From the 1 of 7 linked papers with an AI index.
7 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…
Deterministic Generation of Arbitrary Fock States via Resonant Subspace Engineering
Shan Jin, Ming Li, Weizhou Cai +10
Deterministic preparation of high-excitation Fock states is a central challenge in bosonic quantum information, with control complexity that generically explodes as the Hilbert spa…
Pareto Front Engineering of Dynamical Sweet Spots in Superconducting Qubits
Zhen Yang, Shan Jin, Yajie Hao +4
Operating superconducting qubits at dynamical sweet spots (DSSs) suppresses decoherence from low-frequency flux noise. A key open question is how long coherence can be extended und…
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…
An effcient variational quantum Korkin-Zolotarev algorithm for solving shortest vector problems
Xiaokai Hou, Guoqing Zhou, Shan Jin +5
Noisy intermediate-scale quantum cryptanalysis focuses on the capability of near-term quantum devices to solve the mathematical problems underlying cryptography, and serves as a co…
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…