相关论文: Evolving Quantum Circuits using Genetic Algorithms
Administratively withdrawn.
This paper has been withdrawn as it has been superseded by 0808.2697
This paper is withdrawn because the results in the paper are included in a paper to be published in Mathematical and Computer Modelling.
Paper is taken out with immediate effect.
This paper has been withdrawn.
This paper has been withdrawn.
This paper has been withdrawn by the author due to some problems.
This paper is withdrawn because the results in the paper are included in a paper to be published in Mathematical and Computer Modelling.
This is article is taken out.
This paper has been withdrawn by the author(s), due to the existence of a much better paper in http://arxiv.org/abs/cs.CR/0207027
Demonstrating quantum advantage using conventional quantum algorithms remains challenging on current noisy gate-based quantum computers. Automated quantum circuit synthesis via quantum machine learning has emerged as a promising solution,…
Variational quantum circuits build the foundation for various classes of quantum algorithms. In a nutshell, the weights of a parametrized quantum circuit are varied until the empirical sampling distribution of the circuit is sufficiently…
This paper was withdrawn by the author.
This paper was withdrawn by the author. It turns out that similar ideas have been presented before. The author apologizes.
This manuscript contains an outline of lectures course "Evolutionary Algorithms" read by the author. The course covers Canonic Genetic Algorithm and various other genetic algorithms as well as evolutionary strategies, genetic programming,…
This paper was withdrawn by the authors.
This paper has been temporarily withdrawn for corrections.
This paper has been withdrawn due to its publication
This paper has been withdrawn by the author due to an error in the proof.
We use elementary variational arguments to prove, and improve on, gap estimates which arise in simulating quantum circuits by adiabatic evolution.