Study on The Effect of Encoding Method in Quantum Machine Learning

Quantum machine learning algorithms use qubit encoding and quantum circuits to perform feature extraction and pattern recognition. However, the choice of data encoding methods can have a significant impact on the model's performance. To evaluate the effect of different encoding methods in a systematic way, we propose two metrics: distribution distance and distribution radius. These metrics describe how the encoded data distribute in the Hilbert space. We show that there is a positive correlation between prediction accuracy and distribution distance, and a negative correlation between prediction accuracy and distribution radius, both theoretically and experimentally. Based on our findings, we suggest a comparative evaluation of data encoding methods for quantum machine learning, which can help improve the learning efficiency.