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researcher

Jun Wang

4 papers hereh-index 10743 citations47 works total

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

author position
  • first author1
  • middle author2
  • last author1

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • quant-ph2
  • cond-mat.mes-hall1
  • cond-mat.stat-mech1
same name
  • Jun Wang — 64 papers, h 35
  • Jun Wang — 55 papers, h 47
  • Jun Wang — 27 papers, h 12
  • Jun Wang — 20 papers, h 4
  • Jun Wang — 18 papers, h 11
  • Jun Wang — 17 papers, h 11

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

most citedUnsupervised Generative Modeling Using Matrix Product States

261 citations · 295 across the 4 of their papers we have counts for

collaborators

4 papers

quant-ph2022

Self-consistent Gradient-like Eigen Decomposition in Solving Schrödinger Equations

Xihan Li, Xiang Chen, Rasul Tutunov +3

The Schrödinger equation is at the heart of modern quantum mechanics. Since exact solutions of the ground state are typically intractable, standard approaches approximate Schröding…

cond-mat.mes-hall2022★ 7 cited

AD-NEGF: An End-to-End Differentiable Quantum Transport Simulator for Sensitivity Analysis and Inverse Problems

Yingzhanghao Zhou, Xiang Chen, Peng Zhang +3

Since proposed in the 70s, the Non-Equilibrium Green Function (NEGF) method has been recognized as a standard approach to quantum transport simulations. Although it achieves superi…

quant-ph2017★ 27 cited

Scalable Quantum Tomography with Fidelity Estimation

Jun Wang, Zhao-Yu Han, Song-Bo Wang +4

We propose a quantum tomography scheme for pure qudit systems which adopts random base measurements and generative learning methods, along with a built-in fidelity estimation appro…

cond-mat.stat-mech2017★ 261 cited

Unsupervised Generative Modeling Using Matrix Product States

Zhao-Yu Han, Jun Wang, Heng Fan +2

Generative modeling, which learns joint probability distribution from data and generates samples according to it, is an important task in machine learning and artificial intelligen…

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