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F., Dresselhaus P. D., Benz S. P., Rippard W. H. (2018). Ultralow power artificial synapses using nanotextured magnetic Josephson junctions / Science Advances, Vol. 4, no. 1 // https://doi.org/10.1126/sciadv.1701329

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Chen Y., Zhou Y., Zhuge F., Tian B., Yan M., Li Y., He Y., Shui Miao X. (2019). Graphene–ferroelectric transistors as complementary synapses for supervised learning in spiking neural network / npj 2D Materials and Applications, 3, 31 // https://doi.org/10.1038/s41699-019-0114-6

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Sanchez Esqueda I., Yan X., Rutherglen C., Kane A., Cain T., Marsh P., Liu Q., Galatsis K., Wang H., Zhou C. (2018). Aligned Carbon Nanotube Synaptic Transistors for Large-Scale Neuromorphic Computing / ACS Nano, Vol. 12, Iss. 7, pp. 7352—7361 // https://doi.org/10.1021/acsnano.8b03831

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Zhang H.-T., Park T. J., Islam A. N. M. N., Tran D. S. J., Manna S., Wang Q., Mondal S., Yu H., Banik S., Cheng S., Zhou H., Gamage S., Mahapatra S., Zhu Y., Abate Y., Jiang N., Sankaranarayanan S. K. R. S., Sengupta A., Teuscher C., Ramanathan S. (2022). Reconfigurable perovskite nickelate electronics for artificial intelligence / Science, Vol. 375, Iss. 6580, pp. 533—539 // https://doi.org/10.1126/science.abj7943

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Tasić M., Ivković J., Carlström G., Melcher M., Bollella P., Bendix J., Gorton L., Persson P., Uhlig J., Strand D. (2022). Electro-mechanically switchable hydrocarbons based on [8]annulenes / Nature Communications, Vol. 13, Iss. 860 // https://doi.org/10.1038/s41467-022-28384-8

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Gent E. (2022). MIT Researchers Create Artificial Synapses 10,000x Faster Than Biological Ones. / Singularity hub, August 1, 2022 // https://singularityhub.com/2022/08/01/mit-researchers-created-artificial-synapses-10000x-faster-than-biological-ones/

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Onen M., Emond N., Wang B., Zhang D., Ross F. M., Li J., Yildiz B., Del Alamo J. A. (2022). Nanosecond protonic programmable resistors for analog deep learning // https://doi.org/10.1126/science.abp8064

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Fedorov A. K., Beloussov S. M. (2021). Quantum computing at the quantum advantage threshold / Unpublished paper.

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* Дит — единица количества информации, содержащейся в сообщении о данном состоянии системы, имеющей десять равновероятных состояний.

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Wang Y., Hu Z., Sanders B. C., Kais S. (2020). Qudits and high-dimensional quantum computing // https://arxiv.org/abs/2008.00959

1716

Fedorov A. K., Beloussov S. M. (2021). Quantum computing at the quantum advantage threshold / Unpublished paper.

1717

Wang G. (2014). Quantum Algorithm for Linear Regression // https://arxiv.org/abs/1402.0660

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Schuld M., Sinayskiy I., Petruccione F. (2016). Prediction by linear regression on a quantum computer // https://arxiv.org/abs/1601.07823

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Li G., Wang Y., Luo Y., Feng Y. (2019). Quantum Data Fitting Algorithm for Non-sparse Matrices // https://arxiv.org/abs/1907.06949

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Dutta S., Suau A., Dutta S., Roy S., Behera B. K., Panigrahi P. K. (2020). Quantum circuit design methodology for multiple linear regression / IET Quantum Communication, Vol. 1, Iss. 2, pp. 55-61 // https://doi.org/10.1049/iet-qtc.2020.0013

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Lu S., Braunstein S. L. (2014). Quantum decision tree classifier / Quantum Information Processing, Vol. 13, pp. 757—770 // https://doi.org/10.1007/s11128-013-0687-5

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Rebentrost P., Mohseni M., Lloyd S. (2014). Quantum Support Vector Machine for Big Data Classification / Physical Review Letters, Vol. 113, Iss. 13 // https://doi.org/10.1103/PhysRevLett.113.130503

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Chatterjee R., Yu T. (2016). Generalized Coherent States, Reproducing Kernels, and Quantum Support Vector Machines // https://arxiv.org/abs/1612.03713

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Schuld M., Killoran N. (2018). Quantum machine learning in feature Hilbert spaces // https://arxiv.org/abs/1803.07128

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Monras A., Beige A., Wiesner K. (2010). Hidden Quantum Markov Models and non-adaptive read-out of many-body states // https://arxiv.org/abs/1002.2337

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Srinivasan S., Gordon G., Boots B. (2017). Learning Hidden Quantum Markov Models // https://arxiv.org/abs/1710.09016

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Denil M., de Freitas N. (2011). Toward the Implementation of a Quantum RBM / NIPS 2011 Deep Learning and Unsupervised Feature Learning Workshop // https://www.cs.ubc.ca/~nando/papers/quantumrbm.pdf

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Dumoulin V., Goodfellow I. J., Courville A., Bengio Y. (2013). On the Challenges of Physical Implementations of RBMs // https://arxiv.org/abs/1312.5258

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Wiebe N., Kapoor A., Svore K. M. (2014). Quantum Deep Learning // https://arxiv.org/abs/1412.3489

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Benedetti M., Realpe-Gómez J., Biswas R., Perdomo-Ortiz A. (2015). Estimation of effective temperatures in quantum annealers for sampling applications: A case study with possible applications in deep learning // https://arxiv.org/abs/1510.07611

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Amin M. H., Andriyash E., Rolfe J., Kulchytskyy B., Melko R. (2016). Quantum Boltzmann Machine // https://arxiv.org/abs/1601.02036

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Anschuetz E. R., Cao Y. (2019). Realizing Quantum Boltzmann Machines Through Eigenstate Thermalization / https://arxiv.org/abs/1903.01359

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Khoshaman A., Vinci W., Denis B., Andriyash E., Sadeghi H., Amin M. H. (2018). Quantum variational autoencoder / Quantum Science and Technology, Vol. 4, No. 1 // https://iopscience.iop.org/article/10.1088/2058-9565/aada1f

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Cong I., Choi S., Lukin M. D. (2019). Quantum convolutional neural networks / Nature Physics, Vol. 15, pp. 1273—1278 // https://doi.org/10.1038/s41567-019-0648-8

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Chen S. E.-C., Yoo S., Fang Y.-L. L. (2020). Quantum Long Short-Term Memory // https://arxiv.org/abs/2009.01783

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Di Sipio R. (2021). Toward a Quantum Transformer / Towards Data Science, Jan 10, 2021 // https://towardsdatascience.com/toward-a-quantum-transformer-a51566ed42c2

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Kak S. C. (1995). Quantum Neural Computing / Advances in Imaging and Electron Physics, Vol. 94, pp. 259—313 // https://doi.org/10.1016/S1076-5670(08)70147-2

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Zak M., Williams C. P. (1998). Quantum Neural Nets / International Journal of Theoretical Physics, Vol. 37, pp. 651—684 // https://doi.org/10.1023/A:1026656110699

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Cao Y., Guerreschi G.

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