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ABSTRACTS OF ARTICLES OF THE JOURNAL "INFORMATION TECHNOLOGIES".
No. 10. Vol. 29. 2023
DOI: 10.17587/it.29.512-521
Samir Khalid Akhmed, PhD student, S. V. Skorodumov, Cand. of Tech. Sc., Senior Researcher,
Moscow Aviation Institute (National Research University),
Sabrin Khalid Akhmed, Student,
I. M. Sechenov First Moscow State Medical University
Quantum Neural Networks in the Problem of Pattern Recognition
Three computational models are considered: classical, hybrid (NISQ) and quantum computational models, their pros, cons, possibilities of implementation in modern realities and the problem of image classification and its solution using neural networks in these computational models. Three computational experiments were carried out using the described image recognition approaches with visualization of the learning process and a comparison of the final metrics was carried out aimed at clarifying the prospects of the applied approach based on quantum computing.
Keywords: quantum computational model, classical computational model, convolutional neural networks, quantum convolutional layer, NISQ, unitary operations, rotation matrices, quantum circuits
P.
512-521
References
- Turing A. M. On Computable Numbers, With An application to the Entscheidunsproblem, London Mathematical Society, 1937, pp. 230—265.
- Church A. An Unsolvable Problem of Elementary Number Theory, American Journal of Mathematics, 1936, vol. 58, no. 58, pp. 345—363, doi:10.2307/2371045
- 3. Aspray W. John von Neumann and the Origins of Modern Computing, MIT Press, 1990, 394 p.
- Lojek B. History of Semiconductor Engineering, New York, Springer-Verlag Berlin Heidelberg, 2007.
- Moore G. E. No Exponential is Forever: But "Forever" Can Be Delayed!, International Solid-State Circuits Conference (ISSCC), 2003, SESSION 1, PLENARY 1.1.
- Moore S. K. Landauer Limit Demonstrated. Scientists show that a 50-year-old principle limiting future CMOS computing is real: Erasing information gives off heat, IEEE Spectrum, 7 Mar 2012.
- Nielsen M. A., Chuang I. L. Quantum computing and quantum information, Moscow, Mir, 2006, 824 p.
- Abobeih M. H., Wang Y., Randall J. Fault-tolerant operation of a logical qubit in a diamond quantum processor, Nature, 2022, vol. 606, pp. 884—889, https://doi.org/10.1038/s41586-022-04819-6
- Xue X., Russ M., Samkharadze N. et al. Quantum logic with spin qubits crossing the surface code threshold, Nature, 2022, vol. 601, pp. 343—347, https://doi.org/10.1038/s41586-021-04273-w
- Preskill J. Quantum Computing in the NISQ era and beyond, Quantum, 2018, no. 2, pp. 78, https://doi.org/10.22331/q-2018-08-06-79
- Schuld M., Petruccione F. Supervised Learning with Quantum Computers, Springer, 2018, 287 p., doi:10.1007/978-3-319-96424-9
- Mari A., Bromley T. R., Killoran N. Estimating the gradient and higher-order derivatives on quantum hardware, Physical Review A, 2021, vol. 103, no. 1, pp. 012405, available at: https:// arxiv.org/abs/2008.06517 (date of access: 26.02.2021), doi:10.1103/physreva.103.012405.
- IBM Quantum Experience [Electronic resource], available at: https://quantum-computing.ibm.com/
- Qiskit Aer Backend [Electronic resource], Qiskit IBM Quantum Computing, available at: https://qiskit.org/documentation/tutorials/simulators/1_aer_provider.html/ 02.12.2022
- Henderson M., Shakya S., Pradhan S., Cook T. Quan-volutional Neural Networks: Powering Image Recognition with Quantum Circuits, Arxiv quantph, available at: https://arxiv.org/pdf/1904.04767.pdf (date of access: 9.04.2019), https://doi.org/10.48550/arXiv.1904.04767
- Akhmed S. H., Skorodumov S. V. The use of neural network approaches in the diagnosis of diseases, Modeling anddata analysis, 2020, vol. 10, no. 2, pp. 49—61, doi:10.17759/ mda.2020100204
- Havlicek V., C rcoles A. D., Temme K., Harrow A. W., Kandala A., Chow J. M., Gambetta J. M. Supervised learning with quantum enhanced feature spaces, Nature, 2019, no. 567, pp. 209—212, doi.org:10.1038/s41586-019-0980-2
- Preskill J. Quantum Computing in the NISQ era and beyond, Quantum, 2018, no. 2, pp. 78, https://doi.org/10.22331/q-2018-08-06-79
- Nikolenko S., Kadurin A., Arkhangelskaya E. Deep learning, St. Petersburg, Peter, 2019, 480 p.
- Peruzzo A., McClean J., Shadbolt P., Yung M.-H., Zhou X.-Q., Love P. J., Aspuru-Guzik A., O'Brien J. L. A variational eigenvalue solver on a quantum processor, Nature Communications,2014, no. 5, pp. 4213, doi.org:10.1038/ncomms5213, arXiv:1304.3061.
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