  Nano-optic imagers that modulate light at sub-wavelength scales could unlock
unprecedented applications in diverse domains ranging from robotics to
medicine. Although metasurface optics offer a path to such ultra-small imagers,
existing methods have achieved image quality far worse than bulky refractive
alternatives, fundamentally limited by aberrations at large apertures and low
f-numbers. In this work, we close this performance gap by presenting the first
neural nano-optics. We devise a fully differentiable learning method that
learns a metasurface physical structure in conjunction with a novel, neural
feature-based image reconstruction algorithm. Experimentally validating the
proposed method, we achieve an order of magnitude lower reconstruction error.
As such, we present the first high-quality, nano-optic imager that combines the
widest field of view for full-color metasurface operation while simultaneously
achieving the largest demonstrated 0.5 mm, f/2 aperture.
