Variational Quantum Circuit-Based Reinforcement Learning for Dynamic Portfolio Optimization

By Vincent Gurgul, Ying Chen, Stefan Lessmann

Published 2026-01-29

Everscope rating

1101.4

Relevance to quantitative trading

8 / 10

Implementation complexity

9 / 10

Reproducibility

5 / 5

About this paper

Methodology: Quantum Reinforcement Learning with Variational Quantum Circuits (QRL-VQC). Problem types: Portfolio Optimization, Reinforcement Learning, Optimization, Time Series Forecasting.

arXiv:2601.18811 · Code · Paper rankings

Open the interactive Everscope explorer for full analysis, charts, and paper battles.