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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.
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