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Feiyang Liu

6 papers hereh-index 547 citations12 works total

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

author position
  • first author2
  • middle author4

Across the 6 of 6 papers where every author was matched, so the position is known.

fields
  • quant-ph5
  • eess.SP1
same name
  • Feiyang Liu — 6 papers, h 5
  • Feiyang Liu — 1 paper
  • Feiyang Liu — 1 paper, h 1
  • Feiyang Liu — 1 paper, h 0
  • Feiyang Liu — 1 paper, h 2
  • Feiyang Liu — 1 paper, h 2

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
20182025
most citedProvable superior accuracy in machine learned quantum models

5 citations · 5 across the 2 of their papers we have counts for

collaborators
Showing quant-phShow all

4 papers · 1 filter

quant-ph2025

Photonic implementation of quantum hidden subgroup database compression

Qianyi Wang, Feiyang Liu, Teng Hu +6

We experimentally demonstrate quantum data compression exploiting hidden subgroup symmetries using a photonic quantum processor. Classical databases containing generalized periodic…

quant-ph2021★ 5 cited

Provable superior accuracy in machine learned quantum models

Chengran Yang, Andrew Garner, Feiyang Liu +5

In modelling complex processes, the potential past data that influence future expectations are immense. Models that track all this data are not only computationally wasteful but al…

quant-ph2018

Quantum advantage in training binary neural networks

Yidong Liao, Daniel Ebler, Feiyang Liu +1

The performance of a neural network for a given task is largely determined by the initial calibration of the network parameters. Yet, it has been shown that the calibration, also r…

quant-ph2018

Learning Simon's quantum algorithm

Kwok Ho Wan, Feiyang Liu, Oscar Dahlsten +1

We consider whether trainable quantum unitaries can be used to discover quantum speed-ups for classical problems. Using methods recently developed for training quantum neural nets,…

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