Abstract
Quantum computers provide a valuable resource to solve computational problems. The maximization of the objective function of a computational problem is a crucial problem in gate-model quantum computers. The objective function estimation is a high-cost procedure that requires several rounds of quantum computations and measurements. Here, we define a method for objective function estimation of arbitrary computational problems in gate-model quantum computers. The proposed solution significantly reduces the costs of the objective function estimation and provides an optimized estimate of the state of the quantum computer for solving optimization problems.
Highlights
Quantum computers provide a valuable resource to solve computational problems
The quantum computer produces a quantum state that yields a high value of the objective function
The framework integrates an objective function extension procedure, a quantum-gate structure segmentation stage, and a machine-learning[11,12,19,50,129,130,131,132,133,134,135] unit called quantum-gate parameter randomization machine learning (QGPR-ML), which outputs the prediction of the new quantum computer state
Summary
Quantum computers provide a valuable resource to solve computational problems. The maximization of the objective function of a computational problem is a crucial problem in gate-model quantum computers. Since each round requires the preparation of a new quantum state and the application of a high number of measurement units, a high-precision approximation of the objective function value of the quantum computer is a costly procedure. The procedure of the objective function estimation in gate-model quantum computers is a subject of optimization.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
