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1707
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
1708
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
1709
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
1710
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
1711
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/
1712
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
1713
Fedorov A. K., Beloussov S. M. (2021). Quantum computing at the quantum advantage threshold / Unpublished paper.
1714
* Дит — единица количества информации, содержащейся в сообщении о данном состоянии системы, имеющей десять равновероятных состояний.
1715
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 Quantum computing at the quantum advantage threshold / Unpublished paper.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
1718
Schuld M., Sinayskiy I., Petruccione F. (2016). Prediction by linear regression on a quantum computer // https://arxiv.org/abs/1601.07823
1719
Li G., Wang Y., Luo Y., Feng Y. (2019). Quantum Data Fitting Algorithm for Non-sparse Matrices // https://arxiv.org/abs/1907.06949
1720
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
1721
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
1722
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
1723
Chatterjee R., Yu T. (2016). Generalized Coherent States, Reproducing Kernels, and Quantum Support Vector Machines // https://arxiv.org/abs/1612.03713
1724
Schuld M., Killoran N. (2018). Quantum machine learning in feature Hilbert spaces // https://arxiv.org/abs/1803.07128
1725
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
1726
Srinivasan S., Gordon G., Boots B. (2017). Learning Hidden Quantum Markov Models // https://arxiv.org/abs/1710.09016
1727
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
1728
Dumoulin V., Goodfellow I. J., Courville A., Bengio Y. (2013). On the Challenges of Physical Implementations of RBMs // https://arxiv.org/abs/1312.5258
1729
Wiebe N., Kapoor A., Svore K. M. (2014). Quantum Deep Learning // https://arxiv.org/abs/1412.3489
1730
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
1731
Amin M. H., Andriyash E., Rolfe J., Kulchytskyy B., Melko R. (2016). Quantum Boltzmann Machine // https://arxiv.org/abs/1601.02036
1732
Anschuetz E. R., Cao Y. (2019). Realizing Quantum Boltzmann Machines Through Eigenstate Thermalization / https://arxiv.org/abs/1903.01359
1733
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
1734
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
1735
Chen S. E.-C., Yoo S., Fang Y.-L. L. (2020). Quantum Long Short-Term Memory // https://arxiv.org/abs/2009.01783
1736
Di Sipio R. (2021). Toward a Quantum Transformer / Towards Data Science, Jan 10, 2021 // https://towardsdatascience.com/toward-a-quantum-transformer-a51566ed42c2
1737
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
1738
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
1739
Cao Y., Guerreschi G.