Achieving overparametrization in NISQ-friendly quantum circuits

Ascertain methods to reach overparametrization in noisy intermediate-scale quantum (NISQ) scenarios while accommodating copy complexity constraints, degraded precision scaling, and the difficulty of embedding many independent parameters into practical quantum circuits.

Background

Classical overparametrized models help mitigate poor cost landscapes, turning many local minima into saddle points and improving trainability. Translating this success to quantum circuits is challenging because deep or wide circuits may be needed, which is difficult on near-term hardware and can exacerbate barren plateaus.

Moreover, backpropagation-like training in quantum settings is hindered by the no-cloning constraint. Some approaches reuse a few copies of states to emulate backpropagation, but at the cost of poor precision scaling. These hurdles make it unclear how to practically realize overparametrization in NISQ-friendly circuits.