21 citations · 76 across the 14 of their papers we have counts for
14 papers
Near-optimal synthesis of non-Gaussian phase gates via qubit-oscillator Rabi control
Zhen Yang, Shan Jin, Zi-Wen Liu +1
Non-Gaussian gates remain a key bottleneck for universal continuous-variable (CV) quantum computation because the nonlinearities they require are difficult to engineer. To address…
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…
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…
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…
Achieving quantum advantages for image filtering
Zidong Cui, Shan Jin, Akira Sone +1
Image processing is a fascinating field for exploring quantum algorithms. However, achieving quantum speedups turns out to be a significant challenge. In this work, we focus on ima…
A duplication-free quantum neural network for universal approximation
Xiaokai Hou, Guanyu Zhou, Qingyu Li +2
The universality of a quantum neural network refers to its ability to approximate arbitrary functions and is a theoretical guarantee for its effectiveness. A non-universal neural n…
Quantum spectral clustering algorithm for unsupervised learning
Qingyu Li, Yuhan Huang, Shan Jin +2
Clustering is one of the most crucial problems in unsupervised learning, and the well-known -means clustering algorithm has been shown to be implementable on a quantum computer…
A universal duplication-free quantum neural network
Xiaokai Hou, Guanyu Zhou, Qingyu Li +2
Universality of neural networks describes the ability to approximate arbitrary function, and is a key ingredient to keep the method effective. The established models for universal…
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…
Multi-layer quantum search and inclusion of NP into BQP
Shan Jin, Xiaoting Wang, Bo Li
In this work, we present a multi-layer quantum search method that generates an exponential speedup of the standard Grover's algorithm. As direct applications, any NP problems can b…