{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T10:06:54Z","timestamp":1784196414602,"version":"3.55.0"},"reference-count":66,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T00:00:00Z","timestamp":1784160000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T00:00:00Z","timestamp":1784160000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100000855","name":"University of Birmingham","doi-asserted-by":"publisher","award":["https:\/\/intranet.birmingham.ac.uk\/as\/libraryservices\/library\/research\/open-access\/publisher-agreements\/springer-nature.aspx"],"award-info":[{"award-number":["https:\/\/intranet.birmingham.ac.uk\/as\/libraryservices\/library\/research\/open-access\/publisher-agreements\/springer-nature.aspx"]}],"id":[{"id":"10.13039\/501100000855","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Quantum Mach. Intell."],"published-print":{"date-parts":[[2026,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Financial crimes\u2019 fast proliferation and sophistication require novel approaches that provide robust and effective solutions. This paper explores the potential of quantum algorithms in combating financial crimes. It highlights the advantages of quantum computing by examining traditional and Machine Learning (ML) techniques alongside quantum approaches. The study showcases advanced methodologies such as Quantum Machine Learning (QML) and Quantum Artificial Intelligence (QAI) as\n                    <jats:italic>potential<\/jats:italic>\n                    solutions for detecting and preventing financial crimes, including money laundering, financial crime detection, cryptocurrency attacks, and market manipulation. These quantum approaches may leverage the inherent computational capabilities of quantum computers to overcome limitations faced by classical methods, contingent on continued advances in hardware and error correction. Furthermore, the paper illustrates how quantum computing could support enhanced financial risk management analysis. Financial institutions may improve their ability to identify and mitigate risks through quantum technologies as the field matures. The paper\u2019s primary contribution is a structured three-layer mapping framework connecting financial crime typologies with classical, machine learning, and quantum countermeasures, accompanied by a feasibility assessment of key quantum algorithms. All capability claims are grounded in an explicit discussion of current NISQ-era limitations and a phased roadmap for future experimental validation.\n                  <\/jats:p>","DOI":"10.1007\/s42484-026-00418-0","type":"journal-article","created":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T09:28:39Z","timestamp":1784194119000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Quantum algorithms: a new frontier in financial crime prevention"],"prefix":"10.1007","volume":"8","author":[{"given":"Abraham Itzhak","family":"Weinberg","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alessio","family":"Faccia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,16]]},"reference":[{"issue":"5","key":"418_CR1","doi-asserted-by":"publisher","first-page":"687","DOI":"10.1007\/s11227-025-07047-7","volume":"81","author":"M AbuGhanem","year":"2025","unstructured":"AbuGhanem M (2025) Ibm quantum computers: Evolution, performance, and future directions. J Supercomput 81(5):687","journal-title":"J Supercomput"},{"key":"418_CR2","doi-asserted-by":"crossref","unstructured":"A\u00efmeur E, Brassard G, Gambs S (2007) Quantum clustering algorithms. In: Proceedings of the 24th international conference on machine learning, pp 1\u20138","DOI":"10.1145\/1273496.1273497"},{"issue":"3","key":"418_CR3","doi-asserted-by":"publisher","first-page":"035003","DOI":"10.1088\/2632-2153\/ab9009","volume":"1","author":"J Alcazar","year":"2020","unstructured":"Alcazar J, Leyton-Ortega V, Perdomo-Ortiz A (2020) Classical versus quantum models in machine learning: insights from a finance application. Mach Learn Sci Technol 1(3):035003","journal-title":"Mach Learn Sci