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Benchmarking Quantum Convolutional Neural Networks for Classification and Data Compression Tasks
/ Khoo, Jun Yong (A-STAR, Singapore) ; Gan, Chee Kwan (A-STAR, Singapore) ; Ding, Wenjun (A-STAR, Singapore) ; Carrazza, Stefano (CERN ; Milan U. ; INFN, Milan) ; Ye, Jun (A-STAR, Singapore ; Technol. Innovation Inst., UAE) ; Kong, Jian Feng (A-STAR, Singapore)
Quantum Convolutional Neural Networks (QCNNs) have emerged as promising models for quantum machine learning tasks, including classification and data compression. [...]
arXiv:2411.13468.
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Qibocal: an open-source framework for calibration of self-hosted quantum devices
/ Pasquale, Andrea (Milan U. ; Technol. Innovation Inst., UAE ; INFN, Milan ; INFN, Milan Bicocca) ; Pedicillo, Edoardo (Milan U. ; Technol. Innovation Inst., UAE) ; Cereijo, Juan (Technol. Innovation Inst., UAE) ; Ramos-Calderer, Sergi (Technol. Innovation Inst., UAE ; ICC, Barcelona U.) ; Candido, Alessandro (CERN) ; Palazzo, Gabriele (Technol. Innovation Inst., UAE ; Milan Bicocca U. ; Unlisted, IT) ; Carobene, Rodolfo (Milan Bicocca U. ; INFN, Milan Bicocca) ; Gobbo, Marco (Milan Bicocca U. ; INFN, Milan Bicocca) ; Efthymiou, Stavros (Technol. Innovation Inst., UAE) ; Tan, Yuanzheng Paul (Nanyang Technol. U.) et al.
Calibration of quantum devices is fundamental to successfully deploy quantum algorithms on current available quantum hardware. [...]
TIF-UNIMI-2024-16 ; CERN-TH-2024-160 ; arXiv:2410.00101.
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Double-bracket quantum algorithms for high-fidelity ground state preparation
/ Robbiati, Matteo (CERN ; Milan U.) ; Pedicillo, Edoardo (Milan U. ; Technol. Innovation Inst., UAE) ; Pasquale, Andrea (Milan U. ; Technol. Innovation Inst., UAE) ; Li, Xiaoyue (Nanyang Technol. U.) ; Wright, Andrew (LPHE, Lausanne) ; Farias, Renato M.S. (Technol. Innovation Inst., UAE ; Rio de Janeiro Federal U.) ; Giang, Khanh Uyen (Nanyang Technol. U.) ; Son, Jeongrak (Nanyang Technol. U.) ; Knörzer, Johannes (Zurich, ETH) ; Goh, Siong Thye (A-STAR, Singapore) et al.
Ground state preparation is a key area where quantum computers are expected to prove advantageous. [...]
TIF-UNIMI-2024-6 ; arXiv:2408.03987.
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Strategies for optimizing double-bracket quantum algorithms
/ Xiaoyue, Li (Nanyang Technol. U.) ; Robbiati, Matteo (CERN ; Milan U.) ; Pasquale, Andrea (Milan U. ; Technol. Innovation Inst., UAE ; INFN, Milan) ; Pedicillo, Edoardo (Milan U. ; Technol. Innovation Inst., UAE) ; Wright, Andrew (Ecole Polytechnique, Lausanne) ; Carrazza, Stefano (CERN ; Milan U. ; Technol. Innovation Inst., UAE ; INFN, Milan) ; Gluza, Marek (Nanyang Technol. U.)
Recently double-bracket quantum algorithms have been proposed as a way to compile circuits for approximating eigenstates. [...]
arXiv:2408.07431.
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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.
