Quantum Algorithms: A New Frontier in Financial Crime Prevention
- URL: http://arxiv.org/abs/2403.18322v1
- Date: Wed, 27 Mar 2024 07:52:10 GMT
- Title: Quantum Algorithms: A New Frontier in Financial Crime Prevention
- Authors: Abraham Itzhak Weinberg, Alessio Faccia,
- Abstract summary: The study showcases advanced methodologies such as Quantum Machine Learning (QML) and Quantum Artificial Intelligence (QAI)
These quantum approaches leverage the inherent computational capabilities of quantum computers to overcome limitations faced by classical methods.
Financial institutions can improve their ability to identify and mitigate risks, leading to more robust risk management strategies.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Financial crimes 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 powerful solutions for detecting and preventing financial crimes, including money laundering, financial crime detection, cryptocurrency attacks, and market manipulation. These quantum approaches leverage the inherent computational capabilities of quantum computers to overcome limitations faced by classical methods. Furthermore, the paper illustrates how quantum computing can support enhanced financial risk management analysis. Financial institutions can improve their ability to identify and mitigate risks, leading to more robust risk management strategies by exploiting the quantum advantage. This research underscores the transformative impact of quantum algorithms on financial risk management. By embracing quantum technologies, organisations can enhance their capabilities to combat evolving threats and ensure the integrity and stability of financial systems.
Related papers
- QML-IDS: Quantum Machine Learning Intrusion Detection System [1.2016264781280588]
We present QML-IDS, a novel Intrusion Detection System that combines quantum and classical computing techniques.
QML-IDS employs Quantum Machine Learning(QML) methodologies to analyze network patterns and detect attack activities.
We show that QML-IDS is effective at attack detection and performs well in binary and multiclass classification tasks.
arXiv Detail & Related papers (2024-10-07T13:07:41Z) - A Brief Review of Quantum Machine Learning for Financial Services [0.0]
This review paper examines state-of-the-art algorithms and techniques in quantum machine learning with potential applications in finance.
The financial applications considered include risk management, credit scoring, fraud detection, and stock price prediction.
arXiv Detail & Related papers (2024-07-17T14:44:47Z) - Assessing the Benefits and Risks of Quantum Computers [0.7224497621488283]
We review what is currently known on the potential uses and risks of quantum computers.
We identify 2 large-scale trends -- new approximate methods and the commercial exploration of business-relevant quantum applications.
We conclude there is a credible expectation that quantum computers will be capable of performing computations which are economically-impactful.
arXiv Detail & Related papers (2024-01-29T17:21:31Z) - Generative AI-enabled Quantum Computing Networks and Intelligent
Resource Allocation [80.78352800340032]
Quantum computing networks execute large-scale generative AI computation tasks and advanced quantum algorithms.
efficient resource allocation in quantum computing networks is a critical challenge due to qubit variability and network complexity.
We introduce state-of-the-art reinforcement learning (RL) algorithms, from generative learning to quantum machine learning for optimal quantum resource allocation.
arXiv Detail & Related papers (2024-01-13T17:16:38Z) - Quantum Multiple Kernel Learning in Financial Classification Tasks [2.8564636890651607]
We propose a hybrid, quantum multiple kernel learning (QMKL) methodology that can improve classification quality over a single kernel approach.
We show QMKL on quantum hardware using an error mitigation pipeline and show the benefits of QMKL in the large qubit regime.
arXiv Detail & Related papers (2023-12-01T00:18:43Z) - Quantum Machine Learning: from physics to software engineering [58.720142291102135]
We show how classical machine learning approach can help improve the facilities of quantum computers.
We discuss how quantum algorithms and quantum computers may be useful for solving classical machine learning tasks.
arXiv Detail & Related papers (2023-01-04T23:37:45Z) - Optimal Stochastic Resource Allocation for Distributed Quantum Computing [50.809738453571015]
We propose a resource allocation scheme for distributed quantum computing (DQC) based on programming to minimize the total deployment cost for quantum resources.
The evaluation demonstrates the effectiveness and ability of the proposed scheme to balance the utilization of quantum computers and on-demand quantum computers.
arXiv Detail & Related papers (2022-09-16T02:37:32Z) - Circuit Symmetry Verification Mitigates Quantum-Domain Impairments [69.33243249411113]
We propose circuit-oriented symmetry verification that are capable of verifying the commutativity of quantum circuits without the knowledge of the quantum state.
In particular, we propose the Fourier-temporal stabilizer (STS) technique, which generalizes the conventional quantum-domain formalism to circuit-oriented stabilizers.
arXiv Detail & Related papers (2021-12-27T21:15:35Z) - On exploring the potential of quantum auto-encoder for learning quantum systems [60.909817434753315]
We devise three effective QAE-based learning protocols to address three classically computational hard learning problems.
Our work sheds new light on developing advanced quantum learning algorithms to accomplish hard quantum physics and quantum information processing tasks.
arXiv Detail & Related papers (2021-06-29T14:01:40Z) - Quantum Computing for Finance: State of the Art and Future Prospects [8.77758485723332]
This article outlines our point of view regarding the applicability, state-of-the-art, and potential of quantum computing for problems in finance.
We describe in detail quantum algorithms for specific applications arising in financial services, such as those involving simulation, optimization, and machine learning problems.
In addition, we include demonstrations of quantum algorithms on IBM Quantum back-ends and discuss the potential benefits of quantum algorithms for problems in financial services.
arXiv Detail & Related papers (2020-06-25T16:02:05Z) - An Application of Quantum Annealing Computing to Seismic Inversion [55.41644538483948]
We apply a quantum algorithm to a D-Wave quantum annealer to solve a small scale seismic inversions problem.
The accuracy achieved by the quantum computer is at least as good as that of the classical computer.
arXiv Detail & Related papers (2020-05-06T14:18:44Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.