arXiv Machine Learning
Generalization of Transformer-Based Neural Quantum States via In-Context Learning
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QiT is a Quantum‑Inspired Transformer designed for visual recognition tasks. It replaces quantum neural network concepts with scalable classical operations: angle‑inspired encoding of image tokens, self‑attention over periodic features approximating quantum fidelity kernels, and gated multiplicative emulation of variational circuit interactions. The model achieves competitive performance on image‑classification benchmarks, matching a classical Transformer while avoiding the high runtime costs of simulated quantum models.
arXiv Machine Learning
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Evaluating Hybrid Quantum-Classical Models for Reduced-Order Brain Deformation Dynamics
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arXiv Machine Learning
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Intrinsic preservation of plasticity in continual quantum learning
arXiv:2511. 17228v2 Announce Type: replace-cross Abstract: Artificial intelligence in dynamic, real-world environments requires the capacity for continual learning.
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Hybrid Quantum Neural Networks: Theory, Implementations, and Applications
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