Improved Financial Forecasting via Quantum Machine Learning

ArXiv ID: 2306.12965 “View on arXiv”

Authors: Unknown

Abstract

Quantum algorithms have the potential to enhance machine learning across a variety of domains and applications. In this work, we show how quantum machine learning can be used to improve financial forecasting. First, we use classical and quantum Determinantal Point Processes to enhance Random Forest models for churn prediction, improving precision by almost 6%. Second, we design quantum neural network architectures with orthogonal and compound layers for credit risk assessment, which match classical performance with significantly fewer parameters. Our results demonstrate that leveraging quantum ideas can effectively enhance the performance of machine learning, both today as quantum-inspired classical ML solutions, and even more in the future, with the advent of better quantum hardware.

Keywords: Quantum Machine Learning, Financial Forecasting, Random Forest, Neural Networks, Credit Risk

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 4.0/10
• Quadrant: Lab Rats
• Why: The paper introduces advanced mathematical concepts like Determinantal Point Processes and quantum orthogonal neural networks, requiring significant theoretical background. While it includes real-world data and reported metrics (6% precision improvement), the implementation details are high-level, the quantum hardware performance is limited by noise, and the primary focus remains on theoretical quantum-inspired algorithms rather than fully realized, backtest-ready models.

  flowchart TD
    A["Research Goal: Enhance Financial Forecasting<br>using Quantum Machine Learning"] --> B["Data: Financial Datasets<br>Churn Prediction & Credit Risk"]
    
    B --> C["Method 1: Quantum-Inspired DPP-Random Forest"]
    B --> D["Method 2: Quantum Neural Networks<br>Orthogonal & Compound Layers"]
    
    C --> E["Computational Process:<br>Classical/Quantum Determinantal Point Processes"]
    D --> F["Computational Process:<br>Quantum-Inspired Architecture Optimization"]
    
    E --> G["Outcome 1: Churn Prediction<br>Precision improved by ~6%"]
    F --> H["Outcome 2: Credit Risk Assessment<br>Matches classical performance<br>with significantly fewer parameters"]
    
    G --> I["Key Finding: Quantum ideas effectively<br>enhance ML via quantum-inspired<br>classical solutions and future hardware"]
    H --> I