Time-series quantum reservoir computing with weak and projective measurements

[1]  M. C. Soriano,et al.  Scalable Photonic Platform for Real-Time Quantum Reservoir Computing , 2022, Physical Review Applied.

[2]  R. Kueng,et al.  The randomized measurement toolbox , 2022, Nature Reviews Physics.

[3]  Pere Mujal.  Quantum Reservoir Computing for Speckle Disorder Potentials , 2022, Condensed Matter.

[4]  M. C. Soriano,et al.  High-Performance Reservoir Computing With Fluctuations in Linear Networks , 2021, IEEE Transactions on Neural Networks and Learning Systems.

[5]  K. Pradel,et al.  Natural quantum reservoir computing for temporal information processing , 2021, Scientific Reports.

[6]  G. Ribeill,et al.  Nonlinear input transformations are ubiquitous in quantum reservoir computing , 2021, Neuromorph. Comput. Eng..

[7]  A. Crespi,et al.  Experimental photonic quantum memristor , 2021, Nature Photonics.

[8]  R. Zambrini,et al.  Quantum thermodynamics under continuous monitoring: a general framework , 2021, 2112.02019.

[9]  M. C. Soriano,et al.  Analytical evidence of nonlinearity in qubits and continuous-variable quantum reservoir computing , 2021, Journal of Physics: Complexity.

[10]  H. Tureci,et al.  Physical reservoir computing using finitely-sampled quantum systems , 2021, 2110.13849.

[11]  B. Neyenhuis,et al.  Realization of Real-Time Fault-Tolerant Quantum Error Correction , 2021, Physical Review X.

[12]  Erik Bollt,et al.  Next generation reservoir computing , 2021, Nature Communications.

[13]  P. Barkoutsos,et al.  Learning to Measure: Adaptive Informationally Complete Generalized Measurements for Quantum Algorithms , 2021, PRX Quantum.

[14]  Kohei Nakajima,et al.  Learning Temporal Quantum Tomography , 2021, Physical review letters.

[15]  Miguel C. Soriano,et al.  Gaussian states of continuous-variable quantum systems provide universal and versatile reservoir computing , 2021, Communications Physics.

[16]  M. C. Soriano,et al.  Dynamical Phase Transitions in Quantum Reservoir Computing. , 2021, Physical review letters.

[17]  Miguel C. Soriano,et al.  Opportunities in Quantum Reservoir Computing and Extreme Learning Machines , 2021, Advanced Quantum Technologies.

[18]  G. Foletto,et al.  Experimental test of sequential weak measurements for certified quantum randomness extraction , 2021, 2101.12074.

[19]  H. Neven,et al.  Machine learning of high dimensional data on a noisy quantum processor , 2021, npj Quantum Information.

[20]  Jian-Wei Pan,et al.  An integrated space-to-ground quantum communication network over 4,600 kilometres , 2021, Nature.

[21]  M. Cerezo,et al.  Variational quantum algorithms , 2020, Nature Reviews Physics.

[22]  K. Temme,et al.  A rigorous and robust quantum speed-up in supervised machine learning , 2020, Nature Physics.

[23]  J. Teufel,et al.  Control and readout of a superconducting qubit using a photonic link , 2020, Nature.

[24]  Andrzej Opala,et al.  Reconstructing Quantum States With Quantum Reservoir Networks , 2020, IEEE Transactions on Neural Networks and Learning Systems.

[25]  T. Paterek,et al.  Realising and compressing quantum circuits with quantum reservoir computing , 2020, Communications Physics.

[26]  C. Bergmeir,et al.  Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions , 2019, International Journal of Forecasting.

[27]  Nakajima Kohei,et al.  Reservoir Computing: Theory, Physical Implementations, and Applications , 2018, Reservoir Computing.

[28]  Keisuke Fujii,et al.  Toward NMR Quantum Reservoir Computing , 2021, Reservoir Computing.

[29]  M. C. Soriano,et al.  Information Processing Capacity of Spin-Based Quantum Reservoir Computing Systems , 2020, Cognitive Computation.

