  Neuromorphic (brain-inspired) photonics leverages photonic chips to
accelerate artificial intelligence, offering high-speed and energy efficient
solutions in RF communication, tensor processing, and data classification.
However, the limited physical size of integrated photonic hardware constrains
network complexity and computational capacity. In light of recent advances in
photonic quantum technology, it is natural to utilize quantum exponential
speedup to scale photonic neural network capabilities. Here we show a
combination of classical network layers with trainable continuous variable
quantum circuits yields hybrid networks with improved trainability and
accuracy. On a classification task, hybrid networks achieve the same
performance when benchmarked against fully classical networks that are twice
the size. When the bit precision of the optimized networks is reduced through
added noise, the hybrid networks still achieve greater accuracy when evaluated
at state of the art bit precision. These hybrid quantum classical networks
demonstrate a unique route to improve computational capacity of integrated
photonic neural networks without increasing the physical network size.
