9 citations · 9 across the 6 of their papers we have counts for
8 papers
Correlation-Guided Fast Machine Unlearning via Hessian Analysis
Ayushi Thakur, Ruchir Gupta, Amit Kumar Jaiswal +1
The increasing adoption of machine learning in network and distributed security systems has created an urgent need for mechanisms that can selectively and efficiently remove the in…
Generative Replay Mitigates Sample Starvation in Quantum Architecture Search
Akash Kundu, Amit Kumar Jaiswal, Sebastian Feld +1
Reinforcement learning (RL) can automate quantum architecture search, but its scalability is limited when useful circuit trajectories become rare in the rapidly expanding search sp…
Bernstein-Vazirani Networks: Quantum Machine Learning by Interference
Natacha Kuete Meli, Tolga Birdal, Prayag Tiwari +2
We introduce Bernstein-Vazirani Networks (BVNs), a non-variational quantum machine learning framework that leverages quantum interference for supervised learning, demonstrated on v…
Complementary Matrix-Gated QKAN Fast-Weight Programmers for Quantum Dynamics Forecasting
Kuo-Chung Peng, Samuel Yen-Chi Chen, Jiun-Cheng Jiang +14
Sequence models must decide what to write into memory and what to retain. In quantum and quantum-inspired sequence learning, nonlinear recurrent updates often require repeated circ…
Gated QKAN-FWP: Scalable Quantum-inspired Sequence Learning
Kuo-Chung Peng, Samuel Yen-Chi Chen, Jiun-Cheng Jiang +16
Fast Weight Programmers (FWPs) encode temporal dependencies through dynamically updated parameters rather than recurrent hidden states. Quantum FWPs (QFWPs) extend this idea with v…
Neural Network Optimization Reimagined: Decoupled Techniques for Scratch and Fine-Tuning
Xin Ning, Qiankun Li, Xiaolong Huang +5
With the accumulation of resources in the era of big data and the rise of pre-trained models in deep learning, optimizing neural networks for various tasks often involves different…
Quantum Long Short-term Memory with Differentiable Architecture Search
Samuel Yen-Chi Chen, Prayag Tiwari
Recent advances in quantum computing and machine learning have given rise to quantum machine learning (QML), with growing interest in learning from sequential data. Quantum recurre…
Federated Quantum Kernel-Based Long Short-term Memory for Human Activity Recognition
Yu-Chao Hsu, Jiun-Cheng Jiang, Chun-Hua Lin +5
In this work, we introduce the Federated Quantum Kernel-Based Long Short-term Memory (Fed-QK-LSTM) framework, integrating the quantum kernel methods and Long Short-term Memory into…