10 papers
Arbitrary control over multimode wave propagation for machine learning
Tatsuhiro Onodera, Martin M. Stein, Benjamin A. Ash +10
Controlled multimode wave propagation can enable more space-efficient photonic processors than architectures based on discrete components connected by single-mode waveguides. Inste…
Physical Foundation Models: Fixed hardware implementations of large-scale neural networks
Logan G Wright, Tianyu Wang, Tatsuhiro Onodera +1
Foundation models are deep neural networks (such as GPT-5, Gemini~3, and Opus~4) trained on large datasets that can perform diverse downstream tasks -- text and code generation, qu…
TRON: Trainable, architecture-reconfigurable random optical neural networks
Ziao Wang, Fei Xia, Logan G. Wright +5
Deep learning has triggered explosive growth in the demand for specialized hardware processors, thus motivating the development of scalable and reconfigurable computing substrates.…
Quantum computational displacement sensing
Sridhar Prabhu, Saeed A. Khan, Xingrui Song +7
Quantum computational sensing (QCS) combines quantum sensing with quantum computing to extract task-relevant information from the physical world. QCS can in principle achieve an ac…
Ultra-low-light computer vision using trained photon correlations
Mandar M. Sohoni, Jérémie Laydevant, Mathieu Ouellet +7
Illumination using correlated photon sources has been established as an approach to allowing high-fidelity images to be reconstructed from noisy camera frames by taking advantage o…
Machine vision with small numbers of detected photons per inference
Shi-Yuan Ma, Jérémie Laydevant, Mandar M. Sohoni +3
Machine vision, including object recognition and image reconstruction, is a central technology in many consumer devices and scientific instruments. The design of machine-vision sys…