Title: Quantum tomography via classical machine learning
Authors: Changjun Kim, Daniel Kyungdeock Park, June-Koo Kevin Rhee
Determination of a wave function or a density matrix of a quantum system and/or its dynamics is of fundamental importance in quantum information science. Unfortunately, the computational cost of full quantum state and process tomography grow exponentially with the number of qubits. In this research project, we are exploring the possibilities to apply classical machine learning techniques such as linear regression and deep learning to assist quantum tomography tasks.
