Quantum computer based Feature Selection in Machine Learning

ArXiv ID: 2306.10591 “View on arXiv”

Authors: Unknown

Abstract

The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior.

Keywords: Feature Selection, Quadratic Unconstrained Binary Optimization (QUBO), Quantum Computing, Supervised Learning, Stochastic Optimization, General (Methodological)

Complexity vs Empirical Score

• Math Complexity: 8.0/10
• Empirical Rigor: 4.0/10
• Quadrant: Lab Rats
• Why: The paper introduces complex quantum computing frameworks (QUBO, QAOA, VQE) and advanced mathematical formulations, but its empirical evaluation is limited to small, simplified datasets with no mention of backtesting or real-world trading implementation.

  flowchart TD
    A["Research Goal:<br>Optimize Feature Selection for<br>Supervised Learning"] --> B["Formulate as QUBO<br>Quadratic Unconstrained Binary Optimization"]
    B --> C{"Problem Size?"}
    
    C --> D["Small Dataset<br>n < 27 features"]
    C --> E["Large Dataset<br>n = 27 features"]
    
    D --> F["Compare: Classical Numerical<br>vs Quantum Computing"]
    E --> G["Compare: Classical Stochastic<br>vs Quantum Computing"]
    
    F --> H["Outcome: Performance depends<br>on specific dataset"]
    G --> I["Outcome: Classical methods superior<br>due to quantum error rates"]
    
    H --> J["Conclusion:<br>Current quantum frameworks<br>limit competitive advantage"]
    I --> J