The Art of Quantum Computing for Finance - SSRN
A new review details how quantum computing is transforming finance, focusing on quantum machine learning, deep learning, and financial derivatives. Quantum computers can solve complex problems like risk analysis and option pricing much faster than classical systems. Notable advances include the quantum Black-Scholes model and quantum Monte Carlo methods. The study introduces terms like quantum adversarial attacks and explores vulnerabilities. While quantum methods show promise, real-world use is limited by hardware issues such as decoherence.
What it examines
This paper reviews how quantum computing can solve complex problems in finance, such as asset management, risk analysis, and derivatives pricing. It explores quantum algorithms, quantum machine learning, and their potential to improve financial models, focusing on current methods, benefits, challenges, and future prospects in the financial industry.
What it concludes
Quantum computing offers promising speedups for financial tasks like risk analysis, portfolio optimization, and option pricing. However, practical use is limited by hardware challenges. Future research should focus on building better quantum processors and algorithms, with applications in banking, trading, fraud detection, and financial risk management.
Evidence objects
Quantum computing is rapidly transforming finance, enabling faster risk analysis, portfolio optimization, and option pricing through quantum machine learning and deep learning, with models like quantum Black-Scholes and quantum Monte Carlo showing exponential speedups.
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The paper introduces novel concepts such as 'quantum adversarial attacks' and quantum noise injection, revealing both the promise and vulnerabilities of quantum-enhanced financial models, based on extensive literature review and practical quantum hardware experiments.
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Despite theoretical advantages, real-world quantum finance faces hardware challenges like decoherence; the authors urge more experimental work and error-correction, noting the lack of detailed empirical results and real-world case studies in current research.
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This paper offers a comprehensive survey of quantum computing applications in finance, spanning quantum machine learning, deep learning, and their roles in trading, portfolio optimization, derivatives pricing, and risk management. While it synthesizes current literature effectively, it lacks original methodologies or novel insights, serving primarily as an informative, state-of-the-art overview.
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Raw abstract and provenance
Brito, “Improved financial forecasting via quantum machine learning,” Quantum. Machine Intelligence, vol. Marquardt,. “Artificial intelligence and machine
Source row: 1922 · abstract type: snippet