Compiling deep learning tasks onto (quantum-) optical systems

Dirk R. Englund

doi:10.1117/12.3023224

10 June 2024 Compiling deep learning tasks onto (quantum-) optical systems

Dirk R. Englund

Author Affiliations +

Proceedings Volume PC13028, Quantum Information Science, Sensing, and Computation XVI; PC130280K (2024) https://doi.org/10.1117/12.3023224
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

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.

Conference Presentation

Citation Download Citation

Dirk R. Englund "Compiling deep learning tasks onto (quantum-) optical systems", Proc. SPIE PC13028, Quantum Information Science, Sensing, and Computation XVI, PC130280K (10 June 2024); https://doi.org/10.1117/12.3023224

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