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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳凱風 | zh_TW |
| dc.contributor.advisor | Kai-Feng Chen | en |
| dc.contributor.author | 陳奕安 | zh_TW |
| dc.contributor.author | Yi-An Chen | en |
| dc.date.accessioned | 2025-07-30T16:04:43Z | - |
| dc.date.available | 2025-07-31 | - |
| dc.date.copyright | 2025-07-30 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-07-15 | - |
| dc.identifier.citation | [1] S. Chatrchyan et al. “Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHC”. In: Physics Letters B 716.1 (2012), pp. 30–61. ISSN: 0370-2693. DOI: https://doi.org/10.1016/j.physletb.2012.08.021. URL: https://www.sciencedirect.com/science/article/pii/S0370269312008581.
[2] G. Aad et al. “Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHC”. In: Physics Letters B 716.1 (2012), pp. 1–29. ISSN: 0370-2693. DOI: https://doi.org/10.1016/j.physletb.2012.08.020. URL: https://www.sciencedirect.com/science/article/pii/S037026931200857X. [3] Wikipedia contributors. Standard Model. Accessed: April 6, 2025. 2025. URL: https://en.wikipedia.org/wiki/Standard_Model. [4] Ashish Vaswani et al. Attention Is All You Need. 2023. arXiv: 1706.03762 [cs.CL]. URL: https://arxiv.org/abs/1706.03762. [5] Matthew Feickert and Benjamin Nachman. A Living Review of Machine Learning for Particle Physics. 2021. arXiv: 2102.02770 [hep-ph]. [6] HEP ML Community. A Living Review of Machine Learning for Particle Physics. URL: https://iml-wg.github.io/HEPML-LivingReview/. [7] Alexander Radovic et al. “Machine learning at the energy and intensity frontiers of particle physics”. In: Nature 560.7716 (2018), pp. 41–48. DOI: 10.1038/s41586-018-0361-2. URL: https://doi.org/10.1038/s41586-018-0361-2. [8] Huilin Qu and Loukas Gouskos. “Jet tagging via particle clouds”. In: Phys. Rev. D 101 (5 Mar. 2020), p. 056019. DOI: 10.1103/PhysRevD.101.056019. URL: https://link.aps.org/doi/10.1103/PhysRevD.101.056019. [9] Isaac Henrion et al. “Neural Message Passing for Jet Physics”. In: 2017. URL: https://api.semanticscholar.org/CorpusID:39724044. [10] Eric A. Moreno et al. “JEDI-net: a jet identification algorithm based on interaction networks”. In: The European Physical Journal C 80.1 (2020), p. 58. DOI: 10.1140/epjc/s10052-020-7608-4. URL: https://doi.org/10.1140/epjc/s10052-020-7608-4. [11] Amit Chakraborty, Sung Hak Lim, and Mihoko M. Nojiri. “Interpretable deep learning for two-prong jet classification with jet spectra”. In: Journal of High Energy Physics 2019.7 (2019), p. 135. DOI: 10.1007/JHEP07(2019)135. URL: https://doi.org/10.1007/JHEP07(2019)135. [12] Amit Chakraborty et al. “Neural network-based top tagger with two-point energy correlations and geometry of soft emissions”. In: Journal of High Energy Physics 2020.7 (2020), p. 111. DOI: 10.1007/JHEP07(2020)111. URL: https://doi.org/10.1007/JHEP07(2020)111. [13] Jonathan Shlomi, Peter Battaglia, and Jean-Roch Vlimant. “Graph neural networks in particle physics”. In: Machine Learning: Science and Technology 2.2 (Dec. 2020), p. 021001. DOI: 10.1088/2632-2153/abbf9a. URL: https://dx.doi.org/10.1088/2632-2153/abbf9a. [14] Xiangyang Ju and Benjamin Nachman. “Supervised jet clustering with graph neural networks for Lorentz boosted bosons”. In: Phys. Rev. D 102 (7 Oct. 2020), p. 075014. DOI: 10.1103/PhysRevD.102.075014. URL: https://link.aps.org/doi/10.1103/PhysRevD.102.075014. [15] Frédéric A. Dreyer and Huilin Qu. Jet tagging in the Lund plane with graph networks. 