Quantum Machine Learning for Finance

Pistoia, Marco; Ahmad, Syed Farhan; Ajagekar, Akshay; Buts, Alexander; Chakrabarti, Shouvanik; Herman, Dylan; Hu, Shaohan; Jena, Andrew; Minssen, Pierre; Niroula, Pradeep; Rattew, Arthur; Sun, Yue; Yalovetzky, Romina

doi:10.48550/arxiv.2109.04298

Cited by 5 publications

(4 citation statements)

References 115 publications

(141 reference statements)

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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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“…QML is employed in many classical applications. Some notable contributions are in sciences [194][195][196][197][198][199][200], in finance [42,[201][202][203], pharmaceutical [43,45], and automotive industries [44]. In many cases, these models replaced a previously-known classical setting [204][205][206][207].…”

Section: Solving Classical Problemsmentioning

confidence: 99%

Quantum Machine Learning: from physics to software engineering

Melnikov¹,

Kordzanganeh²,

Alodjants³

et al. 2023

Preprint

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Quantum machine learning (QML) is a new, rapidly growing, and fascinating area of research where quantum information science and quantum technologies meet novel machine learning and artificial intelligent facilities. A comprehensive analysis of the main directions of current QML methods and approaches is performed in this review. The aim of our work is twofold. First, we show how classical machine learning approach can help improve the facilities of quantum computers and simulators available today. It is most important due to the modern noisy intermediate-scale quantum (NISQ) era of quantum technologies. In particular, the classical machine learning approach allows optimizing quantum hardware for achieving desired quantum states by implementing quantum devices. Second, we discuss how quantum algorithms and quantum computers may be useful for solving keystone classical machine learning tasks. Currently, quantum-inspired algorithms, which use a quantum approach to classical information processing, represent a powerful tool in software engineering for improving classical computation capacities. In this work, we discuss various quantum neural network capabilities that can be implemented in quantum-classical training algorithms for variational circuits. It is expected that quantum computers will be involved in routine machine learning procedures. In this sense, we are showing how it is essential to elucidate the speedup problem for random walks on arbitrary graphs, which are used in both classical and quantum algorithms. Quantum technologies enhanced by machine learning in fundamental and applied quantum physics, as well as quantum tomography and photonic quantum computing, are also covered.

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“…Quantum computing models can improve the learning process of existing classical models [18][19][20][21][22][23][24], allowing for better target function prediction accuracy with fewer iterations [25]. In many industries, including the pharmaceutical [26,27], aerospace [28], automotive [29], logistics [30] and financial [31][32][33][34][35] sector quantum technologies can provide unique advantages over classical computing. Many traditionally important machine learning domains are also getting potential benefits from utilizing quantum technologies, e.g.…”

Section: Introductionmentioning

confidence: 99%

Hybrid quantum physics-informed neural networks for simulating computational fluid dynamics in complex shapes

Sedykh,

Podapaka,

Sagingalieva

et al. 2024

Mach. Learn.: Sci. Technol.

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Finding the distribution of the velocities and pressures of a fluid by solving the Navier-Stokes equations is a principal task in the chemical, energy, and pharmaceutical industries, as well as in mechanical engineering and the design of pipeline systems. With existing solvers, such as OpenFOAM and Ansys, simulations of fluid dynamics in intricate geometries are computationally expensive and require re-simulation whenever the geometric parameters or the initial and boundary conditions are altered. Physics-informed neural networks are a promising tool for simulating fluid flows in complex geometries, as they can adapt to changes in the geometry and mesh definitions, allowing for generalization across fluid parameters and transfer learning across different shapes. We present a hybrid quantum physics-informed neural network that simulates laminar fluid flows in 3D $Y$-shaped mixers. Our approach combines the expressive power of a quantum model with the flexibility of a physics-informed neural network, resulting in a 21\% higher accuracy compared to a purely classical neural network. Our findings highlight the potential of machine learning approaches, and in particular hybrid quantum physics-informed neural network, for complex shape optimization tasks in computational fluid dynamics. By improving the accuracy of fluid simulations in complex geometries, our research using hybrid quantum models contributes to the development of more efficient and reliable fluid dynamics solvers.

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Quantum Machine Learning for Finance

Cited by 5 publications

References 115 publications

Quantum computing for financial risk measurement

Quantum computing for financial risk measurement

Quantum Machine Learning: from physics to software engineering

Hybrid quantum physics-informed neural networks for simulating computational fluid dynamics in complex shapes

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