THE NEW PAPER FROM THE QUANTUM FOR QUANTS WORKGROUP OF THE QUANTUM WORLD ASSOCIATION-QWA REVIEWS THE APPLICATION OF QUANTUM COMPUTING TO FINANCIAL PROBLEMS

Barcelona, 12 July 2018. – Three members of the Quantum-for-Quants Workgroup of the Quantum World Association (QWA), Roman Orus, Sam Mugel and Enrique Lizaso have posted a paper in arXiv discussing how quantum computation can be applied to financial problems, and providing an overview of the current approaches and the potential prospects of these technologies in the specific field of Finance:

http://arxiv.org/abs/1807.03890

The paper visits three main groups of applications: the first one is the quantum optimization algorithms, exposing how quantum annealers may be used to optimize portfolios, find arbitrage opportunities, and perform credit scoring. In the second part the paper visits another rising field in Finance, the application of Artificial Intelligence. The authors discuss deep-learning in finance and give suggestions to improve these methods through quantum machine learning. In the last group of applications, the paper analyzes the quantum amplitude estimation, and how it can result in a quantum speed-up for Monte Carlo sampling. This last interesting point has direct applications to many current financial methods, including pricing of derivatives and risk analysis. Finally, the authors discuss the possible perspectives for the future.

This paper represents a comprehensive review of the current situation, perspectives and publications, and helps readers identify the main lines in which financial problems may take advantage in the future from quantum computing. The authors have been supported and encouraged by the rest of the members of the Quantum-for-Quants Workgroup of the QWA in their work.

The Quantum-for-Quants Workgroup and the Quantum World Association make a call to all companies and individuals interested in these fields to contact the Quantum-for-Quants Workgroup at q4qc@quantumwa.org. We are working in bridging the gap between Quantum Computing and Finance.