Finance

Derivatives Pricing
Time Series Generation

Time Series Generation

Industry Relevance

Although real-world financial time-series data is abundant (stock prices, credit card transactions, ...), there is a growing need for representative synthetic time-series data[1] because synthetic data can: a.o. be shared without confidentiality concerns, it can be provided ‘clean’ from real-world noise, it can enable rare event modeling and it has been shown to improve the accuracy of supervised ML

Time Series Generation

Quantum in Real Life

Crédit Agricole CIB

Crédit Agricole CIB

"Quantum computing is radically different from almost everything we know and use today, in terms of theory, hardware and algorithms. This project will assemble many different competencies around the table: bankers, physicists, mathematicians, computer scientists, IT architects, all cooperating to this remarkable journey. This is a huge challenge, and we are confident to make it a success, jointly with our talented partners PASQAL and Multiverse Computing.”

Ali El Hamidi, Department Head Capital Markets Funding - Global Markets Division

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