Application of deep quantum neural networks to finance

Sakuma, Takayuki

doi:10.48550/arxiv.2011.07319

Cited by 4 publications

(4 citation statements)

References 9 publications

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“…The corresponding quantum implementation [60,93] is therefore an important component. Furthermore, Quantum Machine Learning (QML) has received considerable attention across the full range of techniques such as supervised and unsupervised learning as well as generative modelling tasks [7,8,71,77]. Specifically for financial applications, methods for market scenario generation, such as the joint evolution of dependent risk factors [53], are a promising route [3,23,52,97].…”

Section: Research Landscape In Quantitative Financementioning

confidence: 99%

Quantum computing for financial risk measurement

Wilkens¹,

Moorhouse

2023

Quantum Inf Process

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Quantum computing allows a significant speed-up over traditional CPU- and GPU-based algorithms when applied to particular mathematical challenges such as optimisation and simulation. Despite promising advances and extensive research in hard- and software developments, currently available quantum systems are still largely limited in their capability. In line with this, practical applications in quantitative finance are still in their infancy. This paper analyses requirements and concrete approaches for the application to risk management in a financial institution. On the examples of Value-at-Risk for market risk and Potential Future Exposure for counterparty credit risk, the main contribution lies in going beyond textbook illustrations and instead exploring must-have model features and their quantum implementations. While conceptual solutions and small-scale circuits are feasible at this stage, the leap needed for real-life applications is still significant. In order to build a usable risk measurement system, the hardware capacity—measured in number of qubits—would need to increase by several magnitudes from their current value of about $$10^2$$ 10 2 . Quantum noise poses an additional challenge, and research into its control and mitigation would need to advance in order to render risk measurement applications deployable in practice. Overall, given the maturity of established classical simulation-based approaches that allow risk computations in reasonable time and with sufficient accuracy, the business case for a move to quantum solutions is not very strong at this point.

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Section: Research Landscape In Quantitative Financementioning

confidence: 99%

Quantum computing for financial risk measurement

Wilkens¹,

Moorhouse

2023

Quantum Inf Process

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“…It is one of the deciding factors in the pricing of options. Sakuma [291] investigated the use of the deep quantum neural network proposed by Beer et al [35] in the context of learning implied volatility. Numerical results suggest that such a QML model is a promising candidate for developing powerful methods in finance.…”

Section: Implied Volatilitymentioning

confidence: 99%

A Survey of Quantum Computing for Finance

Herman¹,

Googin²,

Liu³

et al. 2022

Preprint

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Quantum computers are expected to surpass the computational capabilities of classical computers during this decade and have transformative impact on numerous industry sectors, particularly finance. In fact, finance is estimated to be the first industry sector to benefit from quantum computing, not only in the medium and long terms, but even in the short term. This survey paper presents a comprehensive summary of the state of the art of quantum computing for financial applications, with particular emphasis on Monte Carlo integration, optimization, and machine learning, showing how these solutions, adapted to work on a quantum computer, can help solve more efficiently and accurately problems such as derivative pricing, risk analysis, portfolio optimization, natural language processing, and fraud detection. We also discuss the feasibility of these algorithms on near-term quantum computers with various hardware implementations and demonstrate how they relate to a wide range of use cases in finance. We hope this article will not only serve as a reference for academic researchers and industry practitioners but also inspire new ideas for future research.

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“…• Deep quantum neural networks [Sak20] We briefly mention this work studying the use of deep quantum neural networks which exploit the quantum superposition properties by replacing bits by "qubits". Promising results are obtained when using these networks for regression in financial contexts such as implied volatility estimation.…”

Section: Extensions and Perspectivesmentioning

confidence: 99%

Neural networks-based algorithms for stochastic control and PDEs in finance

Germain,

Pham,

Warin

2021

Preprint

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This paper presents machine learning techniques and deep reinforcement learningbased algorithms for the efficient resolution of nonlinear partial differential equations and dynamic optimization problems arising in investment decisions and derivative pricing in financial engineering. We survey recent results in the literature, present new developments, notably in the fully nonlinear case, and compare the different schemes illustrated by numerical tests on various financial applications. We conclude by highlighting some future research directions.

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Application of deep quantum neural networks to finance

Cited by 4 publications

References 9 publications

Quantum computing for financial risk measurement

Quantum computing for financial risk measurement

A Survey of Quantum Computing for Finance

Neural networks-based algorithms for stochastic control and PDEs in finance

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