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Quantum extreme learning machines (QELMs) leverage untrained quantum dynamics to efficiently process information encoded in input quantum states, avoiding the high computational cost of training more complicated nonlinear models.
Bounds for the quantity of information transmitted by a quantum communication channel
Holevo, A. S · 1973
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Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication
Jaeger, H. & Haas, H · 2004
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Extreme learning machine: a new learning scheme of feedforward neural networks
Huang, G.-B., Zhu, Q.-Y. & Siew, C.-K · 2004
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Regression diagnostics: Identifying influential data and sources of collinearity (John Wiley & Sons, 2005)
Belsley, D. A., Kuh, E. & Welsch, R. E · 2005
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Black holes as mirrors: quantum information in random subsystems
Hayden, P. & Preskill, J · 2007
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Fast scramblers
Sekino, Y. & Susskind, L · 2008
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Reservoir computing approaches to recurrent neural network training
Lukoševičius, M. & Jaeger, H · 2009
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Matrices with entries in a principal ideal domain; jordan reduction
Serre, D · 2010
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Quantum Computation and Quantum Information: 10th Anniversary Edition (Cambridge University Press, 2010)
Nielsen, A., M., Chuang & L., I · 2010
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Extreme learning machines: a survey
Huang, G.-B., Wang, D. H. & Lan, Y · 2011
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A Practical Guide to Applying Echo State Networks
Lukoševičius, M · 2012
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DNA Reservoir Computing: A Novel Molecular Computing Approach
Goudarzi, A., Lakin, M. R. & Stefanovic, D · 2013
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Towards the fast scrambling conjecture
Lashkari, N., Stanford, D., Hastings, M., Osborne, T. & Hayden, P · 2013
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Black holes and the butterfly effect
Shenker, S. H. & Stanford, D · 2014
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Delay-based reservoir computing: Noise effects in a combined analog and digital implementation
Soriano, M. C. et al · 2015
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Information processing via physical soft body
Nakajima, K., Hauser, H., Li, T. & Pfeifer, R · 2015
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Localized shocks
Roberts, D. A., Stanford, D. & Susskind, L · 2015
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Chaos in quantum channels
Hosur, P., Qi, X.-L., Roberts, D. A. & Yoshida, B · 2016
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Measuring the scrambling of quantum information
Swingle, B., Bentsen, G., Schleier-Smith, M. & Hayden, P · 2016
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A bound on chaos
Maldacena, J., Shenker, S. H. & Stanford, D · 2016
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Deep Learning (2016), mit press edn
Goodfellow, I., Bengio, Y. & Courville, A · 2016
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On Reservoir Computing: From Mathematical Foundations to Unconventional Applications
Konkoli, Z · 2017
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Computing with networks of nonlinear mechanical oscillators
Coulombe, J. C., York, M. C. A. & Sylvestre, J · 2017
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Thermodynamics of quantum information scrambling
Campisi, M. & Goold, J · 2017
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Out-of-time-order correlators in quantum mechanics
Hashimoto, K., Murata, K. & Yoshii, R · 2017
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An overview of gradient descent optimization algorithms (2017)
Ruder, S · 2017
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Measuring Out-of-Time-Order Correlators on a Nuclear Magnetic Resonance Quantum Simulator
Li, J. et al · 2017
Cited alongside, same era.
Unscrambling the physics of out-of-time-order correlators
Swingle, B · 2018
Cited alongside, same era.
Quasiprobability behind the out-of-time-ordered correlator
Halpern, N. Y., Swingle, B. & Dressel, J · 2018
Cited alongside, same era.
Semiclassical theory of out-of-time-order correlators for low-dimensional classically chaotic systems
Jalabert, R. A., García-Mata, I. & Wisniacki, D. A · 2018
Cited alongside, same era.
