The clustering approach used in QVis, while effective for identifying patterns in qubit performance metrics, has several potential limitations. One significant limitation is the reliance on a fixed number of clusters (k) determined by the user, which may not accurately reflect the underlying data structure. This can lead to either overfitting or underfitting, where important patterns are either missed or misrepresented.
To improve this clustering approach, QVis could implement adaptive clustering algorithms that dynamically determine the optimal number of clusters based on the data characteristics. Techniques such as the silhouette method or the elbow method could be integrated to provide users with insights into the appropriate number of clusters based on the data's inherent structure.
Additionally, the current clustering method primarily focuses on temporal performance metrics, which may overlook the multidimensional relationships between various performance metrics, such as gate fidelity, coherence times, and error rates. To address this, QVis could employ advanced clustering techniques like hierarchical clustering or density-based spatial clustering (DBSCAN), which can capture complex relationships and identify outliers more effectively.
Furthermore, incorporating feature engineering techniques to derive new metrics that encapsulate the interactions between different performance metrics could enhance the clustering process. By visualizing these relationships in a multi-dimensional space, users would gain a more comprehensive understanding of qubit performance and the factors influencing it.