Technol"},{"key":"418_CR4","doi-asserted-by":"crossref","unstructured":"Alexeev Y, Farag MH, Patti TL, Wolf ME, Ares N, Aspuru-Guzik A, Benjamin SC, Cai Z, Chandani Z, Fedele F (2024) Artificial intelligence for quantum computing. arXiv:2411.09131 arXiv preprint","DOI":"10.1038\/s41467-025-65836-3"},{"key":"418_CR5","doi-asserted-by":"crossref","unstructured":"Anantraj I, Umarani B, Karpagavalli C, Usharani C, Lakshmi SJ (2023) Quantum computing\u2019s double-edged sword unravelling the vulnerabilities in quantum key distribution for enhanced network security. In: 2023 International conference on next generation electronics (NEleX). IEEE, pp 1\u20135","DOI":"10.1109\/NEleX59773.2023.10420896"},{"key":"418_CR6","doi-asserted-by":"crossref","unstructured":"Arslan B, Ulker M, Akleylek S, Sagiroglu S (2018) A study on the use of quantum computers, risk assessment and security problems. In: 2018 6th International Symposium on Digital Forensic and Security (ISDFS). IEEE, pp 1\u20136","DOI":"10.1109\/ISDFS.2018.8355318"},{"issue":"2","key":"418_CR7","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1109\/TE.2022.3144943","volume":"65","author":"A Asfaw","year":"2022","unstructured":"Asfaw A, Blais A, Brown KR, Candelaria J, Cantwell C, Carr LD, Combes J, Debroy DM, Donohue JM, Economou SE et al (2022) Building a quantum engineering undergraduate program. IEEE Trans Educ 65(2):220\u2013242","journal-title":"IEEE Trans Educ"},{"key":"418_CR8","unstructured":"Bergholm V, Izaac J, Schuld M, Gogolin C, Ahmed S, Ajith V, Alam MS, Alonso-Linaje G, AkashNarayanan B, Asadi A et al (2018) Pennylane: Automatic differentiation of hybrid quantum-classical computations. arXiv preprint arXiv:1811.04968"},{"issue":"1","key":"418_CR9","doi-asserted-by":"publisher","first-page":"015004","DOI":"10.1103\/RevModPhys.94.015004","volume":"94","author":"K Bharti","year":"2022","unstructured":"Bharti K, Cervera-Lierta A, Kyaw TH, Haug T, Alperin-Lea S, Anand A, Degroote M, Heimonen H, Kottmann JS, Menke T et al (2022) Noisy intermediate-scale quantum algorithms. Rev Mod Phys 94(1):015004","journal-title":"Rev Mod Phys"},{"issue":"7671","key":"418_CR10","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1038\/nature23460","volume":"549","author":"ET Campbell","year":"2017","unstructured":"Campbell ET, Terhal BM, Vuillot C (2017) Roads towards fault-tolerant universal quantum computation. Nature 549(7671):172\u2013179","journal-title":"Nature"},{"issue":"1","key":"418_CR11","doi-asserted-by":"publisher","first-page":"2312121","DOI":"10.1080\/09540091.2024.2312121","volume":"36","author":"L Chen","year":"2024","unstructured":"Chen L, Li T, Chen Y, Chen X, Wozniak M, Xiong N, Liang W (2024) Design and analysis of quantum machine learning: a survey. Connect Sci 36(1):2312121","journal-title":"Connect Sci"},{"key":"418_CR12","unstructured":"Childs AM, Eisenberg JM (2003) Quantum algorithms for subset finding. quant-ph\/0311038 arXiv preprint"},{"issue":"7","key":"418_CR13","doi-asserted-by":"publisher","first-page":"073011","DOI":"10.1088\/1367-2630\/18\/7\/073011","volume":"18","author":"I Cong","year":"2016","unstructured":"Cong I, Duan L (2016) Quantum discriminant analysis for dimensionality reduction and classification. New J Phys 18(7):073011","journal-title":"New J Phys"},{"issue":"2","key":"418_CR14","doi-asserted-by":"publisher","first-page":"024013","DOI":"10.1088\/2058-9565\/abd3db","volume":"6","author":"B Coyle","year":"2021","unstructured":"Coyle B, Henderson M, Le JCJ, Kumar N, Paini M, Kashefi E (2021) Quantum versus classical generative modelling in finance. Quant Sci Technol 6(2):024013","journal-title":"Quant Sci Technol"},{"issue":"7920","key":"418_CR15","doi-asserted-by":"publisher","first-page":"667","DOI":"10.1038\/s41586-022-04940-6","volume":"607","author":"AJ Daley","year":"2022","unstructured":"Daley AJ, Bloch I, Kokail C, Flannigan S, Pearson N, Troyer M, Zoller P (2022) Practical quantum advantage in quantum simulation. Nature 607(7920):667\u2013676","journal-title":"Nature"},{"key":"418_CR16","unstructured":"Dalzell AM, McArdle S, Berta