Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Influence of IQT on research in ICT

Treść / Zawartość
Warianty tytułu
Języki publikacji
This paper is written by a group of Ph.D. students pursuing their work in different areas of ICT, outside the direct area of Information Quantum Technologies IQT. An ambitious task was undertaken to research, by each co-author, a potential practical influence of the current IQT development on their current work. The research of co-authors span the following areas of ICT: CMOS for IQT, QEC, quantum time series forecasting, IQT in biomedicine. The intention of the authors is to show how quickly the quantum techniques can penetrate in the nearest future other, i.e. their own, areas of ICT.
  • Warsaw University of Technology, Warsaw, Poland
  • Warsaw University of Technology, Warsaw, Poland
  • Warsaw University of Technology, Warsaw, Poland
  • Warsaw University of Technology, Warsaw, Poland
  • Warsaw University of Technology, Warsaw, Poland
  • Warsaw University of Technology, Warsaw, Poland
  • [1] E. Chitambar and G. Gour, “Quantum resource theories,” Reviews of modern physics, vol. 91, no. 2, 2019. [Online]. Available:
  • [2] J. Preskill, “Quantum computing in the NISQ era and beyond,” Quantum, vol. 2, p. 79, 2018. [Online]. Available:
  • [3] Blokhina, Elena and et al., “CMOS Position-Based Charge Qubits: Theoretical Analysis of Control and Entanglement,” IEEE Access, vol. 8, pp. 4182–4197, 2020. [Online]. Available:
  • [4] Sebastiano, Fabio et al., “Cryogenic CMOS interfaces for quantum devices,” in 2017 7th IEEE International Workshop on Advances in Sensors and Interfaces (IWASI), 2017, pp. 59–62. [Online]. Available:
  • [5] Wang, Zewei et al., “Designing EDA-Compatible Cryogenic CMOS Platform for Quantum Computing Applications,” in 2021 5th IEEE Electron Devices Technology Manufacturing Conference (EDTM), 2021, pp. 1–3. [Online]. Available:
  • [6] Stefanovic, Danica and Kayal, Maher, Structured Analog Design. Springer Netherlands, 2008.
  • [7] Singh, Kirmender and Jain, Piyush, “BSIM3v3 to EKV2.6 Model Parameter Extraction and Optimisation using LM Algorithm on 0.18μ Technology node,” International Journal of Electronics and Telecommunications, vol. 64, pp. 5–11, 01 2018. [Online]. Available:
  • [8] Simoen, Eddy and Claeys, Cor, “Impact of CMOS processing steps on the drain current kink of NMOSFETs at liquid helium temperature,” IEEE Transactions on Electron Devices, vol. 48, no. 6, pp. 1207–1215, 2001. [Online]. Available:
  • [9] Johnson, Erik B. et al., “Characteristics of CMOS avalanche photodiodes at cryogenic temperatures,” in 2009 IEEE Nuclear Science Symposium Conference Record (NSS/MIC), 2009, pp. 2108–2114. [Online]. Available:
  • [10] L. S. Pontryagin, The Mathematical Theory of Optimal Processes. New York: Interscience, 1962.
