4 papers · 1 filter
HamQASBench: A Hamiltonian-Informed Diagnostic Benchmark for Evaluating Quantum Architecture Search
Jiayang Niu, Akib Karim, Yan Wang +5
Quantum Architecture Search (QAS) automates the design of parameterized quantum circuits for variational quantum algorithms, yet existing benchmarks organize instances by molecular…
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…
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…