Quantum computing for financial risk measurement

Wilkens, Sascha; Moorhouse, Joe

doi:10.1007/s11128-022-03777-2

Cited by 17 publications

(2 citation statements)

References 83 publications

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“…Quantum machine learning (QML) algorithms [88] are emerging as powerful tools for solving complex economic problems, harnessing the principles of quantum mechanics to potentially revolutionize how we approach these challenges. For instance: a) The variational quantum eigensolver (VQE) is one such algorithm, particularly useful in finding the ground state of economic Hamiltonians for optimization problems, such as portfolio allocation in finance.…”

Section: Quantum Machine Learning In Economicsmentioning

confidence: 99%

Deep learning for economic transformation: a parametric review

Tariq,

Ahmed,

Khan

et al. 2024

IJEECS

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Deep learning (DL) is increasingly recognized for its effectiveness in analyzing and forecasting complex economic systems, particularly in the context of Pakistan's evolving economy. This paper investigates DL's transformative role in managing and interpreting increasing volumes of intricate economic data, leading to more nuanced insights. DL models show a marked improvement in predictive accuracy and depth over traditional methods across various economic domains and policymaking scenarios. Applications include demand forecasting, risk evaluation, market trend analysis, and resource allocation optimization. These processes utilize extensive datasets and advanced algorithms to identify patterns that traditional methods cannot detect. Nonetheless, DL's broader application in economic research faces challenges like limited data availability, complexity of economic interactions, interpretability of model outputs, and significant computational power requirements. The paper outlines strategies to overcome these barriers, such as enhancing model interpretability, employing federated learning for better data privacy, and integrating behavioral and social economic theories. It concludes by stressing the importance of targeted research and ethical considerations in maximizing DL's impact on economic insights and innovation, particularly in Pakistan and globally.

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Section: Quantum Machine Learning In Economicsmentioning

confidence: 99%

Deep learning for economic transformation: a parametric review

Tariq,

Ahmed,

Khan

et al. 2024

IJEECS

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“…A study written by Wilkens S. and Moorhouse J. in 2023 investigates the feasibility of implementing real-world risk measurement applications on quantum computers. Their work contributes to a feasibility analysis of running realistic market risk and counterparty-credit risk applications on quantum devices [ 30 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Modern finance through quantum computing—A systematic literature review

Bunescu,

Vârtei

2024

PLoS ONE

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Human intellectual restlessness originates from the need for knowledge of the modern world. The financial world is struggling to prototype accurate and fast data at low risk. The quantum approach to finance can support this desire. The goal of this paper is to provide a comprehensive review of the literature on how quantum computing can be used in finance. This research aims to expose an architecture of the state of the art in quantum finance. In terms of methodology, the PSALSAR framework was used to conduct this systematic literature review. The selection procedure followed the PRISMA guidelines and was applied in two databases (Web of Science and Scopus) without time limit. In total, 94 out of 1646 articles were included for data extraction and assessment of content evaluation covering the period 2001–2023. The current review of quantum finance literature is structured around the following themes: journals, research methods, tested data series, research topics in quantum finance, and future research directions. Within the financial sector, quantum computing is used in three main areas: simulation, optimization, and machine learning. These areas are supported by algorithms that have been created in recent years. Finally, we propose to highlight the benefits and the applications of quantum finance and to stimulate the interest in the future prospects of the debates.

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Quantum‐Neural Network Model for Platform Independent Ddos Attack Classification in Cyber Security

Küçükkara,

Atban,

Bayılmış

2024

Adv Quantum Tech

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Quantum Machine Learning (QML) leverages the transformative power of quantum computing to explore a broad range of applications, including optimization, data analysis, and complex problem‐solving. Central to this study is the using of an innovative intrusion detection system leveraging QML models, with a preference for Quantum Neural Network (QNN) architectures for classification tasks. The inherent advantages of QNNs, notably their parallel processing capabilities facilitated by quantum computers and the exploitation of quantum superposition and parallelism, are elucidated. These attributes empower QNNs to execute certain classification tasks expediently and with heightened efficiency. Empirical validation is conducted through the deployment and testing of a QNN‐based intrusion detection system, employing a subset of the CIC‐DDoS 2019 dataset. Notably, despite employing a reduced feature set, the QNN‐based system exhibits remarkable classification accuracy, achieving a commendable rate of 92.63%. Moreover, the study advocates for the utilization of quantum computing libraries such as Qiskit, facilitating QNN training on local machines or quantum simulators. The findings underscore the efficacy of a QNN‐based intrusion detection system in attaining superior classification accuracy when confronted with large‐scale training datasets. However, it is imperative to acknowledge the constraints imposed by the limited number of qubits available on local machines and simulators.

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Quantum computing for financial risk measurement

Cited by 17 publications

References 83 publications

Deep learning for economic transformation: a parametric review

Deep learning for economic transformation: a parametric review

Modern finance through quantum computing—A systematic literature review

Quantum‐Neural Network Model for Platform Independent Ddos Attack Classification in Cyber Security

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