Daniel Bilsborrow

Characterizing and predicting quantum dynamics is essential for the development of robust quantum technologies. However, real-world quantum devices often exhibit temporally correlated noise—non-Markovian effects where errors propagate through time and degrade quantum information processing. In this work, we present a scalable tomographic framework for multi-time quantum processes using the higher-order transformation (process tensor) formulation. Our approach accounts for scenarios where past gate errors non-trivially influence future outcomes. By integrating tensor network learning techniques, as well as shadow tomography, we achieve efficient noise characterization for large-scale quantum systems. This scalable, higher-order framework provides new avenues for accurately simulating and mitigating complex noise in next-generation quantum hardware.