5 citations · 16 across the 7 of their papers we have counts for
7 papers
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…
Information compression via hidden subgroup quantum autoencoders
Feiyang Liu, Kaiming Bian, Fei Meng +2
We design a quantum method for classical information compression that exploits the hidden subgroup quantum algorithm. We consider sequence data in a database with a priori unknown…
Machine learning Hadron Spectral Functions in Lattice QCD
Shi-Yang Chen, Heng-Tong Ding, Fei-Yi Liu +2
Hadron spectral functions carry all the information of hadrons and are encoded in the Euclidean two-point correlation functions. The extraction of hadron spectral functions from th…
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…
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…
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,…
On intelligent energy harvesting
Feiyang Liu, Yulong Zhang, Oscar Dahlsten +1
We probe the potential for intelligent intervention to enhance the power output of energy harvesters. We investigate general principles and a case study: a bi-resonant piezo electr…