Who can sample forever? Shot noise effects in quantum linear regression
The research analyzed Quantum Fidelity Kernels and QELMs, models that estimate quantum features using finite shots. It found that finite sampling makes these models ill-conditioned, leading to prediction errors dependent on whether training targets are exact or noisy. The study establishes a tradeoff between dataset size and per-state statistics for resource-aware training protocols.