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An open-source framework for quantum hardware control
/ Pedicillo, Edoardo (Technol. Innovation Inst., UAE ; Milan U.) ; Candido, Alessandro (CERN) ; Efthymiou, Stavros (Technol. Innovation Inst., UAE) ; Sargsyan, Hayk (Technol. Innovation Inst., UAE) ; Tan, Yuanzheng Paul (Nanyang Technol. U.) ; Cereijo, Juan (Technol. Innovation Inst., UAE) ; Khoo, Jun Yong (A-STAR, Singapore) ; Pasquale, Andrea (Technol. Innovation Inst., UAE ; Milan U.) ; Robbiati, Matteo (Milan U. ; CERN) ; Carrazza, Stefano (Technol. Innovation Inst., UAE ; Milan U. ; CERN)
The development of quantum computers needs reliable quantum hardware and tailored software for controlling electronics specific to various quantum platforms. [...]
TIF-UNIMI-2024-10 CERN-TH-2024-126 ; CERN-TH-2024-126 ; arXiv:2407.21737.
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The path to $\hbox {N}^3\hbox {LO}$ parton distributions
/ NNPDF Collaboration
We extend the existing leading (LO), next-to-leading (NLO), and next-to-next-to-leading order (NNLO) NNPDF4.0 sets of parton distribution functions (PDFs) to approximate next-to-next-to-next-to-leading order (aN$^3$LO). We construct an approximation to the N$^3$LO splitting functions that includes all available partial information from both fixed-order computations and from small and large $x$ resummation, and estimate the uncertainty on this approximation by varying the set of basis functions used to construct the approximation. [...]
arXiv:2402.18635; Nikhef-2023-020; TIF-UNIMI-2023-23; Edinburgh 2023/29.-
2024-07-03 - 59 p.
- Published in : Eur. Phys. J. C 84 (2024) 659
Fulltext: 2402.18635 - PDF; Publication - PDF;
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Computing technology innovation for the Next Generation Trigger and particle theory
/ Carrazza, Stefano (speaker) (CERN)
In this talk we motivate and present an overview of novel unconventional computing technologies such as digital and analog quantum platformsin the context of the Next Generation HEP Trigger Proposal (NGT) and particle theoryresearch activities. After a brief introduction of quantum technologies and therespective R&Dactivitiesfor the NGT, we illustrate the major tasks and challenges for the implementation and performance benchmark ofefficient classical simulation algorithmsfor quantum qubit systems on hardware accelerators (multi-GPU,and FPGAs) andtheoretical foundations for quantum machine learningmodels inspired by the NGT applications.Finally, we conclude by summarizing the potential synergieswith other research activities associated tothe NGT and carried out in the TH Department..
2024 - 4512.
Theory Colloquia
External link: Event details
In : Computing technology innovation for the Next Generation Trigger and particle theory
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Characterization of a Transmon Qubit in a 3D Cavity for Quantum Machine Learning and Photon Counting
/ D'Elia, Alessandro (Frascati) ; Alfakes, Boulos (Unlisted) ; Alkhazaleh, Anas (Unlisted) ; Banchi, Leonardo (Florence U. ; INFN, Florence) ; Beretta, Matteo (Frascati) ; Carrazza, Stefano (Unlisted ; INFN, Milan ; Milan U. ; Padua U. ; INFN, Padua ; CERN) ; Chiarello, Fabio (Frascati ; IFN, Rome) ; Di Gioacchino, Daniele (Frascati) ; Giachero, Andrea (Milan Bicocca U. ; INFN, Milan Bicocca) ; Henrich, Felix (Heidelberg U.) et al.
In this paper we report the use of superconducting transmon qubit in a 3D cavity for quantum machine learning and photon counting applications. We first describe the realization and characterization of a transmon qubit coupled to a 3D resonator, providing a detailed description of the simulation framework and of the experimental measurement of important parameters, like the dispersive shift and the qubit anharmonicity. [...]
arXiv:2402.04322; TIF-UNIMI-2024-2.-
2024-02-11 - 21 p.
- Published in : Appl. Sciences 14 (2024) 1478
Fulltext: Publication - PDF; 2402.04322 - PDF;
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