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Artificial intelligence and IoT based optical quantum computing application legal implications in privacy and regulatory analysis

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Abstract

Parallel developments in the realms of AI and quantum technology have shown a great deal of promise for mutual benefit. Combining them refers to using AI methods to create quantum computing (QC) and quantum physics algorithms as well as using QC to improve AI applications. QC has the power to revolutionise a number of industries. One of the biggest barriers preventing the broad application of QC is the famously challenging nature of controlling quantum systems. AI has made it possible to automate quantum system control in novel ways. In particular, the use of AI in conjunction with the Internet of Things (IoT) can potentially ease problems that have historically been connected to QC and quantum communication while revealing invaluable insight into the intricate and multifaceted field of quantum physics. However, QC may also be applied to improve AI applications. This study tries to examine how these technologies are used in the judicial system and how they affect courtroom behaviour. Accelerators are necessary for the quick and efficient execution of artificial intelligence activities across a variety of applications. Despite a half-century of study, general-purpose optical computing methods still haven’t developed into a workable technology. However, optical computing methods may be able to address certain domain-specific demands. The focus of the article is on how these technologies may increase the efficacy and efficiency of law enforcement and the legal system, rather than providing a thorough examination of the legal system.

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References

  • Badawi, A.: Engineering the optical properties of PVA/PVP polymeric blend in situ using tin sulfide for optoelectronics. Appl. Phys. A. 126(5), 335 (2020)

    Article  ADS  CAS  Google Scholar 

  • Caligiuri, L.M., Musha, T.: Accelerated quantum computation by means of evanescent photons and its prospects for optical quantum hypercomputers and artificial intelligence. In 2019 International Conference on Engineering, Science, and Industrial Applications (ICESI) (pp. 1–10). IEEE. (2019), August

  • Carvalho, R.P.: Organic Electrode Battery Materials: A Journey from Quantum Mechanics to Artificial Intelligence. Acta Universitatis Upsaliensis (2022). (Doctoral dissertation)

  • Cervera-Lierta, A., Krenn, M., Aspuru-Guzik, A.: Design of quantum optical experiments with logic artificial intelligence. Quantum. 6, 836 (2022)

    Article  Google Scholar 

  • Davids, J., Lidströmer, N., Ashrafian, H.: Artificial Intelligence in Medicine using Quantum Computing in the future of Healthcare. In: Artificial Intelligence in Medicine, pp. 423–446. Springer International Publishing, Cham (2022)

    Chapter  Google Scholar 

  • El Sayed, A.M., Saber, S.: Structural, optical analysis, and Poole–Frenkel emission in NiO/CMC–PVP: Bio-nanocomposites for optoelectronic applications. J. Phys. Chem. Solids. 163, 110590 (2022)

    Article  Google Scholar 

  • Falbo, E., Fusè, M., Lazzari, F., Mancini, G., Barone, V.: Integration of Quantum Chemistry, statistical mechanics, and Artificial Intelligence for computational spectroscopy: The UV–Vis spectrum of TEMPO radical in different solvents. J. Chem. Theory Comput. 18(10), 6203–6216 (2022)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Gentili, P.L.: Establishing a new link between fuzzy logic, neuroscience, and quantum mechanics through bayesian probability: Perspectives in artificial intelligence and unconventional computing. Molecules. 26(19), 5987 (2021)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Gong, Y., Zhang, Y.Z., Fang, S., Liu, C., Niu, J., Li, G., Lai, W.Y.: Artificial intelligent optoelectronic skin with anisotropic electrical and optical responses for multi-dimensional sensing. Appl. Phys. Reviews, 9(2). (2022)

  • Guo, P., Huang, K., Xu, Z.: Partial Differential Equations is All You Need for Generating Neural Architectures–A Theory for Physical Artificial Intelligence Systems. arXiv preprint arXiv:2103.08313. (2021)

  • Guo, Z., Li, J., Liang, J., Wang, C., Zhu, X., He, T.: Regulating optical activity and anisotropic second-harmonic generation in zero-dimensional hybrid copper halides. Nano Lett. 22(2), 846–852 (2022)

    Article  ADS  CAS  PubMed  Google Scholar 

  • Hadi, A.G., Al-Ramadhan, Z., Hashim, A.: Enhanced optical characteristics and low energy gap of srtio3 doped polymeric blend for optoelectronics devices. In Journal of Physics: Conference Series1963(1), 012004. IOP Publishing. (2021), July

  • Krenn, M., Landgraf, J., Foesel, T., Marquardt, F.: Artificial intelligence and machine learning for quantum technologies. Phys. Rev. A. 107(1), 010101 (2023)

    Article  ADS  CAS  Google Scholar 

  • Kulkarni, J.P., Krenn, M.: Prediction of future research trends in Optics using Semantic Analysis and Artificial Neural Networks. (2022)

  • Lazarev, V.S., Yurik, I.V., Lis, P.A., Kachan, D.A., Dydik, N.S.: Some methodological aspects of selection serials to be included in the information environment for researchers in a technical or natural science (by example of optoelectronics and optical systems). (2019)

  • Li, W., Ma, H., Li, S., Ma, J.: Computational and data driven molecular material design assisted by low scaling quantum mechanics calculations and machine learning. Chem. Sci. 12(45), 14987–15006 (2021)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Mezquita, Y., Alonso, R.S., Casado-Vara, R., Prieto, J., Corchado, J.M.: A review of k-nn algorithm based on classical and quantum machine learning. In Distributed Computing and Artificial Intelligence, Special Sessions, 17th International Conference (pp. 189–198). Springer International Publishing. (2021)

  • Rao, P.S., Yaqoob, S.I., Ahmed, M.A., Abdinabievna, P.S., Yaseen, S.M., Arumugam, M.: Integrated artificial intelligence and predictive maintenance of electric vehicle components with optical and quantum enhancements. Opt. Quant. Electron. 55(10), 1–19 (2023)

    Article  Google Scholar 

  • Rem, B.S., Käming, N., Tarnowski, M., Asteria, L., Fläschner, N., Becker, C., Weitenberg, C.: Identifying quantum phase transitions using artificial neural networks on experimental data. Nat. Phys. 15(9), 917–920 (2019)

    Article  CAS  Google Scholar 

  • Simine, L., Allen, T.C., Rossky, P.J.: Predicting optical spectra for optoelectronic polymers using coarse-grained models and recurrent neural networks. Proceedings of the National Academy of Sciences, 117(25), 13945–13948. (2020)

  • Zhu, Y., Yu, K.: Artificial intelligence (AI) for quantum and quantum for AI. Opt. Quant. Electron. 55(8), 697 (2023)

    Article  Google Scholar 

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Sha Dong1: Study Conception And Design, Data Collection, Hanjun Chen2*:Analysis And Interpretation Of Results,: Manuscript Preparation.

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Correspondence to Hanjun Chen.

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Dong, S., Chen, H. Artificial intelligence and IoT based optical quantum computing application legal implications in privacy and regulatory analysis. Opt Quant Electron 56, 556 (2024). https://doi.org/10.1007/s11082-023-06161-1

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