Noisy intermediate-scale quantum algorithms

K Bharti, A Cervera-Lierta, TH Kyaw, T Haug… - Reviews of Modern …, 2022 - APS
A universal fault-tolerant quantum computer that can efficiently solve problems such as
integer factorization and unstructured database search requires millions of qubits with low …

Quantum walk and its application domains: A systematic review

K Kadian, S Garhwal, A Kumar - Computer Science Review, 2021 - Elsevier
Quantum random walk is the quantum counterpart of a classical random walk. The classical
random walk concept has long been used as a computational framework for designing …

Efficient on-chip training of optical neural networks using genetic algorithm

H Zhang, J Thompson, M Gu, XD Jiang, H Cai… - Acs …, 2021 - ACS Publications
Recent advances in silicon photonic chips have made huge progress in optical computing
owing to their flexibility in the reconfiguration of various tasks. Its deployment of neural …

[HTML][HTML] Experimental quantum Hamiltonian learning

J Wang, S Paesani, R Santagati, S Knauer, AA Gentile… - Nature Physics, 2017 - nature.com
The efficient characterization of quantum systems,,, the verification of the operations of
quantum devices,, and the validation of underpinning physical models,,, are central …

Using an imperfect photonic network to implement random unitaries

R Burgwal, WR Clements, DH Smith, JC Gates… - Optics …, 2017 - opg.optica.org
We numerically investigate the implementation of Haar-random unitarity transformations and
Fourier transformations in photonic devices consisting of beam splitters and phase shifters …

Learning models of quantum systems from experiments

AA Gentile, B Flynn, S Knauer, N Wiebe, S Paesani… - Nature Physics, 2021 - nature.com
As Hamiltonian models underpin the study and analysis of physical and chemical
processes, it is crucial that they are faithful to the system they represent. However …

[HTML][HTML] Basic protocols in quantum reinforcement learning with superconducting circuits

L Lamata - Scientific reports, 2017 - nature.com
Superconducting circuit technologies have recently achieved quantum protocols involving
closed feedback loops. Quantum artificial intelligence and quantum machine learning are …

Calibration of multiparameter sensors via machine learning at the single-photon level

V Cimini, E Polino, M Valeri, I Gianani, N Spagnolo… - Physical Review …, 2021 - APS
Calibration of sensors is a fundamental step in validating their operation. This can be a
demanding task, as it relies on acquiring detailed modeling of the device, which can be …

Quantum autoencoders via quantum adders with genetic algorithms

L Lamata, U Alvarez-Rodriguez… - Quantum Science …, 2018 - iopscience.iop.org
The quantum autoencoder is a recent paradigm in the field of quantum machine learning,
which may enable an enhanced use of resources in quantum technologies. To this end …

Calibration of quantum sensors by neural networks

V Cimini, I Gianani, N Spagnolo, F Leccese… - Physical Review Letters, 2019 - APS
Introducing quantum sensors as a solution to real world problems demands reliability and
controllability outside of laboratory conditions. Producers and operators ought to be …