Practical Portfolio Optimization with Warm-Starting Qubits using QAOA

[RoyalWeden]

Team Name:

Quantastox

Project Description:

Almost everyone knows about stocks, and many take their well-earned money to invest in the ones they feel may be profitable. Through utilizing the Quantum Approximate Optimization Algorithm (QAOA) to solve Quantum Unconstrained Binary Optimization problems with warm-starting QAOA, an optimal portfolio can be generated with identical results to classical systems. This is done with Yahoo Finance stock time-series data.

QAOA is a great way to solve combinatorial optimization problems on Noisy Intermediate-Scale Quantum devices, which is why it is the main algorithm used in this project. Since QAOA gives just an approximate, I saw the benefit in warm-starting the qubits into an initial state. This allows the quantum algorithm to inherit the performance guarantees of a classical algorithm. The full implementation of this hybrid quantum-classical algorithm for portfolio optimization returns great results even with such complex systems to solve.

Presentation:

Non-Technical Slideshow: https://github.com/RoyalWeden/QHack-PortfolioOptimization/blob/main/Quantastox%20PowerPoint.pdf

While my source code, as a Jupyter Notebook, does have code throughout, it also provides an explanation to methods and algorithms used for optimization. https://github.com/RoyalWeden/QHack-PortfolioOptimization

Source code:

https://github.com/RoyalWeden/QHack-PortfolioOptimization

Which challenges/prizes would you like to submit your project for?

• IBM Qiskit Challenge
• Google Quantum AI Research Challenge --- arXiv:2004.04197
• Hybrid Algorithms Challenge
• QAOA Challenge
• Quantum Finance Challenge
• Young Scientist Challenge

[isaacdevlugt]

Thank you for your submission! There's still time to populate your submission with code, presentation material, etc. Please make any final adjustments before the deadline tonight at 17h00 EST!

Good luck!