Speaker
Domenico Pomarico
(INFN Sezione di Bari)
Description
Learning tasks are implemented via mappings of the sampled data set, including both the classical and the quantum framework. The quantum-inspired approach mimics the support vector machine mapping in a high-dimensional feature space, yielded by the qubit encoding. In our application such scheme is framed in the formulation of a least-squares problem for the minimization of the mean squared error cost function, implemented by means of measurements. The ability of quantum algorithms to manage a high number of parameters will characterize their analysis capability for complex systems, like the targeted biomedical framework.
References
https://www.mdpi.com/2227-7390/9/4/410
Primary author
Domenico Pomarico
(INFN Sezione di Bari)
Co-authors
Mr
Albino Biafora
(Dipartimento di Economia e Finanza, Università degli Studi di Bari)
Dr
Alfredo Zito
(Istituto tumori "Giovanni Paolo II" IRCCS)
Dr
Annarita Fanizzi
(Istituto tumori "Giovanni Paolo II" IRCCS)
Dr
Daniele La Forgia
(Istituto tumori "Giovanni Paolo II" IRCCS)
Dr
Maria Irene Pastena
(Istituto tumori "Giovanni Paolo II" IRCCS)
Prof.
Nicola Amoroso
(Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari)
Dr
Pasquale Tamborra
(Istituto tumori "Giovanni Paolo II" IRCCS)
Dr
Raffaella Massafra
(Istituto tumori "Giovanni Paolo II" IRCCS)
Prof.
Roberto Bellotti
(Dipartimento di Fisica, Università degli Studi di Bari)
Ms
Samantha Bove
(Istituto tumori "Giovanni Paolo II" IRCCS)
Prof.
Vito Lorusso
(Istituto tumori "Giovanni Paolo II" IRCCS)
Dr
Vittorio Didonna
(Istituto tumori "Giovanni Paolo II" IRCCS)
