Quantum computing is seen as the ultimate solution for solving difficult optimization problems such as NP-hard problems with constrained combinatorial optimization. However, there is no line of sight as to when these systems will be available. Recently, in-depth analysis and benchmarking of quantum computing has spurred the creation of new innovative algorithmic and hardware techniques. These help side-step some of the most difficult technical hurdles facing quantum computing but at the same time offer notable improvements compared to standard digital computing methods. Of particular interest are quantum inspired solvers that are classical in nature, but emulate interacting dynamical systems such has magnetic spins. One area I am interested in researching are photonic implementations of Hopfield Neural Networks (HNNs). They are classical Ising machines with quadratic couplings between magnetic spins. I have some preliminary un-published work on optical Ising machines that show significant energy benefits on the III-V/Si platform. My goal would be to implement the dense coupling weights and the dot product operations with fully interconnected optical memristors/charge-trap memory on a heterogeneous III-V/Si platform with a mix of electronics for non-linearities. We are currently involved with the electronic instantiation of this under the DARPA QUICC program. I seek to demonstrate the optical counterpart may lead to orders of magnitude improvement in speed to solution and energy-efficiency. I believe the scaling of optics in quantum inspired computing systems can pave the way for important advances in cryptography, supply chain/logistics optimization, vehicle routing, social graphs, to name a few.