  • [11] N. C. Jones, R. Van Meter, A. G. Fowler, P. L. McMahon, J. Kim, T. D. Ladd, and Y. Yamamoto, “Layered architecture for quantum computing,” Physical review. X, vol. 2, no. 3, p. 031007, 2012. [Online]. Available:
  • [12] P. Faist, S. Nezami, V. V. Albert, G. Salton, F. Pastawski, P. Hayden, and J. Preskill, “Continuous symmetries and approximate quantum error correction,” Physical Review X, vol. 10, no. 4, oct 2020. [Online]. Available:
  • [13] L. Egan, D. M. Debroy, C. Noel, A. Risinger, D. Zhu, D. Biswas, M. Newman, M. Li, K. R. Brown, M. Cetina, and C. Monroe, “Fault-tolerant control of an error-corrected qubit,” Nature (London), vol. 598, no. 7880, pp. 281–286, 2021. [Online]. Available:
  • [14] T. Chen, Z.-Y. Xue, and Z. D. Wang, “Error-tolerant geometric quantum control for logical qubits with minimal resource,” 2021. [Online]. Available:
  • [15] H. Levine, A. Keesling, A. Omran, H. Bernien, S. Schwartz, A. S. Zibrov, M. Endres, M. Greiner, V. Vuletić, and M. D. Lukin, “High-fidelity control and entanglement of Rydberg-atom qubits,” Physical review letters, vol. 121, no. 12, pp. 123 603–123 603, 2018. [Online]. Available:
  • [16] B. Eastin and E. Knill, “Restrictions on transversal encoded quantum gate sets,” Physical review letters, vol. 102, no. 11, pp. 110 502–110 502, 2009. [Online]. Available:
  • [17] Y. Yang, Y. Mo, J. M. Renes, G. Chiribella, and M. P. Woods, “Optimal universal quantum error correction via bounded reference frames,” 2020. [Online]. Available:
  • [18] A. M. Brańczyk, P. E. M. F. Mendonça, A. Gilchrist, A. C. Doherty, and S. D. Bartlett, “Quantum control of a single qubit,” Physical review. A, Atomic, molecular, and optical physics, vol. 75, no. 1, 2007. [Online]. Available:
  • [19] T. Shibata, S. Yamamoto, S. Nakazawa, E. H. Lapasar, K. Sugisaki, K. Maruyama, K. Toyota, D. Shiomi, K. Sato, and T. Takui, “Molecular optimization for nuclear spin state control via a single electron spin qubit by optimal microwave pulses: Quantum control of molecular spin qubits,” Applied magnetic resonance, 2021. [Online]. Available:
  • [20] T. Chen and Z.-Y. Xue, “High-fidelity and robust geometric quantum gates that outperform dynamical ones,” Physical review applied, vol. 14, no. 6, 2020. [Online]. Available:
  • [21] C.-Y. Ding, L.-N. Ji, T. Chen, and Z.-Y. Xue, “Path-optimized nonadiabatic geometric quantum computation on superconducting qubits,” 2021. [Online]. Available:
  • [22] J. M. Gambetta, J. M. Chow, and M. Steffen, “Building logical qubits in a superconducting quantum computing system,” npj Quantum Information, vol. 3, no. 1, pp. 1–7, 2017. [Online]. Available:
  • [23] J. van Dijk, E. Kawakami, R. Schouten, M. Veldhorst, L. Vandersypen, M. Babaie, E. Charbon, and F. Sebastiano, “Impact of classical control electronics on qubit fidelity,” Phys. Rev. Applied, vol. 12, p. 044054, Oct 2019. [Online]. Available:
  • [24] P. Esling and C. Agon, “Time-series data mining,” ACM Computing Surveys (CSUR), vol. 45, no. 1, pp. 1–34, 2012. [Online]. Available:
  • [25] J. G. De Gooijer and R. J. Hyndman, “25 years of time series forecasting,” International Journal of Forecasting, vol. 22, no. 3, pp. 443–473, 2006. [Online]. Available:
  • [26] V. Cerqueira, L. Torgo, and C. Soares, “Machine learning vs statistical methods for time series forecasting: Size matters,” arXiv preprint arXiv:1909.13316, 2019. [Online]. Available:
  • [27] B. Lim and S. Zohren, “Time-series forecasting with deep learning: a survey,” Philosophical Transactions of the Royal Society A, vol. 379, no. 2194, p. 20200209, 2021. [Online]. Available:
  • [28] Z. Hajirahimi and M. Khashei, “Hybrid structures in time series modeling and forecasting: A review,” Engineering Applications of Artificial Intelligence, vol. 86, pp. 83–106, 2019. [Online]. Available:
  • [29] C. R. Azevedo and T. A. Ferreira, “The application of qubit neural networks for time series forecasting with automatic phase adjustment mechanism,” in Proc. XXVII Congr. Brazilian Comput. Sci. Soc. (VI Nat. Meeting Artif. Intell.), vol. 2007, 2007, pp. 1112–1121.