2021. arXiv: 2012.08526 [hep-ph]. URL: https://arxiv.org/abs/2012.08526. [16] Shiqi Gong et al. “An efficient Lorentz equivariant graph neural network for jet tagging”. In: Journal of High Energy Physics 2022.7 (2022), p. 30. DOI: 10.1007/JHEP07(2022)030. URL: https://doi.org/10.1007/JHEP07(2022)030. [17] Fei Ma, Feiyi Liu, and Wei Li. “Jet tagging algorithm of graph network with Haar pooling message passing”. In: Phys. Rev. D 108 (7 Oct. 2023), p. 072007. DOI: 10.1103/PhysRevD.108.072007. URL: https://link.aps.org/doi/10.1103/PhysRevD.108.072007. [18] Farouk Mokhtar, Raghav Kansal, and Javier Duarte. Do graph neural networks learn traditional jet substructure? 2022. arXiv: 2211 . 09912 [hep-ex]. URL: https://arxiv.org/abs/2211.09912. [19] Daniel Murnane. Graph Structure from Point Clouds: Geometric Attention is All You Need. 2023. arXiv: 2307.16662 [cs.LG]. URL: https://arxiv.org/abs/2307.16662. [20] Savannah Thais et al. Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges. 2022. arXiv: 2203.12852 [hep-ex]. [21] Jun Guo et al. “Boosted Higgs boson jet reconstruction via a graph neural network”. In: Phys. Rev. D 103 (11 June 2021), p. 116025. DOI: 10.1103/PhysRevD.103.116025. URL: https://link.aps.org/doi/10.1103/PhysRevD.103.116025. [22] Jacob Biamonte et al. “Quantum machine learning”. In:Nature 549.7671 (2017), pp. 195–202. DOI: 10.1038/nature23474. URL: https://doi.org/10.1038/nature23474. [23] Amine Zeguendry, Zahi Jarir, and Mohamed Quafafou. “Quantum Machine Learning: A Review and Case Studies”. In: Entropy 25.2 (2023). ISSN: 1099-4300. DOI: 10.3390/e25020287. URL: https://www.mdpi.com/1099-4300/25/2/287. [24] David Peral García, Juan Cruz-Benito, and Francisco José García-Peñalvo. Systematic Literature Review: Quantum Machine Learning and its applications. 2022. arXiv: 2201.04093 [quant-ph]. [25] Kyriaki A. Tychola, Theofanis Kalampokas, and George A. Papakostas. “Quantum Machine Learning mdash;An Overview”. In: Electronics 12.11 (2023). ISSN: 2079-9292. DOI: 10.3390/electronics12112379. URL: https://www.mdpi.com/2079-9292/12/11/2379. [26] Maria Schuld and Francesco Petruccione. Machine Learning with Quantum Computers. Jan. 2021. ISBN: 978-3-030-83097-7. DOI: 10.1007/978-3-030-83098-4. [27] Yi-An Chen and Kai-Feng Chen. “Jet discrimination with a quantum complete graph neural network”. In: Phys. Rev. D 111 (1 Jan. 2025), p. 016020. DOI: 10.1103/PhysRevD.111.016020. URL: https://link.aps.org/doi/10.1103/PhysRevD.111.016020. [28] M. Cerezo et al. “Variational quantum algorithms”. In: Nature Reviews Physics 3.9 (2021), pp. 625–644. DOI: 10.1038/s42254-021-00348-9. URL: https://doi.org/10.1038/s42254-021-00348-9. [29] Alberto Peruzzo et al. “A variational eigenvalue solver on a photonic quantum processor”. In: Nature Communications 5.1 (2014), p. 4213. DOI: 10.1038/ncomms5213. URL: https://doi.org/10.1038/ncomms5213. [30] Jarrod R McClean et al. “The theory of variational hybrid quantum-classical algorithms”. In: New Journal of Physics 18.2 (Feb. 2016), p. 023023. DOI: 10.1088/1367-2630/18/2/023023. URL: https://dx.doi.org/10.1088/1367-2630/18/2/023023. [31] J. Altmann et al. “Towards the understanding of heavy quarks hadronization: from leptonic to heavy-ion collisions”. In: The European Physical Journal C 85.1 (2025), p. 16. DOI: 10.1140/epjc/s10052-024-13641-5. URL: https://doi.org/10.1140/epjc/s10052-024-13641-5. [32] CMS Collaboration and Thomas Mc Cauley. Multi-jet event recorded by the CMS detector (Run 2, 13 TeV). CERN Document Server. Accessed: 2025-04-04. 2015. URL: https://cds.cern.ch/record/2114784. [33] CMS Collaborations and N.Tran. Jet Types. Accessed: 2025-04-05. URL: https://cmsopendata-workshop.github.io/workshop2022-lesson-physics-objects/06-substructure/index.html. [34] Izaak Neutelings. CMS coordinate system. TikZ.net. Accessed: 2025-04-05. URL: https://tikz.net/axis3d_cms/. [35] Matteo Cacciari, Gavin P. Salam, and Gregory Soyez. “The anti-kt jet clustering algorithm”. In: Journal of High Energy Physics 2008.04 (Apr. 2008), p. 063. DOI: 10.1088/1126-6708/2008/04/063. URL: https://dx.doi.org/10.1088/1126-6708/2008/04/063. [36] CMS