arXiv:2601. 22300v3 Announce Type: replace-cross Abstract: We propose a deep photonic neuromorphic network (PNN) architecture based on phase-change material (PCM) synapses and local optical feedback for online, unsupervised Hebbian learning.
arXiv:2506. 21324v3 Announce Type: replace-cross Abstract: Neuromorphic and quantum computing have recently emerged as promising paradigms for advancing artificial intelligence, each offering complementary strengths.
arXiv:2608. 08317v1 Announce Type: new Abstract: Biological neural systems achieve high efficiency and robustness through compartmentalized architectures.
arXiv:2607. 00286v1 Announce Type: cross Abstract: Oscillatory neural networks (ONNs) have emerged as a promising neuromorphic architecture, leveraging coupled dynamical systems to perform computation and represent information through phase relationships.
arXiv:2604. 02429v2 Announce Type: replace-cross Abstract: Convolutional neural networks (CNNs) have transformed image processing, but the energy consumption and inference latency of electronic based implementations remain fundamental bottlenecks.
arXiv:2605. 22097v2 Announce Type: replace-cross Abstract: Photonic quantum computing is a promising platform for scalable quantum machine learning, but designing effective hybrid architectures remains challenging under hardware and optimization constraints.
arXiv:2608. 06963v1 Announce Type: new Abstract: Biologically plausible learning models aim to explain how neural circuits can implement effective learning under the constraints of real neurons.
arXiv:2608. 02229v1 Announce Type: new Abstract: Classical neural networks frequently produce overconfident predictions on ambiguous or out-of-distribution (OOD) data, a liability that grows with each AI system deployed in safety-critical real-world scenarios.
Photonic quantum machine learning offers a route to trainable physical representations built from phase, interference and measurement. However, its role in scientific machine learning remains largely unexplored.
arXiv:2606. 09756v1 Announce Type: new Abstract: Responses to perturbations are key to understanding physical systems.
arXiv:2606. 18713v1 Announce Type: new Abstract: Photonic quantum machine learning offers a route to trainable physical representations built from phase, interference and measurement.
arXiv:2606. 09117v1 Announce Type: cross Abstract: While Ising machines serve as advanced physical solvers for the Ising model,enabling applications in combinatorial optimization and neural network training,their scalability for large-scale neural networks remains constrained by hardware connectivity limitations and suboptimal training methodologies.
arXiv:2606. 29099v1 Announce Type: cross Abstract: Artificial neural networks (ANN) provide accurate continuous-valued representation, whereas spiking neural networks (SNN) offer event-driven temporal processing, yet both paradigms face limitations when value encoding and timing dynamics must be learned within a single computational structure.