Objective function maximization by machine learning for gate-model quantum computers

L. Gyongyosi; S. Imre

doi:10.1117/12.2558041

24 April 2020 Objective function maximization by machine learning for gate-model quantum computers

L. Gyongyosi, S. Imre

Proceedings Volume 11391, Quantum Information Science, Sensing, and Computation XII; 113910L (2020) https://doi.org/10.1117/12.2558041
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

We define a framework for objective function estimation and maximization of arbitrary computational problems in gatemodel quantum computers. The method significantly reduces the costs of the objective function estimation and provides an estimate of the new state of the quantum computer. The framework integrates an objective function extension procedure, a segmentation algorithm that utilizes the gate parameters of the quantum computer, and a machine-learning unit for the quantum state prediction. The results are particularly convenient for the performance optimization of experimental gate-model quantum computations.

Citation Download Citation

L. Gyongyosi and S. Imre "Objective function maximization by machine learning for gate-model quantum computers", Proc. SPIE 11391, Quantum Information Science, Sensing, and Computation XII, 113910L (24 April 2020); https://doi.org/10.1117/12.2558041

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available