  Barren plateaus appear to be a major obstacle to using variational quantum
algorithms to simulate large-scale quantum systems or replace traditional
machine learning algorithms. They can be caused by multiple factors such as
expressivity, entanglement, locality of observables, or even hardware noise. We
propose classical splitting of ans\"atze or parametrized quantum circuits to
avoid barren plateaus. Classical splitting is realized by splitting an $N$
qubit ansatz to multiple ans\"atze that consists of $\mathcal{O}(\log N)$
qubits. We show that such an ansatz can be used to avoid barren plateaus. We
support our results with numerical experiments and perform binary
classification on classical and quantum datasets. Then, we propose an extension
of the ansatz that is compatible with variational quantum simulations. Finally,
we discuss a speed-up for gradient-based optimization and hardware
implementation, robustness against noise and parallelization, making classical
splitting an ideal tool for noisy intermediate scale quantum (NISQ)
applications.
