Watching Quantum Models Think: Hilbert-Space Interpretability in Quantum Transformer Blocks
Deep learning models are powerful but opaque. As quantum machine learning matures, the field faces a defining choice: build quantum models that are equally opaque, or exploit the mathematical structure of quantum mechanics to make them inherently interpretable. We show that the latter is possible. By tracking quantum mutual information~(MI), entanglement entropy, and state fidelity through the layers of a Quantum Transformer Block (\qtb{}), a fully-coherent variational circuit with quantum analogues of both attention and feedforward, we gain direct insight into how the model processes information: which tokens it attends to, when correlations form, and why predictions fail. On four tasks ...
| Authors | Diego Iacopetta, Andrea Gasparini |
|---|---|
| Primary category | quant-ph |
| Published | 19 September 2026 |
| Last updated at source | 19 September 2026 |
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