M, Bienias P, Chen C-F, Gily\u00e9n A, Hann CT, Kastoryano MJ, Khabiboulline ET, Kubica A et al (2023) Quantum algorithms: A survey of applications and end-to-end complexities. arXiv preprint arXiv:2310.03011"},{"issue":"1","key":"418_CR17","first-page":"9","volume":"2","author":"G De Luca","year":"2022","unstructured":"De Luca G (2022) A survey of nisq era hybrid quantum-classical machine learning research. J Artif Intell Technol 2(1):9\u201315","journal-title":"J Artif Intell Technol"},{"issue":"7","key":"418_CR18","doi-asserted-by":"publisher","first-page":"074001","DOI":"10.1088\/1361-6633\/aab406","volume":"81","author":"V Dunjko","year":"2018","unstructured":"Dunjko V, Briegel HJ (2018) Machine learning & artificial intelligence in the quantum domain: a review of recent progress. Rep Prog Phys 81(7):074001","journal-title":"Rep Prog Phys"},{"key":"418_CR19","doi-asserted-by":"crossref","unstructured":"Egan L, Debroy DM, Noel C, Risinger A, Zhu D, Biswas D, Newman M, Li M, Brown KR, Cetina M (2020) Fault-tolerant operation of a quantum error-correction code. arXiv:2009.11482 arXiv preprint","DOI":"10.1038\/s41586-021-03928-y"},{"key":"418_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TQE.2020.3030314","volume":"1","author":"DJ Egger","year":"2020","unstructured":"Egger DJ, Gambella C, Marecek J, McFaddin S, Mevissen M, Raymond R, Simonetto A, Woerner S, Yndurain E (2020) Quantum computing for finance: State-of-the-art and future prospects. IEEE Trans Quant Eng 1:1\u201324","journal-title":"IEEE Trans Quant Eng"},{"issue":"2","key":"418_CR21","doi-asserted-by":"publisher","first-page":"61","DOI":"10.18034\/ei.v9i2.549","volume":"9","author":"A Ganapathy","year":"2021","unstructured":"Ganapathy A (2021) Quantum computing in high frequency trading and fraud detection. Eng Intern 9(2):61\u201372","journal-title":"Eng Intern"},{"key":"418_CR22","unstructured":"Ganguly S (2023) Implementing quantum generative adversarial network (qgan) and qcbm in finance. arXiv:2308.08448 arXiv preprint"},{"key":"418_CR23","unstructured":"Gil D (2023) Institute for business value. The Quantum Decade: A Playbook for Achieving Awareness, Readiness, and Advantage, 4th edn. IBM. https:\/\/www.ibm.com\/thought-leadership\/institute-business-value\/en-us\/report\/quantum-decade. Accessed 22 Mar 2024"},{"key":"418_CR24","doi-asserted-by":"publisher","first-page":"100514","DOI":"10.1016\/j.iot.2022.100514","volume":"19","author":"SS Gill","year":"2022","unstructured":"Gill SS, Xu M, Ottaviani C, Patros P, Bahsoon R, Shaghaghi A, Golec M, Stankovski V, Wu H, Abraham A et al (2022) Ai for next generation computing: Emerging trends and future directions. Intern Things 19:100514","journal-title":"Intern Things"},{"key":"418_CR25","doi-asserted-by":"crossref","unstructured":"Girasa R, Scalabrini GJ (2022) Regulation of innovative technologies: Blockchain, artificial intelligence and quantum computing. Springer","DOI":"10.1007\/978-3-031-03869-3"},{"key":"418_CR26","doi-asserted-by":"crossref","unstructured":"Gomes J, Khan S, Svetinovic D (2023) Fortifying the blockchain: A systematic review and classification of post-quantum consensus solutions for enhanced security and resilience. IEEE Access","DOI":"10.1109\/ACCESS.2023.3296559"},{"key":"418_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TQE.2022.3213474","volume":"3","author":"M Grossi","year":"2022","unstructured":"Grossi M, Ibrahim N, Radescu V, Loredo R, Voigt K, Von Altrock C, Rudnik A (2022) Mixed quantum-classical method for fraud detection with quantum feature selection. IEEE Trans Quant Eng 3:1\u201312","journal-title":"IEEE Trans Quant Eng"},{"issue":"11","key":"418_CR28","doi-asserted-by":"publisher","first-page":"5651","DOI":"10.3390\/app12115651","volume":"12","author":"R Guarasci","year":"2022","unstructured":"Guarasci R, De Pietro G, Esposito M (2022) Quantum natural language processing: Challenges and opportunities. Appl Sci 12(11):5651","journal-title":"Appl Sci"},{"key":"418_CR29","doi-asserted-by":"publisher","first-page":"127936","DOI":"10.1016\/j.physa.2022.127936","volume":"604","author":"M