Surrogate-based optimization for variational quantum algorithms

Towards Practical Quantum Variational Algorithms

The preparation of quantum states using short quantum circuits is one of the most promising near-term applications of small quantum computers, especially if the circuit is short enough and the fidelity of gates high enough that it can be executed without quantum error correction. Such quantum state preparation can be used in variational approaches, optimizing parameters in the circuit to minimi...

17 Surrogate - Based Optimization

Surrogate-Based Optimization

Surrogate-based optimization (Queipo et al. 2005, Simpson et al. 2008) represents a class of optimization methodologies that make use of surrogate modeling techniques to quickly find the local or global optima. It provides us a novel optimization framework in which the conventional optimization algorithms, e.g. gradient-based or evolutionary algorithms are used for sub-optimization(s). Surrogat...

RBF-based surrogate model for evolutionary optimization

Many today’s engineering tasks use approximation of their expensive objective function. Surrogate models, which are frequently used for this purpose, can save significant costs by substituting some of the experimental evaluations or simulations needed to achieve an optimal or near-optimal solution. This paper presents a surrogate model based on RBF networks. In contrast to the most of the surro...

Learning surrogate models for simulation-based optimization

We address a central problem in modeling, namely that of learning an algebraic model from data obtained from simulations or experiments. We propose a methodology that uses a small number of simulations or experiments to learn models that are as accurate and as simple as possible. The approach begins by building a low-complexity surrogate model. The model is built using a best subset technique t...