1 citations · 1 across the 9 of their papers we have counts for
11 papers
FunnelAudit: Responsibility Auditing in Multi-Route Recommender Systems
Jie Li, Dudu Luo, Jiayang Niu +2
Multi-route recommender systems combine retrieval, allocation, fusion, and ranking, making individual inclusions and exclusions difficult to audit. Route overlap can hide effects f…
DreamQAS: Learning a Decision-Useful World Model for VQE-Efficient Quantum Architecture Search
Jiayang Niu, Yan Wang, Jie Li +4
Reinforcement-learning-based quantum architecture search (RL-QAS) repeatedly invokes a variational quantum eigensolver (VQE) after each gate addition even though circuit transition…
Energy Accuracy Is Not Enough: A Structure-Aware Benchmark and Evaluation Protocol for Quantum Architecture Search
Jiayang Niu, Akib Karim, Yan Wang +5
Quantum architecture search for molecular ground-state estimation is commonly evaluated through energy accuracy, which does not describe circuit cost or the physical properties of…
A Givens-exchange ansatz for molecular variational eigensolvers
Azadeh Alavi, Fatemeh Kouchmeshki, Muhammad Usman +4
Molecular ground-state energies help determine conformer rankings, reaction energetics, and electronic effects in computational drug discovery, but accurate calculations become dif…
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…
CRUISE on Quantum Computing for Feature Selection in Recommender Systems
Jiayang Niu, Jie Li, Ke Deng +1
Using Quantum Computers to solve problems in Recommender Systems that classical computers cannot address is a worthwhile research topic. In this paper, we use Quantum Annealers to…