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Case Study: Quantum Financial Engineering on AWS Cloud

Design, development and delivery of a Quantum Platform on AWS Cloud for optimising investment portfolios.

  • S3
  • EC2
  • EKS
  • EMR
  • QLDB
  • DynamoDB
  • Terraform

Project Background

The financial industry has been facing challenges with traditional portfolio optimization methods, which are based on historical data and do not take into account the potential impact of real-time macroeconomic events on investment performance.

Upon finalising the scope with the client, we set out to create a quantum computing platform on AWS that would address these issues by incorporating quantum computing techniques to optimise investment portfolios under different metrics and by including relevant real-time macroeconomic data.



Research & Development

Leveraging our experience in Quantitative Finance and Quantum Computing, our research and development personnel began by studying the existing portfolio optimization methods and identifying their limitations in terms of incorporating real-time macroeconomic data and leveraging quantum computing techniques.

We also conducted market research to understand the specific needs and requirements of financial institutions in terms of portfolio optimization and risk management, together with infrastructure limitations.

Based on this research, we designed a platform that utilises quantum computing techniques to optimise investment portfolios under different metrics, such as return-to-volatility. The platform also includes a feature that allows for the analysis of a larger universe of assets, including alternative assets and the incorporation of relevant real-time macroeconomic data.

Architecture

To ensure high scalability and performance, we deployed the platform on AWS using a complex and highly available architecture.

We also improved traditional cybersecurity practices by enabling quantum-encryption of the information with the latest on-cloud security technologies.

Deployment

Once development was complete, we deployed the platform on AWS. The platform was deployed using a Kubernetes cluster, which allowed us to easily scale the solution as needed and handle high traffic loads.

We used Terraform for infrastructure as code, which allowed us to easily provision and manage the infrastructure on AWS.

To store and process the financial data, we used Amazon S3, Amazon DynamoDB, and Amazon Elastic MapReduce (EMR). These services allowed us to store and process the data in a highly available and scalable manner, while also providing built-in security features to protect user data.

Conclusion

Through the development and deployment of this quantum platform on AWS, we were able to provide financial institutions with a more efficient and effective way to optimise investment portfolios.

The platform incorporates quantum computing techniques and relevant real-time macroeconomic data, allowing for more accurate and robust portfolio optimization.

By leveraging AWS managed services, we were able to achieve high scalability, performance, and security while complying with industry regulations.

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