VQE Portfolio Optimization¶
PennyLane quantum optimization
Portfolio Optimization via VQE
A modular research toolkit for portfolio selection and allocation with binary VQE, QAOA, QUBO/Ising mappings, lambda sweeps, and fractional simplex-constrained VQE.
Research workflows
Quantum-native formulations for portfolio optimization
The package keeps binary, QAOA, and fractional workflows on a common data model so experiments can compare selection probabilities, feasible candidates, allocations, costs, and efficient-frontier traces.
Binary VQE
Select exactly K assets by mapping a cardinality-constrained mean-variance objective to a QUBO and Ising Hamiltonian.
QAOA
Solve the same binary objective with alternating cost and mixer Hamiltonians, samples, marginal probabilities, and feasible picks.
Fractional VQE
Optimize long-only continuous allocations with the simplex constraint enforced by construction rather than by penalties.
Published package
Installable Python tooling
The PyPI package exposes reusable APIs and a first-class command-line interface for running experiments without notebooks.
Notebook clients
Examples and real-data workflows
Notebooks in this repository are thin clients around the public API. Start with synthetic binary, QAOA, and fractional examples, then move into the real market-data examples and lambda-sweep workflows.
Documentation and source
Read the full project materials
This page is generated by the repository's Sphinx Pages workflow. The deeper project documentation remains available as generated HTML from the repository Markdown files.