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09:00
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Applications of Tensor Networks (TN) and Quantum Machine Learning (QML) to High-Energy Physics
(until 13:05)
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09:30
|
--- Welcome coffee ---
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|
10:00
|
Welcome and introduction
-
Michelangelo Mangano
(CERN)
|
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10:05
|
Quantum Machine Learning Integration in the High Energy Physics Pipeline
- Dr
Michele Grossi
(CERN)
|
|
11:05
|
TNS and the simulation of Lattice Gauge Theories
-
Mari Carmen Bañuls
|
|
12:05
|
Discussion: Utilizing QML Algorithms within Tensor Networks
|
|
|
09:00
|
Prospects on Tensor Networks and Machine Learning
(until 13:20)
|
|
09:00
|
Tensor network algorithms for HEP quantum simulation
-
Simone Montangero
(Padova University)
|
|
09:45
|
Optimising Tree Tensor Networks for classification on hardware accelerators
-
Alberto Coppi
|
|
10:05
|
Tree Tensor Network implementation on FPGA
-
Lorenzo Borella
(Universita e INFN, Padova (IT))
|
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10:30
|
--- Morning coffee ---
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11:00
|
Provable exponential quantum advantages in learning from physics data
-
Vedran Dunjko
|
|
12:00
|
Discussion: Using Tensor Networks to Enhance QML Algorithms
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