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Precise Quantum Angle Generator Designed for Noisy Quantum Devices
/ Rehm, Florian (CERN ; DESY) ; Vallecorsa, Sofia (RWTH Aachen U.) ; Borras, Kerstin (DESY) ; Krücker, Dirk (RWTH Aachen U.) ; Grossi, Michele (RWTH Aachen U.) ; Varo, Valle (RWTH Aachen U.)
The Quantum Angle Generator (QAG) is a cutting-edge quantum machine learning model designed to generate precise images on current Noise Intermediate Scale Quantum devices. It utilizes variational quantum circuits and incorporates the MERA-upsampling architecture, achieving exceptional accuracy. [...]
2024 - 8 p.
- Published in : EPJ Web Conf. 295 (2024) 12006
Fulltext: PDF;
In : 26th International Conference on Computing in High Energy & Nuclear Physics, Norfolk, Virginia, Us, 8 - 12 May 2023, pp.12006
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Reduced Precision Research of a GAN Image Generation Use-case
/ Rehm, Florian (CERN ; RWTH, Aachen) ; Saletore, Vikram (Intel, Santa Clara) ; Vallecorsa, Sofia (CERN) ; Borras, Kerstin (RWTH, Aachen ; DESY) ; Krücker, Dirk (DESY)
In this research a deep convolutional Generative Adversarial Network (GAN) model is post-training quantized to a reduced precision arithmetic for a complex High Energy Physics (HEP) use-case. This research is motivated by the aim to decrease the necessary model size and computing time for reducing the required hardware resources for future Large Hadron Collider (LHC) detector simulations at CERN. [...]
2023
In : 10th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2021), 4 - 6 Feb 2021, Vienna, Austria, pp.3–22
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Benchmark of Generative Adversarial Networks for Fast HEP Calorimeter Simulations
/ Rehm, Florian (CERN ; RWTH Aachen U.) ; Vallecorsa, Sofia (CERN) ; Borras, Kerstin (RWTH Aachen U. ; DESY) ; Krücker, Dirk (DESY)
Highly precise simulations of elementary particles interaction and processes are fundamental to
accurately reproduce and interpret the experimental results in High Energy Physics (HEP) detectors
and to correctly reconstruct the particle flows. Today, detector simulations typically rely on Monte
Carlo-based methods which are extremely demanding in terms of computing resources. [...]
2021 - 6 p.
- Published in : (2021) , pp. 310-315
Fulltext: PDF;
In : 9th International Conference on Distributed Computing and Grid Technologies in Science and Education (GRID 2021), Dubna, Russian Federation, 5 - 9 Jul 2021, pp.310-315
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Impact of quantum noise on the training of quantum Generative Adversarial Networks
/ Borras, Kerstin (DESY, Zeuthen ; RWTH Aachen U.) ; Chang, Su Yeon (CERN ; Ecole Polytechnique, Lausanne) ; Funcke, Lena (MIT, Cambridge, CTP ; IAIFI, Cambridge) ; Grossi, Michele (CERN) ; Hartung, Tobias (Cyprus Inst. ; Bath U.) ; Jansen, Karl (DESY, Zeuthen) ; Kruecker, Dirk (DESY, Zeuthen) ; Kühn, Stefan (Cyprus Inst.) ; Rehm, Florian (CERN ; RWTH Aachen U.) ; Tüysüz, Cenk (DESY, Zeuthen ; Humboldt U., Berlin) et al.
Current noisy intermediate-scale quantum devices suffer from various sources of intrinsic quantum noise. Overcoming the effects of noise is a major challenge, for which different error mitigation and error correction techniques have been proposed. [...]
arXiv:2203.01007; MIT-CTP/5400.-
2023 - 6 p.
- Published in : J. Phys.: Conf. Ser.
Fulltext: 2203.01007 - PDF; document - PDF;
In : 20th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2021), Daejeon, Korea, 29 Nov - 3 Dec 2021, pp.012093
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Higgs analysis with quantum classifiers
/ Belis, Vasileios (ETH, Zurich (main)) ; González-Castillo, Samuel (Oviedo U.) ; Reissel, Christina (ETH, Zurich (main)) ; Vallecorsa, Sofia (CERN) ; Combarro, Elías F. (Oviedo U.) ; Dissertori, Günther (ETH, Zurich (main)) ; Reiter, Florentin (Zurich, ETH-CSCS/SCSC)
We have developed two quantum classifier models for the $t\bar{t}H(b\bar{b})$ classification problem, both of which fall into the category of hybrid quantum-classical algorithms for Noisy Intermediate Scale Quantum devices (NISQ). Our results, along with other studies, serve as a proof of concept that Quantum Machine Learning (QML) methods can have similar or better performance, in specific cases of low number of training samples, with respect to conventional ML methods even with a limited number of qubits available in current hardware. [...]
arXiv:2104.07692.-
2021 - 12 p.
- Published in : EPJ Web Conf. 251 (2021) 03070
Fulltext: 2104.07692 - PDF; document - PDF;
In : 25th International Conference on Computing in High-Energy and Nuclear Physics (CHEP), Online, Online, 17 - 21 May 2021, pp.03070
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