10 citations · 10 across the 4 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…
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…
MindSpore Quantum: A User-Friendly, High-Performance, and AI-Compatible Quantum Computing Framework
Xusheng Xu, Jiangyu Cui, Zidong Cui +36
We introduce MindSpore Quantum, a pioneering hybrid quantum-classical framework with a primary focus on the design and implementation of noisy intermediate-scale quantum (NISQ) alg…
Maximising Quantum-Computing Expressive Power through Randomised Circuits
Yingli Yang, Zongkang Zhang, Anbang Wang +3
In the noisy intermediate-scale quantum era, variational quantum algorithms (VQAs) have emerged as a promising avenue to obtain quantum advantage. However, the success of VQAs depe…