*NOTE: A NOSE-MOUTH-MASK IS MANDATORY!
The last 10 years have seen an accelerating increase in the interest towards quantum technologies, with monumental research efforts devoted to the realms of quantum sensing, simulation and computation. However, such efforts are often frustrated by the important role played by noise, at a time when the scale of quantum devices does not satisfy the requirements for full error correction mechanisms.
This talk will overview several methods, especially based on machine learning protocols, to characterise non-ideal quantum (photonic) devices, as well as mitigate the effects of noise. Applications of said methods will be discussed, along with their experimental implementation, to achieve robust and efficient quantum phase estimation, extend state-of-art variational algorithms, and finally automate the characterisation of untrusted quantum devices, leveraging upon the principles of quantum simulation.
