Related papers: Optimizing Variational Quantum Circuits using Evol…

In variational quantum algorithms, parameterization is typically applied to single-qubit gates.In this study, we instead parameterize a generalized controlled gate and propose an algorithm to locally minimize the cost function by maximally…

This paper is withdrawn

This paper has been withdrawn by the author(s), due a crucial error in Eqn. 30.

This paper has been withdrawn by the authors.

This paper has been withdrawn by the author(s), due to acceptance of the paper for publication in Physical Chemistry Chemical Physics.

This paper has been withdrawn by the author due to a crucial sign error in equation 1

We investigate the potential of bio-inspired evolutionary algorithms for designing quantum circuits with specific goals, focusing on two particular tasks. The first one is motivated by the ideas of Artificial Life that are used to reproduce…

The paper has been withdrawn due to numerical error.

This paper was withdrawn by the authors.

Traditional quantum circuit optimization is performed directly at the circuit level. Alternatively, a quantum circuit can be translated to a ZX-diagram which can be simplified using the rules of the ZX-calculus, after which a simplified…

The balance of exploration versus exploitation (EvE) is a key issue on evolutionary computation. In this paper we will investigate how an adaptive controller aimed to perform Operator Selection can be used to dynamically manage the EvE…

This paper has been withdrawn by the author due to some mistakes

This paper has been withdrawn by the author(s), due a crucial error on the entanglement of $\Gamma$ registers.

We combine two popular optimization approaches to derive learning algorithms for generative models: variational optimization and evolutionary algorithms. The combination is realized for generative models with discrete latents by using…

Withdrawn: replaced by e-Print: arXiv:0810.0063 [hep-ph]

This article is withdrawn because the co-authors are not in favor of publication.

The performance of evolutionary algorithms can be heavily undermined when constraints limit the feasible areas of the search space. For instance, while Covariance Matrix Adaptation Evolution Strategy is one of the most efficient algorithms…

The article is taken out.

This paper was withdrawn by the author. It turns out that similar ideas have been presented before. The author apologizes.

This article has been withdrawn.