  Tensor algebra lies at the core of computational science and machine
learning. Due to its high usage, entire libraries exist dedicated to improving
its performance. Conventional tensor algebra performance boosts focus on
algorithmic optimizations, which in turn lead to incremental improvements. In
this paper, we describe a method to accelerate tensor algebra a different way:
by outsourcing operations to an optical microchip. We outline a numerical
programming language developed to perform tensor algebra computations that is
designed to leverage our optical hardware's full potential. We introduce the
language's current grammar and go over the compiler design. We then show a new
way to store sparse rank-n tensors in RAM that outperforms conventional array
storage (used by C++, Java, etc.). This method is more memory-efficient than
Compressed Sparse Fiber (CSF) format and is specifically tuned for our optical
hardware. Finally, we show how the scalar-tensor product, rank-$n$ Kronecker
product, tensor dot product, Khatri-Rao product, face-splitting product, and
vector cross product can be compiled into operations native to our optical
microchip through various tensor decompositions.
