6 papers
Hybrid Action Reinforcement Learning for Quantum Architecture Search
Jiayang Niu, Yan Wang, Jie Li +4
Reinforcement learning-based Quantum Architecture Search (QAS) offers a promising avenue for automating the design of variational quantum circuits, but existing methods typically d…
Rehearsal-free and Task-free Online Continual Learning With Contrastive Prompt
Aopeng Wang, Ke Deng, Yongli Ren +1
The main challenge of continual learning is \textit{catastrophic forgetting}. Because of processing data in one pass, online continual learning (OCL) is one of the most difficult c…
Quantum Semi-Random Forests for Qubit-Efficient Recommender Systems
Azadeh Alavi, Fatemeh Kouchmeshki, Abdolrahman Alavi +2
Modern recommenders describe each item with hundreds of sparse semantic tags, yet most quantum pipelines still map one qubit per tag, demanding well beyond one hundred qubits, far…
Estimating Quantum Execution Requirements for Feature Selection in Recommender Systems Using Extreme Value Theory
Jiayang Niu, Qihan Zou, Jie Li +3
Recent advances in quantum computing have significantly accelerated research into quantum-assisted information retrieval and recommender systems, particularly in solving feature se…
Metamorphic Evaluation of ChatGPT as a Recommender System
Madhurima Khirbat, Yongli Ren, Pablo Castells +1
With the rise of Large Language Models (LLMs) such as ChatGPT, researchers have been working on how to utilize the LLMs for better recommendations. However, although LLMs exhibit b…
Performance-Driven QUBO for Recommender Systems on Quantum Annealers
Jiayang Niu, Jie Li, Ke Deng +3
Quantum annealers offer a promising hardware platform for solving combinatorial optimization problems, especially those formulated as Quadratic Unconstrained Binary Optimization (Q…