Sparsified Kolmogorov-Arnold Networks for Interpretable Quantum State Tomography
Machine-learning approaches to quantum state tomography can achieve high reconstruction fidelity, but the physical structure used by the trained model often remains implicit. Here we ask whether a sparsified Kolmogorov-Arnold Network (KAN) can be used not only as a regressor, but also as an inspectable reconstruction rule whose internal organization can be checked against known Pauli structure. We study a controlled three-qubit GHZ-family benchmark in which all 63 non-identity Pauli expectation values are used to reconstruct three GHZ-subspace variables: the population imbalance $z$, the real off-diagonal component $c$, and the imaginary off-diagonal component $s$. Under finite-shot sampl...
| Authors | Xinge Wu, Huaxin Wang, Jiajun Liu et al. |
|---|---|
| Primary category | quant-ph |
| Published | 10 June 2026 |
| Last updated at source | 10 June 2026 |
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