arXiv:2606.11814v1 Announce Type: cross Abstract: 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
The increasing complexity of quantum systems and the drive for more interpretable AI models are converging, leading to research into transparent machine learning for quantum state characterization.
This research offers a method to not only achieve high-fidelity quantum state reconstruction but also to understand the underlying physical structure used by the AI model, critical for validation and trust in quantum computing.
The ability to interpret AI models used in quantum state tomography could accelerate the debugging and development of quantum processors by providing insights into their physical behavior rather than just black-box results.
- · Quantum computing researchers
- · Quantum hardware developers
- · AI interpretability specialists
- · Developers relying solely on black-box AI models for quantum analysis
Improved understanding and validation of complex quantum states through interpretable AI.
Faster iteration and error correction in the design and fabrication of next-generation quantum computers.
Enhanced trust and broader adoption of AI-driven tools within the quantum physics community, potentially leading to new algorithmic discoveries.
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Read at arXiv cs.LG