Compiling deep learning tasks onto (quantum-) optical systems

The hardware limitations of conventional electronics in deep neural network (DNN) applications have spurred exploration into alternative architectures, including optical accelerators. This work investigates the scalability and performance metrics—such as throughput, energy consumption, and latency—of various optical and opto-electronic architectures, with a focus on recently developed hardware error correction techniques, in-situ training methods, initial field trials, as well as extensions into DNN-based inference on quantum signals with reversible, quantum-coherent resources.