Robust Ground State Tomography with Neural Networks
Published in Proc. Korea Information and Communications Society (KICS) Winter Conference, 2022
Recommended citation: S. M. Kazim, J. ur Rehman, and H. Shin, "Robust Ground State Tomography with Neural Networks," Proc. Korea Information and Communications Society (KICS) Winter Conference, Feb. 2022
Abstract – In this paper, we present a neural network quantum state tomography scheme, which offers improvement in generalization to unseen data over conventional methods. Quantum state tomography (QST) is a resource intensive task and requires prohibitively large processing for even moderately sized quantum systems. Here, we perform the tomography of a 4-qubit 2-local Hamiltonian with true and estimated expectations with respect to a small set of observables, which is sufficient to achieve high fidelity. This method is scalable to larger states and Hamiltonians with arbitrary structures.