  • [30] M. Benedetti, E. Lloyd, S. Sack, and M. Fiorentini, “Parameterized quantum circuits as machine learning models,” Quantum Science and Technology, vol. 4, no. 4, p. 043001, 2019. [Online]. Available:
  • [31] D. Emmanoulopoulos and S. Dimoska, “Quantum machine learning in finance: Time series forecasting,” arXiv preprint arXiv:2202.00599, 2022. [Online]. Available:
  • [32] P. Singh, G. Dhiman, S. Guo, R. Maini, H. Kaur, A. Kaur, H. Kaur, J. Singh, and N. Singh, “A hybrid fuzzy quantum time series and linear programming model: Special application on taiex index dataset,” Modern Physics Letters A, vol. 34, no. 25, p. 1950201, 2019. [Online]. Available:
  • [33] P. Singh, “FQTSFM: A fuzzy-quantum time series forecasting model,” Information Sciences, vol. 566, pp. 57–79, 2021. [Online]. Available:
  • [34] Z.-k. Feng, W.-j. Niu, Z.-y. Tang, Z.-q. Jiang, Y. Xu, Y. Liu, and H.-r. Zhang, “Monthly runoff time series prediction by variational mode decomposition and support vector machine based on quantum-behaved particle swarm optimization,” Journal of Hydrology, vol. 583, p. 124627, 2020. [Online]. Available:
  • [35] J. Kober, J. A. Bagnell, and J. Peters, “Reinforcement learning in robotics: A survey,” The International Journal of Robotics Research, vol. 32, no. 11, pp. 1238–1274, 2013. [Online]. Available:
  • [36] K. Shao, Z. Tang, Y. Zhu, N. Li, and D. Zhao, “A survey of deep reinforcement learning in video games,” CoRR, vol. abs/1912.10944, 2019. [Online]. Available:
  • [37] V. Uc-Cetina, N. Navarro-Guerrero, A. Martín-González, C. Weber, and S. Wermter, “Survey on reinforcement learning for language processing,” CoRR, vol. abs/2104.05565, 2021. [Online]. Available:
  • [38] G. Dulac-Arnold, N. Levine, D. J. Mankowitz, J. Li, C. Paduraru, S. Gowal, and T. Hester, “Challenges of real-world reinforcement learning: definitions, benchmarks and analysis,” Machine Learning, vol. 110, no. 9, pp. 2419–2468, Sep 2021. [Online]. Available:
  • [39] D. Dong, C. Chen, H. Li, and T.-J. Tarn, “Quantum reinforcement learning,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 38, no. 5, pp. 1207–1220, oct 2008. [Online]. Available:
  • [40] V. Dunjko, J. M. Taylor, and H. J. Briegel, “Quantum-enhanced machine learning,” Phys. Rev. Lett., vol. 117, p. 130501, Sep 2016. [Online]. Available:
  • [41] M. A. Nielsen and I. L. Chuang, Quantum Computation and Quantum Information: 10th Anniversary Edition, 10th ed. USA: Cambridge University Press, 2011.
  • [42] C. Ryan-Anderson, J. G. Bohnet, K. Lee, D. Gresh, A. Hankin, J. P. Gaebler, D. Francois, A. Chernoguzov, D. Lucchetti, N. C. Brown, T. M. Gatterman, S. K. Halit, K. Gilmore, J. A. Gerber, B. Neyenhuis, D. Hayes, and R. P. Stutz, “Realization of real-time fault-tolerant quantum error correction,” Phys. Rev. X, vol. 11, p. 041058, Dec 2021. [Online]. Available:
  • [43] F. Flamini, A. Hamann, S. Jerbi, L. M. Trenkwalder, H. P. Nautrup, and H. J. Briegel, “Photonic architecture for reinforcement learning,” New Journal of Physics, vol. 22, no. 4, p. 045002, apr 2020. [Online]. Available:
  • [44] L. P. Kaelbling, M. L. Littman, and A. W. Moore, “Reinforcement learning: A survey,” CoRR, vol. cs.AI/9605103, 1996. [Online]. Available:
  • [45] S. Wu, S. Jin, D. Wen, and X. Wang, “Quantum reinforcement learning in continuous action space,” 2020. [Online]. Available:
  • [46] Y. Du, M.-H. Hsieh, T. Liu, and D. Tao, “Expressive power of parametrized quantum circuits,” Physical Review Research, vol. 2, no. 3, jul 2020. [Online]. Available:
