The performance of variational quantum algorithms (VQAs) critically depends on the objective function, which in turn relies on the form of the variational ansatz. A good ansatz translates to relatively shallow circuits and involves a low number of classical optimization parameters. These features can be achieved more easily if the ansatz knows something about the problem that is simulated. In this talk, I will give a brief background on VQAs and present our techniques for problem-tailored ansatze. These include symmetry-preserving circuits, the ADAPT-VQE algorithm, and a pulse-based VQE, which we named ctrl-VQE. Our simulations show that these techniques outperform competing ansatze in terms of circuit depth, accuracy, and trainability.
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