From the 1 of 4 linked papers with an AI index.
4 papers
Quantum Optical Reinforcement Learning via Spectrum-Resolved Hong-Ou-Mandel Interference
Shaojun Wu, Jiahua Xu, Shan Jin +7
The paper presents a spectrum‑resolved Hong‑Ou‑Mandel (SR‑HOM) optical neural network that uses photon spectral modes as trainable resources to build a compact actor‑critic reinfor…
Enhancing the reachability of variational quantum algorithms via input-state design
Shaojun Wu, Shan Jin, Abolfazl Bayat +1
Variational quantum algorithms (VQAs) face an inherent trade-off between expressivity and trainability: deeper circuits can represent richer states but suffer from noise accumulati…
Quantum reinforcement learning in continuous action space
Shaojun Wu, Shan Jin, Dingding Wen +2
Quantum reinforcement learning (QRL) is a promising paradigm for near-term quantum devices. While existing QRL methods have shown success in discrete action spaces, extending these…
Fixed-point quantum continuous search algorithm with optimal query complexity
Shan Jin, Yuhan Huang, Shaojun Wu +4
Continuous search problems (CSPs), which involve finding solutions within a continuous domain, frequently arise in fields such as optimization, physics, and engineering. Unlike dis…