Guo","year":"2022","unstructured":"Guo M, Liu H, Li Y, Li W, Gao F, Qin S, Wen Q (2022) Quantum algorithms for anomaly detection using amplitude estimation. XXPhys A 604:127936","journal-title":"XXPhys A"},{"issue":"7747","key":"418_CR30","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1038\/s41586-019-0980-2","volume":"567","author":"V Havl\u00ed\u010dek","year":"2019","unstructured":"Havl\u00ed\u010dek V, C\u00f3rcoles AD, Temme K, Harrow AW, Kandala A, Chow JM, Gambetta JM (2019) Supervised learning with quantum-enhanced feature spaces. Nature 567(7747):209\u2013212","journal-title":"Nature"},{"issue":"4","key":"418_CR31","doi-asserted-by":"publisher","first-page":"585","DOI":"10.3390\/businesses3040036","volume":"3","author":"M-L How","year":"2023","unstructured":"How M-L, Cheah S-M (2023) Business renaissance: Opportunities and challenges at the dawn of the quantum computing era. Businesses 3(4):585\u2013605","journal-title":"Businesses"},{"issue":"06","key":"418_CR32","doi-asserted-by":"publisher","first-page":"40","DOI":"10.37547\/tajpslc\/Volume07Issue06-08","volume":"7","author":"K Ilova\u010da","year":"2025","unstructured":"Ilova\u010da K (2025) Implementation of dual-use technologies in defense and public security. Am J Politic Sci Law Criminol 7(06):40\u201348","journal-title":"Am J Politic Sci Law Criminol"},{"issue":"02","key":"418_CR33","doi-asserted-by":"publisher","first-page":"2350044","DOI":"10.1142\/S0219749923500442","volume":"22","author":"N Innan","year":"2024","unstructured":"Innan N, Khan MA-Z, Bennai M (2024) Financial fraud detection: a comparative study of quantum machine learning models. Intern J Quant Inf 22(02):2350044","journal-title":"Intern J Quant Inf"},{"key":"418_CR34","doi-asserted-by":"crossref","unstructured":"Innan N, Khan MA, Bennai -Z, M (2023) Financial fraud detection: a comparative study of quantum machine learning models. Intern J Quant Inf 2350044","DOI":"10.1142\/S0219749923500442"},{"key":"418_CR35","unstructured":"Jacquier A, Kondratyev O (2024) Quantum machine learning and optimisation in finance: Drive financial innovation with quantum-powered algorithms and optimisation strategies. Packt Publishing Ltd"},{"key":"418_CR36","doi-asserted-by":"publisher","first-page":"100419","DOI":"10.1016\/j.cosrev.2021.100419","volume":"41","author":"K Kadian","year":"2021","unstructured":"Kadian K, Garhwal S, Kumar A (2021) Quantum walk and its application domains: A systematic review. Comput Sci Rev 41:100419","journal-title":"Comput Sci Rev"},{"key":"418_CR37","doi-asserted-by":"crossref","unstructured":"Kalra A, Qureshi F, Tisi M (2018) Portfolio asset identification using graph algorithms on a quantum annealer. Available at SSRN 3333537","DOI":"10.2139\/ssrn.3333537"},{"key":"418_CR38","unstructured":"Kerenidis I, Prakash A (2016) Quantum recommendation systems. arXiv:1603.08675 arXiv preprint"},{"issue":"4","key":"418_CR39","doi-asserted-by":"publisher","first-page":"044007","DOI":"10.1088\/2058-9565\/abae7d","volume":"5","author":"F Leymann","year":"2020","unstructured":"Leymann F, Barzen J (2020) The bitter truth about gate-based quantum algorithms in the nisq era. Quant Sci Technol 5(4):044007","journal-title":"Quant Sci Technol"},{"issue":"6","key":"418_CR40","doi-asserted-by":"publisher","first-page":"062616","DOI":"10.1103\/PhysRevA.110.062616","volume":"110","author":"A Majumder","year":"2024","unstructured":"Majumder A, Krumm M, Radkohl T, Fiderer LJ, Nautrup HP, Jerbi S, Briegel HJ (2024) Variational measurement-based quantum computation for generative modeling. Phys Rev A 110(6):062616","journal-title":"Phys Rev A"},{"issue":"1","key":"418_CR41","doi-asserted-by":"publisher","first-page":"10002","DOI":"10.1209\/0295-5075\/134\/10002","volume":"134","author":"S Mangini","year":"2021","unstructured":"Mangini S, Tacchino F, Gerace D, Bajoni D, Macchiavello C (2021) Quantum computing models for artificial neural networks. Europhys Lett 134(1):10002","journal-title":"Europhys Lett"},{"key":"418_CR42","unstructured":"Meyer N, Ufrecht