AI in EE

AI IN DIVISIONS

AI in Wave Division

AI in EE

AI IN DIVISIONS

AI in Wave Division

AI in Wave Division

Research introduction on "Quantum tomography via classical machine learning."

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.

Copyright ⓒ 2015 KAIST Electrical Engineering. All rights reserved. Made by PRESSCAT

Copyright ⓒ 2015 KAIST Electrical Engineering. All rights reserved. Made by PRESSCAT

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한국과학기술원(KAIST)
Tel. 042-350-3411   Fax. 042-350-3410

Copyright ⓒ 2015 KAIST Electrical
Engineering. All rights reserved.
Made by PRESSCAT