Visualization techniques are crucial for enhancing the understanding of complex datasets, especially in fields like quantum computing. QHAna is a tool specifically designed to allow users to explore and experiment with datasets generated by quantum computing and quantum machine learning. Built on the RAMP architecture, QHAna integrates additional capabilities through microservices and plugins. The aim of this thesis is to expand QHAna’s functionality by implementing new visualization methods. To ensure the effectiveness of these additions, an evaluation of various visualization techniques will be conducted, focusing solely on those relevant to quantum computing and quantum machine learning. Following this assessment, selected techniques will be implemented, thereby enriching QHAna’s ability to support the interpretation of generated data, especially data generated by quantum computing and quantum machine learning. The micro frontends and generated visualizations for all implemented plugins will be provided to demonstrate their utility. The implementation of these plugins will be discussed briefly, focusing on implementation challenges and plugin architecture.