C, Periyasamy M, Scherer DD, Plinge A, Mutschler C (2022) A survey on quantum reinforcement learning. arXiv:2211.03464 arXiv preprint"},{"key":"418_CR43","doi-asserted-by":"crossref","unstructured":"Mishra SR, Mohapatra H (2024) Enhancing money laundering detection through machine learning: A comparative study of algorithms and feature selection techniques. IGI Global","DOI":"10.4018\/979-8-3693-0659-8.ch012"},{"key":"418_CR44","unstructured":"Naik A, Yeniaras E, Hellstern G, Prasad G, Vishwakarma SKLP (2023) From portfolio optimization to quantum blockchain and security: A systematic review of quantum computing in finance. arXiv:2307.01155 arXiv preprint"},{"key":"418_CR45","doi-asserted-by":"crossref","unstructured":"Nicholls J, Kuppa A, Le-Khac N-A (2023) Fraudlens: Graph structural learning for bitcoin illicit activity identification. In: Proceedings of the 39th annual computer security applications conference, pp 324\u2013336","DOI":"10.1145\/3627106.3627200"},{"issue":"1","key":"418_CR46","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1038\/s41534-019-0141-3","volume":"5","author":"MY Niu","year":"2019","unstructured":"Niu MY, Boixo S, Smelyanskiy VN, Neven H (2019) Universal quantum control through deep reinforcement learning. NPJ Quant Inf 5(1):33","journal-title":"NPJ Quant Inf"},{"key":"418_CR47","doi-asserted-by":"crossref","unstructured":"Njorbuenwu M, Swar B, Zavarsky P (2019) A survey on the impacts of quantum computers on information security. In: 2019 2nd International Conference on Data Intelligence and Security (ICDIS). IEEE, pp 212\u2013218","DOI":"10.1109\/ICDIS.2019.00039"},{"key":"418_CR48","doi-asserted-by":"crossref","unstructured":"Nos\u00e1l J (2023) Crime in the digital age: A new frontier. Springer","DOI":"10.1007\/978-3-031-24673-9_11"},{"issue":"7","key":"418_CR49","first-page":"6","volume":"18","author":"AE Omolara","year":"2018","unstructured":"Omolara AE, Jantan A, Abiodun OI, Singh MM, Anbar M, Kemi D (2018) State-of-the-art in big data application techniques to financial crime: a survey. Intern J Comput Sci Netw Secur 18(7):6\u201316","journal-title":"Intern J Comput Sci Netw Secur"},{"key":"418_CR50","doi-asserted-by":"crossref","unstructured":"Patil HP, Li P, Liu J, Zhou H (20230 Folding-free zne: A comprehensive quantum zero-noise extrapolation approach for mitigating depolarizing and decoherence noise. In: 2023 IEEE International conference on Quantum Computing and Engineering (QCE), vol 1. IEEE, pp 898\u2013909","DOI":"10.1109\/QCE57702.2023.00104"},{"key":"418_CR51","doi-asserted-by":"crossref","unstructured":"Peelam MS, Rout AA, Chamola V (2023) Quantum computing applications for internet of things. IET Quant Commun","DOI":"10.1049\/qtc2.12079"},{"key":"418_CR52","doi-asserted-by":"crossref","unstructured":"Ramezani SB, Sommers A, Manchukonda HK, Rahimi S, Amirlatifi A (2020) Machine learning algorithms in quantum computing: A survey. In: 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, pp 1\u20138","DOI":"10.1109\/IJCNN48605.2020.9207714"},{"issue":"1","key":"418_CR53","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1140\/epjqt\/s40507-024-00285-3","volume":"11","author":"M Rath","year":"2024","unstructured":"Rath M, Date H (2024) Quantum data encoding: A comparative analysis of classical-to-quantum mapping techniques and their impact on machine learning accuracy. EPJ Quant Technol 11(1):72","journal-title":"EPJ Quant Technol"},{"key":"418_CR54","unstructured":"Ray SA (2024) Quantum machine learning with quantum cheshire cat generative AI model: Quantum mirage data. Compassionate AI Lab"},{"issue":"3","key":"418_CR55","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1080\/00107514.2019.1667078","volume":"60","author":"J Roffe","year":"2019","unstructured":"Roffe J (2019) Quantum error correction: an introductory guide. Contemp Phys 60(3):226\u2013245","journal-title":"Contemp Phys"},{"key":"418_CR56","unstructured":"Saxena A, Mancilla J, Montalban I, Pere C (2023) Financial Modeling Using Quantum Computing: Design and manage quantum machine learning solutions