18 citations · 36 across the 5 of their papers we have counts for
5 papers
Quantum Optical Reinforcement Learning via Spectrum-Resolved Hong-Ou-Mandel Interference
Shaojun Wu, Jiahua Xu, Shan Jin +7
Hong-Ou-Mandel (HOM) interference-based optical neural networks can offer complexity advantages on benchmark learning tasks, but conventional readout compresses the coincidence spe…
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…
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…
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…
A Query-based Quantum Eigensolver
Shan Jin, Shaojun Wu, Guanyu Zhou +4
Solving eigenvalue problems is crucially important for both classical and quantum applications. Many well-known numerical eigensolvers have been developed, including the QR and the…