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CERN Document Server Sök i 2 journaler efter:  Sökningen tog 0.80 sekunder. 
1.
Quantum noise modeling through Reinforcement Learning / Bordoni, Simone (Rome U. ; Technol. Innovation Inst., UAE ; INFN, Rome) ; Papaluca, Andrea (Technol. Innovation Inst., UAE ; Australian Natl. U., Canberra) ; Buttarini, Piergiorgio (Rome U. ; Technol. Innovation Inst., UAE) ; Sopena, Alejandro (Technol. Innovation Inst., UAE ; Madrid, IFT) ; Giagu, Stefano (U. Rome La Sapienza (main) ; INFN, Rome) ; Carrazza, Stefano (Technol. Innovation Inst., UAE ; CERN ; Milan U. ; INFN, Milan)
In the current era of quantum computing, robust and efficient tools are essential to bridge the gap between simulations and quantum hardware execution. [...]
TIF-UNIMI-2024-9 ; arXiv:2408.01506.
- 15.
Fulltext
2.
Real-time error mitigation for variational optimization on quantum hardware / Robbiati, Matteo (Milan U. ; INFN, Milan ; CERN) ; Sopena, Alejandro (Madrid, IFT) ; Papaluca, Andrea (Australian Natl. U., Canberra ; Technol. Innovation Inst., UAE) ; Carrazza, Stefano (Milan U. ; INFN, Milan ; CERN ; Technol. Innovation Inst., UAE)
In this work we put forward the inclusion of error mitigation routines in the process of training Variational Quantum Circuit (VQC) models. [...]
arXiv:2311.05680 ; TIF-UNIMI-2023-10 ; CERN-TH-2023-207.
- 12 p.
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