for financial analysis and decision making. Packt Publishing Ltd,"},{"issue":"12","key":"418_CR57","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10462-024-10973-2","volume":"57","author":"M Schmitt","year":"2024","unstructured":"Schmitt M, Flechais I (2024) Digital deception: Generative artificial intelligence in social engineering and phishing. Artif Intell Rev 57(12):1\u201323","journal-title":"Artif Intell Rev"},{"key":"418_CR58","doi-asserted-by":"crossref","unstructured":"Schuld M (2021) Supervised quantum machine learning models are kernel methods. arXiv:2101.11020 arXiv preprint","DOI":"10.1007\/978-3-030-83098-4_6"},{"key":"418_CR59","doi-asserted-by":"crossref","unstructured":"Stein J, Schuman D, Benkard M, Holger T, Sajko W, K\u00f6lle M, N\u00fc\u00dflein J, S\u00fcnkel L, Salomon O, Linnhoff-Popien C (2023) Exploring unsupervised anomaly detection with quantum boltzmann machines in fraud detection. arXiv:2306.04998 arXiv preprint","DOI":"10.5220\/0012326100003636"},{"key":"418_CR60","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.physrep.2022.08.003","volume":"986","author":"J Tilly","year":"2022","unstructured":"Tilly J, Chen H, Cao S, Picozzi D, Setia K, Li Y, Grant E, Wossnig L, Rungger I, Booth GH et al (2022) The variational quantum eigensolver: a review of methods and best practices. Phys Rep 986:1\u2013128","journal-title":"Phys Rep"},{"key":"418_CR61","unstructured":"Tomar S, Tripathi R, Kumar S (2025) Comprehensive survey of qml: From data analysis to algorithmic advancements. arXiv:2501.09528 arXiv preprint"},{"key":"418_CR62","unstructured":"Trivedi SR, Krishnakumar D, Bajaj RV (2021) Loan frauds and bad boy billionaires: A new approach of loan fraud prevention using natural language processing (nlp). NIBM Working Paper Series"},{"issue":"2","key":"418_CR63","first-page":"43","volume":"8","author":"L Wang","year":"2020","unstructured":"Wang L, Alexander CA (2020) Quantum science and quantum technology: Progress and challenges. Am J Electr Electron Eng 8(2):43\u201350","journal-title":"Am J Electr Electron Eng"},{"key":"418_CR64","doi-asserted-by":"crossref","unstructured":"Wang M, Das P, Nair PJ (2024) Qoncord: A multi-device job scheduling framework for variational quantum algorithms. In: 2024 57th IEEE\/ACM international symposium on microarchitecture (MICRO). IEEE, pp 735\u2013749","DOI":"10.1109\/MICRO61859.2024.00060"},{"issue":"2","key":"418_CR65","first-page":"020344","volume":"3","author":"GA White","year":"2022","unstructured":"White GA, Pollock FA, Hollenberg LC, Modi K, Hill CD (2022) Non-markovian quantum process tomography. PRX. Quantum 3(2):020344","journal-title":"Quantum"},{"issue":"1","key":"418_CR66","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1038\/s41534-019-0130-6","volume":"5","author":"S Woerner","year":"2019","unstructured":"Woerner S, Egger DJ (2019) Quantum risk analysis. NPJ Quant Inf 5(1):15","journal-title":"NPJ Quant Inf"}],"container-title":["Quantum Machine Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42484-026-00418-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42484-026-00418-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42484-026-00418-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T09:28:54Z","timestamp":1784194134000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42484-026-00418-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,16]]},"references-count":66,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,12]]}},"alternative-id":["418"],"URL":"https:\/\/doi.org\/10.1007\/s42484-026-00418-0","relation":{},"ISSN":["2524-4906","2524-4914"],"issn-type":[{"value":"2524-4906","type":"print"},{"value":"2524-4914","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,16]]},"assertion":[{"value":"28 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 July 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 July 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Not applicable.","order":1,"name":"Ethics","label":"Clinical trial number","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"78"}}