Cite this DOI

Abhishek Murikipudi (2025). Quantum-stimulated AI for Continuous Credit Risk Categorization in High-Frequency Trading. *Journal of Science & Technology*, *10*(5), 22–28. https://doi.org/10.46243/jst.2025.v10.i05.pp22-28

@article{abhishekmurikipudi2025quantumstimulated,
  author    = {Abhishek Murikipudi},
  title     = {{Quantum-stimulated AI for Continuous Credit Risk Categorization in High-Frequency Trading}},
  journal   = {Journal of Science \& Technology},
  year      = {2025},
  month     = {may},
  volume    = {10},
  number    = {5},
  pages     = {22--28},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jst.2025.v10.i05.pp22-28},
  url       = {https://doi.org/10.46243/jst.2025.v10.i05.pp22-28},
  language  = {en},
  abstract  = {This research focuses on the use of quantum-stimulated artificial intelligence approaches for enhancing the credit riskclassification in high-frequency trading systems. This paper considers the several obstacles, the speeds, the accuracy,and scopes of the improvement of credit risk evaluation by quantum algorithms. The study represents fresh approachesintroduced in trading structures regarding the application of quantum-inspired AI and offers guidelines based on thebest practices of efficient, fast, and flexible credit risk handling in fluctuating markets.}
}

TY  - JOUR
TI  - Quantum-stimulated AI for Continuous Credit Risk Categorization in High-Frequency Trading
AU  - Abhishek Murikipudi
JO  - Journal of Science & Technology
PY  - 2025
DA  - 2025/05/16/
VL  - 10
IS  - 5
SP  - 22
EP  - 28
PB  - Longman Publishers
SN  - 2456-5660
LA  - en
AB  - This research focuses on the use of quantum-stimulated artificial intelligence approaches for enhancing the credit riskclassification in high-frequency trading systems. This paper considers the several obstacles, the speeds, the accuracy,and scopes of the improvement of credit risk evaluation by quantum algorithms. The study represents fresh approachesintroduced in trading structures regarding the application of quantum-inspired AI and offers guidelines based on thebest practices of efficient, fast, and flexible credit risk handling in fluctuating markets.
DO  - 10.46243/jst.2025.v10.i05.pp22-28
UR  - https://doi.org/10.46243/jst.2025.v10.i05.pp22-28
ER  -

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    "DOI": "10.46243/jst.2025.v10.i05.pp22-28",
    "URL": "https://doi.org/10.46243/jst.2025.v10.i05.pp22-28",
    "title": "Quantum-stimulated AI for Continuous Credit Risk Categorization in High-Frequency Trading",
    "source": "Smart Scholars DOI Registry",
    "container-title": "Journal of Science & Technology",
    "author": [
        {
            "family": "Abhishek Murikipudi",
            "ORCID": "https://orcid.org/0009-0006-6296-5598"
        }
    ],
    "issued": {
        "date-parts": [
            [
                2025,
                5,
                16
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    "volume": "10",
    "issue": "5",
    "page": "22-28",
    "publisher": "Longman Publishers",
    "language": "en",
    "abstract": "This research focuses on the use of quantum-stimulated artificial intelligence approaches for enhancing the credit riskclassification in high-frequency trading systems. This paper considers the several obstacles, the speeds, the accuracy,and scopes of the improvement of credit risk evaluation by quantum algorithms. The study represents fresh approachesintroduced in trading structures regarding the application of quantum-inspired AI and offers guidelines based on thebest practices of efficient, fast, and flexible credit risk handling in fluctuating markets.",
    "ISSN": "2456-5660"
}

From the record as registered (version 2) — the record and its history. Programs: https://registry.smartscholars.in/api.php?action=cite&doi=10.46243%2Fjst.2025.v10.i05.pp22-28 gives all four in one JSON answer.