Encoding Numerical Data for Generative Quantum Machine Learning
The study investigated how encoding continuous numerical data for generative quantum machine learning models affects their performance. It found that using Gray codes, specifically reflected Gray codes, conserves data structure and avoids artificial correlations compared to standard binary codes. This leads to faster and more accurate training for quantum circuits.