Feature Selection on Quantum Computers

Understand

In machine learning, fewer features reduce model complexity.

• Carefully assessing the influence of each input feature on the model quality is therefore a crucial preprocessing step.
• We propose a novel feature selection algorithm based on a quadratic unconstrained binary optimization (QUBO) problem, which allows to select a specified number of features based on their importance and redundancy.
• In contrast to iterative or greedy methods, our direct approach yields higherquality solutions.