Grover Search for Portfolio Selection
A. Ege Yilmaz, Stefan Stettler, Thomas Ankenbrand, Urs Rhyner
TL;DR
This paper introduces quantum oracles tailored for Grover's algorithm to optimize portfolio selection based on investor preferences, including return, risk, and Sharpe ratio, demonstrated through quantum simulations.
Contribution
It presents novel quantum oracles for portfolio selection that incorporate investor criteria, enabling more efficient quantum optimization methods.
Findings
Successfully implemented portfolio selection oracles on quantum simulators.
Demonstrated potential for quantum algorithms to improve financial decision-making.
Provides a framework for future quantum finance applications.
Abstract
We present explicit oracles designed to be used in Grover's algorithm to match investor preferences. Specifically, the oracles select portfolios with returns and standard deviations exceeding and falling below certain thresholds, respectively. One potential use case for the oracles is selecting portfolios with the best Sharpe ratios. We have implemented these algorithms using quantum simulators.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Quantum Mechanics and Applications · Quantum Information and Cryptography