Quantum Portfolio Optimizer
Version updated for https://github.com/shymonski-dev/quantum-portfolio-optimizer to version v1.0.
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Action Type
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Action Summary
The Quantum Portfolio Optimizer is a hybrid quantum-classical application designed to solve the Markowitz portfolio optimization problem by converting asset allocation into a Quadratic Unconstrained Binary Optimization (QUBO) problem. Utilizing advanced quantum algorithms like Variational Quantum Eigensolver (VQE) and Quantum Approximate Optimization Algorithm (QAOA), it automates optimal portfolio construction while incorporating ESG constraints, CVaR risk objectives, and integer-constrained baselines for comparison. Optimized for IBM Quantum Hardware, it provides efficient quantum circuit transpilation, dynamic partitioning, and high-performance solutions for institutional-grade financial data analysis.
Release notes
This release packages Quantum Portfolio Optimizer as an open source toolkit and GitHub Action for running portfolio optimization experiments with quantum and classical methods, including structured outputs for repeatable testing in workflows. It is intended strictly for testing and experimental research use only, and it can run both on local simulation and on actual IBM Quantum hardware through IBM Runtime.