Towards Explainable Quantum AI: Informing the Encoder Selection of Quantum Neural Networks via Visualization

TL;DR

This paper introduces XQAI-Eyes, a visualization tool that helps developers understand and select effective quantum encoders for Quantum Neural Networks by comparing classical data features with quantum states.

Contribution

The paper presents a novel visualization method that bridges classical and quantum data analysis, aiding in systematic encoder selection for QNNs.

Findings

XQAI-Eyes enables comparison of classical features and quantum states.

It helps identify effective encoders based on pattern preservation.

Domain experts derived practical encoder selection practices.

Abstract

Quantum Neural Networks (QNNs) represent a promising fusion of quantum computing and neural network architectures, offering speed-ups and efficient processing of high-dimensional, entangled data. A crucial component of QNNs is the encoder, which maps classical input data into quantum states. However, choosing suitable encoders remains a significant challenge, largely due to the lack of systematic guidance and the trial-and-error nature of current approaches. This process is further impeded by two key challenges: (1) the difficulty in evaluating encoded quantum states prior to training, and (2) the lack of intuitive methods for analyzing an encoder's ability to effectively distinguish data features. To address these issues, we introduce a novel visualization tool, XQAI-Eyes, which enables QNN developers to compare classical data features with their corresponding encoded quantum states and to examine the mixed quantum states across different classes. By bridging classical and quantum perspectives, XQAI-Eyes facilitates a deeper understanding of how encoders influence QNN performance. Evaluations across diverse datasets and encoder designs demonstrate XQAI-Eyes's potential to support the exploration of the relationship between encoder design and QNN effectiveness, offering a holistic and transparent approach to optimizing quantum encoders. Moreover, domain experts used XQAI-Eyes to derive two key practices for quantum encoder selection, grounded in the principles of pattern preservation and feature mapping.