# A Survey of Quantum Computing for Finance

**Authors:** Dylan Herman, Cody Googin, Xiaoyuan Liu, Alexey Galda, Ilya Safro, Yue Sun, Marco Pistoia, Yuri Alexeev  
**arXiv:** [2201.02773](https://arxiv.org/abs/2201.02773)  
**Date:** 2022-01-08  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2201.02773v1/

## Key points

- Quantum computing is poised to revolutionize finance by surpassing classical computers in computational power within the next decade.
- Finance is projected to be the first sector to benefit significantly from quantum computing advancements.
- Key areas of focus for quantum computing in finance include Monte Carlo integration, optimization techniques, and machine learning algorithms.
- Quantum algorithms can efficiently address complex financial problems such as derivative pricing, risk analysis, portfolio optimization, natural language processing tasks, and fraud detection mechanisms.
- The feasibility of implementing these quantum algorithms on near-term quantum computers using various hardware configurations is being explored.

## Summary

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Quantum computing is set to revolutionize the field of finance, surpassing classical computers in computational power within the next decade. This transformative technology is expected to have a significant impact on various industry sectors, with finance leading the way in reaping its benefits. In fact, finance is projected to be the first sector to see immediate rewards from quantum computing advancements. A recent survey paper by Dylan Herman et al. provides a comprehensive overview of the current state of quantum computing for financial applications, with a focus on key areas such as Monte Carlo integration, optimization techniques, and machine learning algorithms. By adapting these solutions for quantum computer architecture, researchers demonstrate their ability to efficiently and accurately address complex financial problems including derivative pricing, risk analysis, portfolio optimization, natural language processing tasks, and fraud detection mechanisms. The authors also explore the feasibility of implementing these quantum algorithms on near-term quantum computers using various hardware configurations. Through detailed discussions and analyses, they showcase how these advanced computational methods are relevant across a wide spectrum of use cases within the finance industry. This survey paper serves as a valuable reference for academic researchers and industry practitioners alike while also inspiring new avenues for future research in leveraging quantum computing for financial applications. With 56 pages and 5 tables providing in-depth insights into this cutting-edge field at the intersection of quantum physics and finance, this work sheds light on the potential transformative power that quantum computing holds for shaping the future landscape of financial services.