  • [47] S. Y.-C. Chen, C.-H. H. Yang, J. Qi, P.-Y. Chen, X. Ma, and H.-S. Goan, “Variational quantum circuits for deep reinforcement learning,” 2019. [Online]. Available:
  • [48] Y. Kwak, W. J. Yun, S. Jung, J.-K. Kim, and J. Kim, “Introduction to quantum reinforcement learning: Theory and PennyLane-based Implementation,” 2021. [Online]. Available:
  • [49] J.-A. Li, D. Dong, Z. Wei, Y. Liu, Y. Pan, F. Nori, and X. Zhang, “Quantum reinforcement learning during human decision-making,” Nature Human Behaviour, vol. 4, no. 3, pp. 294–307, Mar 2020. [Online]. Available:
  • [50] K. S. Shenoy, D. Y. Sheth, B. K. Behera, and P. K. Panigrahi, “Demonstration of a measurement-based adaptation protocol with quantum reinforcement learning on the IBM Q experience platform,” Quantum Information Processing, vol. 19, no. 5, p. 161, Apr 2020. [Online]. Available:
  • [51] N. Bruining, R. Barendse, and P. Cummins, “The future of computers in cardiology: ‘the connected patient’?” European Heart Journal, vol. 38, no. 23, pp. 1781–1794, 06 2017. [Online]. Available:
  • [52] K. Bertels, A. Sarkar, T. Hubregtsen., M. Serrao, A. A. Mouedenne, A. Yadav, A. Krol, and I. Ashraf, “Quantum computer architecture: Towards full-stack quantum accelerators,” in 2020 Design, Automation Test in Europe Conference Exhibition (DATE), 2020, pp. 1–6. [Online]. Available:
  • [53] S. Srinivasan, G. Gordon, and B. Boots, “Learning hidden quantum Markov models,” in Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics, ser. Proceedings of Machine Learning Research, A. Storkey and F. Perez-Cruz, Eds., vol. 84. PMLR, 4 2018, pp. 1979–1987. [Online]. Available:
  • [54] E. Prashant S., W. Jonathan, A. Alan, B. Stefan, G. Michael, M. Michael J., S. Guillermo, A.-G. Alán, B. Justin T., B. Matteo, M. John D., S. Stamatios N., T. Jacob, S. Geetha, L. Thomas, G. Mark B., and H. Aram W., “Quantum computing at the frontiers of biological sciences,” Nature Methods, vol. 18, pp. 701–709, 01 2021. [Online]. Available:
  • [55] Y. Cao, J. Romero, and A. Aspuru-Guzik, “Potential of quantum computing for drug discovery,” IBM Journal of Research and Development, vol. 62, no. 6, pp. 6:1–6:20, 2018. [Online]. Available:
  • [56] P. Alberto, M. Jarrod, S. Peter, Y. Man-Hong, Z. Xiao-Qi, L. Peter J., A.-G. Alán, and O. Jeremy L., “A variational eigenvalue solver on a photonic quantum processor,” Nature Communications, vol. 5, no. 4213, 07 2014. [Online]. Available:
  • [57] F. Dmitry A., P. Bo, G. Niranjan, and A. Yuri, “VQE method: a short survey and recent developments,” Matherials Theory, vol. 6, no. 2, 01 2022. [Online]. Available:
  • [58] Y. Song, Y.-D. Zhang, X. Yan, H. Liu, M. Zhou, B. Hu, and G. Yang, “Computer-aided diagnosis of prostate cancer using a deep convolutional neural network from multiparametric MRI,” J. Magn. Reson. Imaging, vol. 48, no. 6, 12 2018. [Online]. Available:
  • [59] C. Outeiral, M. Strahm, J. Shi, G. Morris, S. Benjamin, and C. Deane, “The prospects of quantum computing in computational molecular biology,” Wiley Interdisciplinary Reviews: Computational Molecular Science, vol. 11, 05 2020. [Online]. Available:
  • [60] A. A. Abdullah, M. M. Hassan, and Y. T. Mustafa, “A review on Bayesian deep learning in healthcare: Applications and challenges,” IEEE Access, vol. 10, pp. 36 538–36 562, 2022.
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Identyfikator YADDA
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.