Title: Provably Efficient Learning Algorithms for Noisy Quantum State and Process Tomography
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: quantum machine learning, quantum state tomography, quantum process tomography

Abstract: Characterizing noisy $n$-qubit states and processes is vital yet lacks scalability with conventional methods. We propose a structure-free learning algorithm that reconstructs any noisy process or state from measurement data under both unital and non-unital local noise, yielding ${\rm poly}(n,1/\epsilon)$ sample complexity and classical post-processing running time for target accuracy $\epsilon$. We numerically benchmark the algorithm on both unital and non-unital local noise channels, demonstrating a broadly applicable approach to noisy quantum process tomography. This work establishes a theoretical foundation for practical quantum-process learning and paves the way for scalable process characterization in near-term quantum devices.