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Guo-Ping Guo

47 papers hereh-index 14640 citations65 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author1
  • last author43

Across the 44 of 47 papers where every author was matched, so the position is known.

fields
  • quant-ph44
  • cond-mat.mes-hall1
  • cs.RO1
  • physics.comp-ph1
same name
  • Guo-Ping Guo — 32 papers, h 13
  • Guo-Ping Guo — 5 papers, h 2
  • Guo-Ping Guo — 4 papers, h 2
  • Guo-Ping Guo — 2 papers
  • Guo-Ping Guo — 1 paper
  • Guo-Ping Guo — 1 paper

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20182026
most citedQuantum Neural Network States: A Brief Review of Methods and Applications

101 citations · 387 across the 47 of their papers we have counts for

collaborators
Showing 2026Show all

4 papers · 1 filter

quant-ph2026

Poisson-Compiled Quantum Singular Value Transformation for Power-Exponential Dissipation

Chao Wang, Xi-Ning Zhuang, Menghan Dou +2

We study quantum implementations of the contraction exp(−THα) for H=H†⪰0 and α>0. Poisson summation provides an exact target--alias--tail decomposition whose…

quant-ph2026

Simulation of Lindbladian dynamics via adaptive variational quantum trajectory compression

Huan-Yu Liu, Cheng Xue, Yun-Jie Wang +5

Quantum simulation of open quantum systems in the noisy intermediate-scale quantum (NISQ) era is hindered by the non-unitary nature of dissipative dynamics and the limited quantum…

quant-ph2026

A Unified Poisson Summation Framework for Generalized Quantum Matrix Transformations

Chao Wang, Xi-Ning Zhuang, Menghan Dou +2

We present a unified algorithmic framework for quantum simulation of non-unitary dynamics and matrix functions, governed by the principle of spectral aliasing derived from the Pois…

quant-ph2026

Quantum Deep Learning: A Comprehensive Review

Yanjun Ji, Zhao-Yun Chen, Marco Roth +10

Quantum deep learning (QDL) explores the use of both quantum and quantum-inspired resources to determine when deep learning's core capabilities, such as expressivity, generalizatio…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.