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Showing 1–10 of 10 results
Advanced filters: Author: Darius Bunandar Clear advanced filters

  • The quantum nature of light has been harnessed in a photonic chip to perform machine-learning tasks. For specifically designed problems, the approach outperforms established classical methods.

    • Nicholas Harris
    • Darius Bunandar
    News & Views
    Nature Photonics
    Volume: 19, P: 911-912

  • A large-scale, low-loss and phase-stable programmable nanophotonic processor is fabricated to explore quantum transport phenomena. The signature of environment-assisted quantum transport in discrete-time systems is observed for the first time.

    • Nicholas C. Harris
    • Gregory R. Steinbrecher
    • Dirk Englund
    Research
    Nature Photonics
    Volume: 11, P: 447-452

  • A photonic processor capable of running advanced artificial intelligence models with near-electronic precision is introduced, marking a substantial step towards post-transistor computing technologies.

    • Sufi R. Ahmed
    • Reza Baghdadi
    • Nicholas C. Harris
    Research
    Nature
    Volume: 640, P: 368-374

  • Researchers experimentally demonstrate a fully integrated coherent optical neural network. The system, with six neurons and three layers, operates with a latency of 410 ps.

    • Saumil Bandyopadhyay
    • Alexander Sludds
    • Dirk Englund
    Research
    Nature Photonics
    Volume: 18, P: 1335-1343

  • Demirkiran et al. explore the use of the residue number system to overcome precision challenges in analog computing, paving the way for unleashing its full potential as next-generation AI hardware for advanced tasks.

    • Cansu Demirkiran
    • Lakshmi Nair
    • Ajay Joshi
    ResearchOpen Access
    Nature Communications
    Volume: 15, P: 1-14

  • The variational quantum unsampling protocol provides a way to realize verification and inference of near-term quantum circuit outputs. This protocol is then experimentally verified on a quantum photonic processor.

    • Jacques Carolan
    • Masoud Mohseni
    • Dirk Englund
    Research
    Nature Physics
    Volume: 16, P: 322-327

  • The growing computational demands of AI are significantly increasing carbon emissions from the ICT sector, while traditional CMOS-based computing faces scaling and sustainability limits. This perspective explores photonic computing as a promising, energy-efficient alternative for enabling carbon-sustainable next-generation AI hardware.

    • Farbin Fayza
    • Cansu Demirkiran
    • Ajay Joshi
    ReviewsOpen Access
    Communications Physics
    Volume: 8, P: 1-8