Quantum Machine Learning for Finance

Abstract

Quantum computers are expected to surpass the computational capabilities of classical computers during this decade, and achieve disruptive 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 review paper presents the state of the art of quantum algorithms for financial applications, with particular focus to those use cases that can be solved via Machine Learning.

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Cited by in corpus (8)

• Quantum computing for finance
• Constrained Quantum Optimization for Extractive Summarization on a Trapped-ion Quantum Computer
• Quantum version of the k-NN classifier based on a quantum sorting algorithm
• Expressivity of Variational Quantum Machine Learning on the Boolean Cube
• QRAM: A Survey and Critique
• Approaching Collateral Optimization for NISQ and Quantum-Inspired Computing
• Contextual Quantum Neural Networks for Stock Price Prediction
• Evaluating Variational Quantum Circuit Architectures for Distributed Quantum Computing