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Jing Wang

11 papers hereh-index 577 citations12 works total

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

author position
  • last author11

Across the 11 of 11 papers where every author was matched, so the position is known.

fields
  • quant-ph6
  • cond-mat.mtrl-sci2
  • cond-mat.mes-hall1
  • cond-mat.str-el1
  • cond-mat.supr-con1
same name
  • Jing Wang — 235 papers
  • Jing Wang — 77 papers, h 31
  • Jing Wang — 49 papers, h 32
  • Jing Wang — 37 papers, h 0
  • Jing Wang — 27 papers
  • Jing Wang — 24 papers, h 23

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
20232026
most citedIntrinsic Quantum Mpemba Effect in Markovian Systems and Quantum Circuits

20 citations · 60 across the 11 of their papers we have counts for

collaborators
Showing 2024Show all

4 papers · 1 filter

quant-ph2024★ 20 cited

Intrinsic Quantum Mpemba Effect in Markovian Systems and Quantum Circuits

Dongheng Qian, Huan Wang, Jing Wang

The quantum Mpemba effect (QME) describes the counterintuitive phenomenon in which a system farther from equilibrium reaches steady state faster than one closer to equilibrium. How…

quant-ph2024★ 3 cited

Coherent Information Phase Transition in a Noisy Quantum Circuit

Dongheng Qian, Jing Wang

Coherent information quantifies the transmittable quantum information through a channel and is directly linked to the channel's quantum capacity. In a monitored quantum circuit, re…

quant-ph2024★ 8 cited

Protect Measurement-Induced Phase Transition from Noise

Dongheng Qian, Jing Wang

Scrambling dynamics induced by random unitary gates can protect information from low-rate measurements, which underpins the phenomenon known as the measurement-induced phase transi…

cond-mat.mtrl-sci2024★ 1 cited

Predicting Many Crystal Properties via an Adaptive Transformer-based Framework

Haosheng Xu, Dongheng Qian, Jing Wang

Machine learning has revolutionized many fields, including materials science. However, predicting properties of crystalline materials using machine learning faces challenges in inp…

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