Speaker
Description
We propose a quantum-Hamiltonian-learning-based sequential reconstruction framework for dynamic two-dimensional magnetic-field maps using a local likelihood model derived from a nitrogen-vacancy-center spin-1 Hamiltonian. Local measurements are generated through nitrogen-vacancy spin dynamics governed by local magnetic-field values and a shared dipolar coupling parameter, rather than by direct observation of the latent field. Sequential Bayesian updates over overlapping scan windows are combined with temporal posterior propagation to reconstruct the evolving field.
Numerical proof-of-concept experiments on controlled synthetic maze-like magnetic-field sequences show that the proposed method reconstructs the dominant spatial structure of the tested field class, achieving a final-frame RMSE of (7.037\times10^{-7}\,\mathrm{T}). Adaptive diagnostics show decreasing expected information gain and stable local convergence, while Fisher-information and leakage diagnostics reveal a sensitivity--leakage tradeoff under long-interrogation controls.
Combined horizontal and vertical scans yield better reconstruction than single-direction acquisition in the tested setting. In contrast, the shared coupling parameter (J) is only partially identifiable: its posterior becomes narrow but remains frame-dependent and biased. At the final checkpoint, (J_{\rm std}=87.0\,\mathrm{Hz}), close to a finite-time product-state reference benchmark of (73.3\,\mathrm{Hz}), while remaining (3.35\times) above a gain-extrapolated ideal-state benchmark. The posterior mean remains biased by (326.9\,\mathrm{Hz}), indicating that posterior concentration alone does not imply unbiased coupling recovery. These results demonstrate feasibility for the tested structured field class and identify coupling estimation as the main identifiability bottleneck.
| Topic | Machine learning techniques for experimental quantum information science |
|---|---|
| Additional Topic | Hybrid quantum-classical approaches for learning and optimization |