Explainable Quantum AI Advances Encoder Selection Via Novel Visualization Tools
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Quantum neural networks represent a potentially transformative fusion of quantum computing and artificial intelligence, promising significant speed-ups for complex data processing, yet a critical challenge hinders their widespread adoption: selecting the optimal method for converting classical data into a quantum format. Shaolun Ruan from Singapore Management University, Feng Liang, Rohan Ramakrishna, and Rudai Yan from Nanyang Technological University, alongside Chao Ren from KTH Royal Institute of Technology and Qiang Guan from Kent State University, now present a new visualization tool that directly addresses this problem. Their innovation, XQAI-Eyes, allows developers to compare classical data characteristics with their quantum representations, offering unprecedented insight into how different encoding methods impact performance. Evaluations across multiple datasets demonstrate that XQAI-Eyes not only supports the exploration of encoder design, but also reveals key principles for effective encoder selection, paving the way for more transparent and optimised quantum AI systems.
Visualizing Quantum Neural Network Encoder Performance Scientists have developed XQAI-Eyes, a novel visualization system designed to enhance understanding of quantum neural network (QNN) encoders, crucial components that transform classical data into quantum states. The work addresses a significant challenge in QNN development: the lack of systematic guidance for selecting appropriate encoders, which currently relies heavily on trial and error. Researchers formulated design requirements through interviews with QNN developers, identifying a need for tools that bridge the gap between encoder design and QNN performance. To meet this need, the team proposed two innovative methods, Encoder Expectation Measurement and Quantum Distribution View.
Encoder Expectation Measurement extracts the expectation value of each original data point, transforming abstract encoded states into observable “classical” variables, allowing direct assessment of how data features are encoded.
The Quantum Distribution View visually highlights how well the encoder distinguishes between different classes of data, providing a clear overview of mixed quantum states. Through integration of these methods, XQAI-Eyes enables users to explore how different encoders capture data features and impact model performance across 60 test cases, utilizing 6 datasets and 10 encoders. Evaluations with quantum computing experts demonstrate the system’s effectiveness, revealing two key practices for encoder selection grounded in pattern preservation and feature mapping. To the best of their knowledge, this work represents the first visual explanation of quantum encoder performance, bridging the gap between encoder reasoning and overall QNN performance.
The team has made XQAI-Eyes publicly available to benefit QNN developers, offering a holistic and transparent approach to optimizing encoder design. XQAI-Eyes Reveals Encoder-Performance Relationships This research presents XQAI-Eyes, a novel visualization tool designed to enhance understanding of quantum neural network (QNN) performance by focusing on the crucial role of the encoder. The tool bridges classical and quantum perspectives, allowing developers to compare classical data features with their corresponding quantum encoded states and examine how well different classes are distinguished within the quantum realm. Evaluations across diverse datasets and encoder designs demonstrate XQAI-Eyes’s ability to support exploration of the relationship between encoder design and overall QNN effectiveness, offering a transparent approach to optimization. Through use of the tool, domain experts derived two key practices for encoder selection. These strategies emphasize the importance of aligning feature representations with the training dataset to reduce optimization complexity and encoding data to ensure clear distinction between classes, leading to smoother convergence during model training. The experts highlighted the innovation of the Encoder Expectation Measurement and the intuitive visualization provided by the Quantum Distribution Map, which offers immediate clarity on encoder performance relative to maximum mixed states. The authors acknowledge that achieving optimal QNN performance relies on the combined effect of both the quantum encoder and the overall network structure, though a detailed investigation of network design falls outside the scope of this work. The research contributes a significant advance in explainable quantum neural networks, offering a valuable tool for developers and providing new insights into the often opaque process of encoder selection. 👉 More information 🗞 Towards Explainable Quantum AI: Informing the Encoder Selection of Quantum Neural Networks via Visualization 🧠 ArXiv: https://arxiv.org/abs/2512.14181 Tags: Rohail T. As a quantum scientist exploring the frontiers of physics and technology. My work focuses on uncovering how quantum mechanics, computing, and emerging technologies are transforming our understanding of reality. I share research-driven insights that make complex ideas in quantum science clear, engaging, and relevant to the modern world. Latest Posts by Rohail T.: Shadow Formulation of In-in Correlators Enables New Insights into Four Dimensional De Sitter Space December 18, 2025 Dilepton Production Reveals Quark-Gluon Plasma Acceleration Effects December 18, 2025 Quantum Simulation Advances Classical Systems Using Koopman-von Neumann Mapping and Unitary Evolution December 18, 2025