[30]  J. Grollier,et al.  Quantum neuromorphic computing , 2020, Applied Physics Letters.

[31]  L. C. G. Govia,et al.  Quantum reservoir computing with a single nonlinear oscillator , 2020, Physical Review Research.

[32]  E. Eleftheriou,et al.  Memory devices and applications for in-memory computing , 2020, Nature Nanotechnology.

[33]  H. Nurdin,et al.  Temporal Information Processing on Noisy Quantum Computers , 2020, Physical Review Applied.

[34]  Stanislav Straupe,et al.  Experimental neural network enhanced quantum tomography , 2019, npj Quantum Information.

[35]  Valentin Flunkert,et al.  DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks , 2017, International Journal of Forecasting.

[36]  J. Williams,et al.  Complex , 2020, Encyclopedic Dictionary of Archaeology.

[37]  Tomasz Paterek,et al.  Quantum Neuromorphic Platform for Quantum State Preparation. , 2019, Physical review letters.

[38]  N. Davidson,et al.  Weak-to-strong transition of quantum measurement in a trapped-ion system , 2019 .

[39]  John C. Platt,et al.  Quantum supremacy using a programmable superconducting processor , 2019, Nature.

[40]  Seung Hwan Lee,et al.  Temporal data classification and forecasting using a memristor-based reservoir computing system , 2019, Nature Electronics.

[41]  M. Naghiloo,et al.  Introduction to Experimental Quantum Measurement with Superconducting Qubits , 2019, 1904.09291.

[42]  Andrzej Opala,et al.  Quantum reservoir processing , 2018, npj Quantum Information.

[43]  Chiara Macchiavello,et al.  An artificial neuron implemented on an actual quantum processor , 2018, npj Quantum Information.

[44]  Brian Skinner,et al.  Measurement-Induced Phase Transitions in the Dynamics of Entanglement , 2018, Physical Review X.

[45]  Toshiyuki Yamane,et al.  Recent Advances in Physical Reservoir Computing: A Review , 2018, Neural Networks.

[46]  Shu-Hao Wu,et al.  Quantum generative adversarial learning in a superconducting quantum circuit , 2018, Science Advances.

[47]  Kristan Temme,et al.  Supervised learning with quantum-enhanced feature spaces , 2018, Nature.

[48]  R. Schoelkopf,et al.  To catch and reverse a quantum jump mid-flight , 2018, Nature.

[49]  Seth Lloyd,et al.  Advances in photonic quantum sensing , 2018, Nature Photonics.

[50]  Matthew P. A. Fisher,et al.  Quantum Zeno effect and the many-body entanglement transition , 2018, Physical Review B.

[51]  Kohei Nakajima,et al.  Machine learning with controllable quantum dynamics of a nuclear spin ensemble in a solid , 2018, 1806.10910.

[52]  Juan-Pablo Ortega,et al.  Echo state networks are universal , 2018, Neural Networks.

[53]  Jaideep Pathak,et al.  Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing Approach. , 2018, Physical review letters.

[54]  John Preskill,et al.  Quantum Computing in the NISQ era and beyond , 2018, Quantum.

[55]  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..

[56]  Damien Querlioz,et al.  Vowel recognition with four coupled spin-torque nano-oscillators , 2017, Nature.

[57]  H. Fan,et al.  Emulating Many-Body Localization with a Superconducting Quantum Processor. , 2017, Physical review letters.

[58]  Jun Li,et al.  Quantum Image Processing and Its Application to Edge Detection: Theory and Experiment , 2017, 1801.01465.

[59]  C. Monroe,et al.  Observation of a many-body dynamical phase transition with a 53-qubit quantum simulator , 2017, Nature.

[60]  Laurent Larger,et al.  High-Speed Photonic Reservoir Computing Using a Time-Delay-Based Architecture: Million Words per Second Classification , 2017 .

[61]  Jacob biamonte,et al.  Quantum machine learning , 2016, Nature.

