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This work introduces a computationally efficient unlearning framework that identifies correlated data points in the training set and applies a theoretically derived closed-form parameter update rule, achieving an $82\times$ wall-clock speedup over standard influence function unlearning while preserving model utility wi...
GenQAS is introduced, a tensor network-guided RL framework that combines a fixed matrix product state warm-start with prioritized generative replay and can mitigate sample starvation in quantum architecture search and support more resource efficient circuit discovery.
We introduce Bernstein-Vazirani Networks (BVNs), a non-variational quantum machine learning framework that leverages quantum interference for supervised learning, demonstrated on vision and representation learning tasks. In their standard form, BVNs follow the principle of quantum Fourier sampling: labelled data are pl...
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