Abstract
AI is employed in practically every field, but the rapid growth of AI workloads is making it impossible to stay up with speed, energy utilisation, and the capacity to add more technologies. GPUs and TPUs are two examples of classic hardware accelerators that have gone beyond Moore's Law, although there are still certain basic physical limits. Quantum computers can speed up some operations a lot, but current systems are still constrained by their size and the fact that they lose coherence. But photonics might be a good platform because it can convey messages quickly, doesn't need a lot of power, and works at room temperature. This study looks at Photonic Quantum Neural Networks (PQNNs) as a new technique to speed up AI tasks by using quantum computing theory and photonic hardware together. We look into the theory underlying quantum neural networks (QNNs), photonic circuits for quantum operations, and how photonic devices might make matrix multiplications faster, introduce nonlinearities, and make quantum entanglement happen. We speak about how to test PQNNs in the real world, how they might help with computing, and challenges like noise, making mistakes, and fixing them. Finally, we talk about how PQNNs could make machine learning and complex AI models easier in the future.
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Reference
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