| Abstrakt: | Current quantum computers are still not capable enough to solve practical problems using fully quantum algorithm. However, hybrid algorithms that split the computation between a quantum and a classical computer may be able to use the potential of present quantum computing devices. One such algorithm is the Variational Quantum Eigensolver (VQE), which is used to find the lowest energy state of a given molecule. This is achieved by repeatedly preparing and measuring the quantum state of the molecule on a quantum processor, while a classical optimization algorithm attempts to change the parameters of the state in a way that minimizes the total energy. The choice of optimization method can greatly influence both the quality of the results and the speed of the computation. This work explores the possibilities of applying the Harmonic Oscillator based Particle Swarm Optimization (HOPSO), a new guided random search method. A series of experiments have been performed to evaluate its performance and behavior on VQE based on different parameters. These experiments used a four-qubit electronic Hamiltonian of a hydrogen molecule, a standard VQE benchmark problem. The results have shown that HOPSO is capable of achieving excellent accuracy and demonstrated robust behavior on this problem. Combined with its great tunability, HOPSO has a potential to be a simple-to-use, but efficient optimization method.
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