[62]  F. Kschischang,et al.  Roadmap of optical communications , 2015, 1507.05157.

[63]  Zoran Konkoli,et al.  On Reservoir Computing: From Mathematical Foundations to Unconventional Applications , 2017 .

[64]  F. Reinhard,et al.  Quantum sensing , 2016, 1611.02427.

[65]  M. Clusel,et al.  The role of quantum measurement in stochastic thermodynamics , 2016, 1607.02404.

[66]  Aaron C. E. Lee,et al.  Many-body localization in a quantum simulator with programmable random disorder , 2015, Nature Physics.

[67]  K. Murch,et al.  Quantum trajectories of superconducting qubits , 2015, 1506.08165.

[68]  Claudio Gallicchio,et al.  Human activity recognition using multisensor data fusion based on Reservoir Computing , 2016, J. Ambient Intell. Smart Environ..

[69]  Michael I. Jordan,et al.  Machine learning: Trends, perspectives, and prospects , 2015, Science.

[70]  Markus Aspelmeyer,et al.  Optimal State Estimation for Cavity Optomechanical Systems. , 2015, Physical review letters.

[71]  Jiangfeng Du,et al.  Experimental realization of a quantum support vector machine. , 2015, Physical review letters.

[72]  Chaoyang Lu,et al.  Entanglement-based machine learning on a quantum computer. , 2014, Physical review letters.

[73]  C. Macklin,et al.  Observing single quantum trajectories of a superconducting quantum bit , 2013, Nature.

[74]  Daniel Brunner,et al.  Parallel photonic information processing at gigabyte per second data rates using transient states , 2013, Nature Communications.

[75]  R. J. Schoelkopf,et al.  Quantum Back-Action of an Individual Variable-Strength Measurement , 2013, Science.

[76]  Stuart Moss,et al.  Current Status and Future Directions , 2013 .

[77]  Kae Nemoto,et al.  Quantum communication without the necessity of quantum memories , 2012, Nature Photonics.

[78]  Aephraim M. Steinberg,et al.  Observing the Average Trajectories of Single Photons in a Two-Slit Interferometer , 2011, Science.

[79]  Claus Kiefer,et al.  Quantum Measurement and Control , 2010 .

[80]  D. Gross,et al.  Efficient quantum state tomography. , 2010, Nature communications.

[81]  Alexander Hentschel,et al.  Machine learning for precise quantum measurement. , 2009, Physical review letters.

[82]  S. Girvin,et al.  Introduction to quantum noise, measurement, and amplification , 2008, 0810.4729.

[83]  R. Johnsen,et al.  Theory and Experiment , 2010 .

[84]  Herbert Jaeger,et al.  Reservoir computing approaches to recurrent neural network training , 2009, Comput. Sci. Rev..

[85]  S. Deleglise,et al.  Quantum jumps of light recording the birth and death of a photon in a cavity , 2006, Nature.

[86]  E. Knill,et al.  Realization of quantum error correction , 2004, Nature.

[87]  Harald Haas,et al.  Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication , 2004, Science.

[88]  Francesco Petruccione,et al.  The Theory of Open Quantum Systems , 2002 .

[89]  R. Xu,et al.  Theory of open quantum systems , 2002 .

[90]  T. Brun.  A simple model of quantum trajectories , 2001, quant-ph/0108132.

[91]  Herbert Jaeger,et al.  The''echo state''approach to analysing and training recurrent neural networks , 2001 .

[92]  A. S. Weigend,et al.  Results of the time series prediction competition at the Santa Fe Institute , 1993, IEEE International Conference on Neural Networks.

[93]  Zengo Furukawa,et al.  A General Framework for , 1991 .

[94]  Hübner,et al.  Dimensions and entropies of chaotic intensity pulsations in a single-mode far-infrared NH3 laser. , 1989, Physical review. A, General physics.

[95]  Wineland,et al.  Observation of quantum jumps in a single atom. , 1986, Physical review letters.

[96]  Robert C. Wolpert,et al.  A Review of the , 1985 .