Proposes realizing all-optical neural networks via phase-tunable interference, bad-cavity integration, and transient Rabi dynamics in waveguide QED, with simulations showing high accuracy on MNIST and object recognition.
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4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
An FPGA-based neural-network decoder achieves 550 ns deterministic closed-loop latency for real-time distance-3 surface code error correction on a superconducting processor, matching offline decoding performance.
Physics-informed quantum neural networks trained on noisy measurements can construct nontrivial decision boundaries to classify quantum states via order parameters and are suited for NISQ hardware due to links with Markovian open many-body systems.
A network of optomechanical oscillators is modeled as a platform for neuromorphic computing, with a demonstration that five nodes in all-to-all coupling can implement an XOR gate.
citing papers explorer
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Optical Neural Networks from Coherent Transient Dynamics in Waveguide QED
Proposes realizing all-optical neural networks via phase-tunable interference, bad-cavity integration, and transient Rabi dynamics in waveguide QED, with simulations showing high accuracy on MNIST and object recognition.
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Real-time Surface-Code Error Correction Using an FPGA-based Neural-Network Decoder
An FPGA-based neural-network decoder achieves 550 ns deterministic closed-loop latency for real-time distance-3 surface code error correction on a superconducting processor, matching offline decoding performance.
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Getting large-scale quantum neural networks ready for quantum hardware
Physics-informed quantum neural networks trained on noisy measurements can construct nontrivial decision boundaries to classify quantum states via order parameters and are suited for NISQ hardware due to links with Markovian open many-body systems.
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Neuromorphic computing with optomechanical oscillators
A network of optomechanical oscillators is modeled as a platform for neuromorphic computing, with a demonstration that five nodes in all-to-all coupling can implement an XOR gate.