Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Kulshrestha, Nitin Title A name given to the resource Quantum AI in Finance: Predicting the Unpredictable Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Optimizing Business Operations With Quantum AI;pp.213-233 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.4018/979-8-3373-4347-1.ch008" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3373-4347-1.ch008</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105033838951?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105033838951?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kulshrestha N., Christ University, India Description An account of the resource Navigating modern global financial markets becomes increasingly intricate, which makes the traditional approaches inadequate in processing large volumes of data in real time. This chapter presents Quantum Artificial Intelligence (QAI) and its capabilities in predicting and mitigating unanticipated financial shocks. With the integration of quantum computings parallel processing capabilities and AIs adaptive learning, QAI makes it possible for institutions to model market nonlinearities, scope large- scale simulations, and detect faint signals that are commonly overlooked by classical systems. The scope of this chapter illustrates the impact of QAI in real- time risk evaluation, fraud identification, portfolio management, and macroeconomic prediction and modeling. Focus is given to quantum- boosted machine learning models that are designed to simulate black swan events and other high- dimensional datasets. 2026 by IGI Global Scientific Publishing. All rights reserved. Publisher An entity responsible for making the resource available IGI Global Relation A related resource ISBN: 979-833734349-5; 979-833734347-1; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online