Quantum Tomography for Collider Physics: A Data-Driven Approach to Imaging Hadronic Structure

We present quantum tomography as a novel data-driven framework for uncovering the internal structure of hadrons in high-energy collisions. This approach adapts principles of quantum state reconstruction—widely used in quantum information science—to collider physics, enabling the extraction of distributions directly from experimental data.

This methodology is particularly well-suited to the complex, high-dimensional datasets emerging from current and future collider experiments, such as those at the LHC and the upcoming Electron-Ion Collider (EIC). We discuss how quantum tomography bridges the gap between data and theory, offering an interpretable and model-agnostic pathway to hadronic imaging. Applications include reconstructing Wigner-like distributions and accessing 3D information encoded in exclusive and semi-inclusive processes.

The talk will emphasize how quantum tomography exemplifies a broader paradigm shift in nuclear physics—where data science, statistical inference, and physical insight converge to extract meaningful information from ever-more complex experimental environments. Open-source tools and conceptual tutorials will be highlighted to encourage broader community engagement with this technique.