nature.
[51] Jyrki Piilo,et al. Non-Markovianity over Ensemble Averages in Quantum Complex Networks , 2017, Open Syst. Inf. Dyn..
[52] Jaideep Pathak,et al. Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing Approach. , 2018, Physical review letters.
[53] Juan-Pablo Ortega,et al. Echo state networks are universal , 2018, Neural Networks.
[54] Andrew S. Cassidy,et al. A million spiking-neuron integrated circuit with a scalable communication network and interface , 2014, Science.
[55] L. C. G. Govia,et al. Quantum reservoir computing with a single nonlinear oscillator , 2020, Physical Review Research.
[56] Albert Schliesser,et al. Multimode optomechanical system in the quantum regime , 2016, Proceedings of the National Academy of Sciences.
[57] Travis S. Humble,et al. Quantum supremacy using a programmable superconducting processor , 2019, Nature.
[58] Razvan Pascanu,et al. On the difficulty of training recurrent neural networks , 2012, ICML.
[59] Seth Lloyd,et al. Continuous-variable quantum neural networks , 2018, Physical Review Research.
[60] S. Olivares,et al. Gaussian states in continuous variable quantum information , 2005, quant-ph/0503237.
[61] Michael I. Jordan,et al. Machine learning: Trends, perspectives, and prospects , 2015, Science.
[62] M. Kubát. An Introduction to Machine Learning , 2017, Springer International Publishing.
[63] Zoran Konkoli,et al. On Reservoir Computing: From Mathematical Foundations to Unconventional Applications , 2017 .
[64] A. Friedman. Foundations of modern analysis , 1970 .
[65] H. Neven,et al. Characterizing quantum supremacy in near-term devices , 2016, Nature Physics.
[66] Daniel J. Gauthier,et al. Rapid Time Series Prediction with a Hardware-Based Reservoir Computer , 2018, Chaos.
[67] A. Crespi,et al. Integrated multimode interferometers with arbitrary designs for photonic boson sampling , 2013, Nature Photonics.
[68] Maria Schuld,et al. The quest for a Quantum Neural Network , 2014, Quantum Information Processing.
[69] Roger Melko,et al. Quantum Boltzmann Machine , 2016, 1601.02036.
[70] Gouhei Tanaka,et al. Reservoir Computing With Spin Waves Excited in a Garnet Film , 2018, IEEE Access.
[71] Gerardo Adesso,et al. Continuous Variable Quantum Information: Gaussian States and Beyond , 2014, Open Syst. Inf. Dyn..
[72] Y. Cai,et al. Multimode entanglement in reconfigurable graph states using optical frequency combs , 2017, Nature Communications.
[73] Hans-J. Briegel,et al. Machine learning \& artificial intelligence in the quantum domain , 2017, ArXiv.
[74] E. Schrödinger. Der stetige Übergang von der Mikro- zur Makromechanik , 1926, Naturwissenschaften.
[75] R. Glauber. Coherent and incoherent states of the radiation field , 1963 .
[76] Alicia J. Kollár,et al. Hyperbolic lattices in circuit quantum electrodynamics , 2018, Nature.
[77] Fabio Sciarrino,et al. Photonic implementation of boson sampling: a review , 2019, Advanced Photonics.
[78] Benjamin Schrauwen,et al. An experimental unification of reservoir computing methods , 2007, Neural Networks.
[79] Henry Markram,et al. On the computational power of circuits of spiking neurons , 2004, J. Comput. Syst. Sci..
[80] Damien Querlioz,et al. Neuromorphic computing with nanoscale spintronic oscillators , 2017, Nature.
[81] Toshiyuki Yamane,et al. Recent Advances in Physical Reservoir Computing: A Review , 2018, Neural Networks.
[82] J. Morton,et al. Magnetic resonance with squeezed microwaves , 2016, 1610.03329.