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.

量子算法对金融风险管理具有转变性影响，通过利用量子计算机固有的计算能力，可以提供强大且有效的解决方案，包括金融犯罪检测、预防洗钱、加密货币攻击和市场操纵等方面的问题，并能支持加强的金融风险管理分析。

量子算法：金融犯罪预防的新领域

Quantum Algorithms: A New Frontier in Financial Crime Prevention

The industry of quantum technologies is rapidly expanding, offering promising
opportunities for various scientific domains. Among these emerging
technologies, Quantum Machine Learning (QML) has attracted considerable
attention due to its potential to revolutionize data processing and analysis.
In this paper, we investigate the application of QML in the field of remote
sensing. It is believed that QML can provide valuable insights for analysis of
data from space. We delve into the common beliefs surrounding the quantum
advantage in QML for remote sensing and highlight the open challenges that need
to be addressed. To shed light on the challenges, we conduct a study focused on
the problem of kernel value concentration, a phenomenon that adversely affects
the runtime of quantum computers. Our findings indicate that while this issue
negatively impacts quantum computer performance, it does not entirely negate
the potential quantum advantage in QML for remote sensing.

量子技术快速扩展，量子机器学习在遥感领域的应用引起了广泛关注，本文调查了量子机器学习在遥感领域的潜力，并探讨了其面临的挑战，通过研究核值集中问题发现，量子计算机的性能受到了一定的负面影响，但并不完全否定量子机器学习在遥感中的潜在优势。