The theory of quantum information (Cambridge university press, 2018)
Reconstructing Quantum States With Quantum Reservoir Networks
S. Ghosh, A. Opala, M. Matuszewski, T. Paterek & T. C. H. Liew · 2021
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Creating and concentrating quantum resource states in noisy environments using a quantum neural network
Krisnanda, T., Ghosh, S., Paterek, T. & Liew, T. C. H · 2021
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Information scrambling and chaos in open quantum systems
Zanardi, P. & Anand, N · 2021
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A review on extreme learning machine
Wang, J., Lu, S., Wang, S.-H. & Zhang, Y.-D · 2022
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High-Performance Reservoir Computing With Fluctuations in Linear Networks
Nokkala, J., Martínez-Peña, R., Zambrini, R. & Soriano, M. C · 2022
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Quantum information scrambling in quantum many-body scarred systems
Yuan, D., Zhang, S.-Y., Wang, Y., Duan, L.-M. & Deng, D.-L · 2022
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Watrous, J · 2018
Cited alongside, same era.
Recent advances in physical reservoir computing: A review
Tanaka, G. et al · 2019
Cited alongside, same era.
Quantum Neuromorphic Platform for Quantum State Preparation
Ghosh, S., Paterek, T. & Liew, T. C · 2019
Cited alongside, same era.
Quantum reservoir processing
Ghosh, S., Opala, A., Matuszewski, M., Paterek, T. & Liew, T. C. H · 2019
Cited alongside, same era.
Scrambling and complexity in phase space
Zhuang, Q., Schuster, T., Yoshida, B. & Yao, N. Y · 2019
Cited alongside, same era.
Verified quantum information scrambling
Landsman, K. A. et al · 2019
Cited alongside, same era.
Tripartite information, scrambling, and the role of Hilbert space partitioning in quantum lattice models
Schnaack, O. et al · 2019
Cited alongside, same era.
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Phase-transition-like behavior in information retrieval of a quantum scrambled random circuit system
Zhuang, J.-Z., Wu, Y.-K. & Duan, L.-M · 2022
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The reservoir learning power across quantum many-body localization transition
Xia, W., Zou, J., Qiu, X. & Li, X · 2022
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Experimental measurement of out-of-time-ordered correlators at finite temperature
Green, A. M. et al · 2022
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Holevo information and ensemble theory of gravity
Qi, X.-L., Shangnan, Z. & Yang, Z · 2022
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Quantum reservoir computing in finite dimensions
Martínez-Peña, R. & Ortega, J.-P · 2023
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Time-series quantum reservoir computing with weak and projective measurements
Mujal, P., Martínez-Peña, R., Giorgi, G. L., Soriano, M. C. & Zambrini, R · 2023
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Scalable Photonic Platform for Real-Time Quantum Reservoir Computing
García-Beni, J., Giorgi, G. L., Soriano, M. C. & Zambrini, R · 2023
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On fundamental aspects of quantum extreme learning machines (2023)
Xiong, W. et al · 2023
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Potential and limitations of quantum extreme learning machines
Innocenti, L. et al · 2023
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An operational definition of quantum information scrambling (2023)
Lo Monaco, G. et al · 2023
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Shadow tomography on general measurement frames
Innocenti, L. et al · 2023
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Enhancing the performance of quantum reservoir computing and solving the time-complexity problem by artificial memory restriction
Čindrak, S., Donvil, B., Lüdge, K. & Jaurigue, L · 2024
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Krylov expressivity in quantum reservoir computing and quantum extreme learning
Čindrak, S., Jaurigue, L. & Lüdge, K · 2024
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Experimental property reconstruction in a photonic quantum extreme learning machine
Suprano, A. et al · 2024
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Scrambling dynamics and out-of-time-ordered correlators in quantum many-body systems
Xu, S. & Swingle, B · 2024
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Quasiprobabilities in quantum thermodynamics and many-body systems
Gherardini, S. & De Chiara, G · 2024
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Information scrambling–a quantum thermodynamic perspective
Touil, A. & Deffner, S · 2024
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Quantum scrambling via accessible tripartite information
Monaco, G. L. et al · 2058
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