Quantum circuit design search

This article explores search strategies for the design of parameterized quantum circuits. We propose several optimization approaches including random plus survival fittest, reinforcement learning both with classical and hybrid controllers, Bayesian as decision makers to a circuit in an automated way specific task such multi-labeled classification over dataset. introduce nontrivial architectures that are arduous be hand-designed efficient terms trainability. In addition, we reuploading initial data into circuits option find more general designs. numerically show some suggested Iris dataset accomplish better results compared established designs literature. investigate trainability these structures on unseen Glass. report meaningful advantages benchmarks Glass which supports fact inherently trainable.