# Financial Decision Making using Reinforcement Learning with Dirichlet Priors and Quantum-Inspired Genetic Optimization

## TL;DR

This paper introduces a hybrid reinforcement learning framework with Dirichlet priors and quantum-inspired genetic algorithms to optimize financial budget allocation, demonstrating high accuracy on real-world corporate data.

## Contribution

It combines deep RL, stochastic modeling, and quantum-inspired heuristics to improve adaptive financial decision-making over existing methods.

## Key findings

- Achieves cosine similarity of 0.9990 with actual allocations.
- Low KL divergence of 0.0023 indicates high model-data alignment.
- Effectively models shifting financial contexts using Dirichlet distributions.

## Abstract

Traditional budget allocation models struggle with the stochastic and nonlinear nature of real-world financial data. This study proposes a hybrid reinforcement learning (RL) framework for dynamic budget allocation, enhanced with Dirichlet-inspired stochasticity and quantum mutation-based genetic optimization. Using Apple Inc. quarterly financial data (2009 to 2025), the RL agent learns to allocate budgets between Research and Development and Selling, General and Administrative to maximize profitability while adhering to historical spending patterns, with L2 penalties discouraging unrealistic deviations. A Dirichlet distribution governs state evolution to simulate shifting financial contexts. To escape local minima and improve generalization, the trained policy is refined using genetic algorithms with quantum mutation via parameterized qubit rotation circuits. Generation-wise rewards and penalties are logged to visualize convergence and policy behavior. On unseen fiscal data, the model achieves high alignment with actual allocations (cosine similarity 0.9990, KL divergence 0.0023), demonstrating the promise of combining deep RL, stochastic modeling, and quantum-inspired heuristics for adaptive enterprise budgeting.