Collaborations. Jet Clustering Algorithm. Accessed: 2025-04-05. URL: https://cms-opendata-workshop.github.io/workshop2023-lesson-physics-objects/04-jetmet/index.html. [37] Gregor Kasieczka et al. Top Quark Tagging Reference Dataset. Version v0 (2018_03_27). Zenodo, Mar. 2019. DOI: 10.5281/zenodo.2603256. URL: https://doi.org/10.5281/zenodo.2603256. [38] Kai-Feng Chen and Yang-Ting Chien. “Deep learning jet substructure from two-particle correlations”. In: Phys. Rev. D 101 (11 June 2020), p. 114025. DOI: 10.1103/PhysRevD.101.114025. URL: https://link.aps.org/doi/10.1103/PhysRevD.101.114025. [39] Hamza Kheddar et al. Image Classification in High-Energy Physics: A Comprehensive Survey of Applications to Jet Analysis. 2024. arXiv: 2403.11934 [hep-ph]. URL: https://arxiv.org/abs/2403.11934. [40] Jason Sang Hun Lee et al. “Quark-Gluon Jet Discrimination Using Convolutional Neural Networks”. In: Journal of the Korean Physical Society 74.3 (2019), pp. 219–223. DOI: 10.3938/jkps.74.219. URL: https://doi.org/10.3938/jkps.74.219. [41] Jing Li and Hao Sun. An Attention Based Neural Network for Jet Tagging. 2020. arXiv: 2009.00170 [hep-ph]. URL: https://arxiv.org/abs/2009.00170. [42] Sang Kwan Choi et al. “Automatic detection of boosted Higgs boson and top quark jets in an event image”. In: Phys. Rev. D 108 (11 Dec. 2023), p. 116002. DOI: 10.1103/PhysRevD.108.116002. URL: https://link.aps.org/doi/10.1103/PhysRevD.108.116002. [43] Gregor Kasieczka et al. “Deep-learning top taggers or the end of QCD?” In: Journal of High Energy Physics 2017.5 (2017), p. 6. DOI: 10.1007/JHEP05(2017)006. URL: https://doi.org/10.1007/JHEP05(2017)006. [44] Patrick T. Komiske, Eric M. Metodiev, and Matthew D. Schwartz. “Deep learning in color: towards automated quark/gluon jet discrimination”. In: Journal of High Energy Physics 2017.1 (2017), p. 110. DOI: 10.1007/JHEP01(2017)110. URL: https://doi.org/10.1007/JHEP01(2017)110. [45] Pierre Baldi et al. “Jet substructure classification in high-energy physics with deep neural networks”. In: Phys. Rev. D 93 (9 May 2016), p. 094034. DOI: 10.1103/PhysRevD.93.094034. URL: https://link.aps.org/doi/10.1103/PhysRevD.93.094034. [46] Identification of Jets Containing b-Hadrons with Recurrent Neural Networks at the ATLAS Experiment. Tech. rep. All figures including auxiliary figures are available at https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-PHYS-PUB2017-003. Geneva: CERN, 2017. URL: https://cds.cern.ch/record/2255226. [47] Rafael Teixeira de Lima. Sequence-based Machine Learning Models in Jet Physics. 2021. arXiv: 2102.06128 [physics.data-an]. URL: https://arxiv.org/abs/2102.06128. [48] E. Bols et al. “Jet flavour classification using DeepJet”. In: Journal of Instrumentation 15.12 (Dec. 2020), P12012. DOI: 10.1088/1748-0221/15/12/P12012. URL: https://dx.doi.org/10.1088/1748-0221/15/12/P12012. [49] Daniel Guest et al. “Jet flavor classification in high-energy physics with deep neural networks”. In: Phys. Rev. D 94 (11 Dec. 2016), p. 112002. DOI: 10.1103/PhysRevD.94.112002. URL: https://link.aps.org/doi/10.1103/PhysRevD.94.112002. [50] Jason Sang Hun Lee et al. “Quark Gluon Jet Discrimination with Weakly Supervised Learning”. In: Journal of the Korean Physical Society 75.9 (2019), pp. 652–659. DOI: 10.3938/jkps.75.652. URL: https://doi.org/10.3938/jkps.75.652. [51] Shannon Egan et al. Long Short-Term Memory (LSTM) networks with jet constituents for boosted top tagging at the LHC. 2017. arXiv: 1711.09059 [hep-ex]. [52] Jannicke Pearkes et al. Jet Constituents for Deep Neural Network Based Top Quark Tagging. 2017. arXiv: 1704.02124 [hep-ex]. [53] Taoli Cheng. “Recursive Neural Networks in Quark/Gluon Tagging”. In: Computing and Software for Big Science 2.1 (2018), p. 3. DOI: 10.1007/s41781-018-0007-y. URL: https://doi.org/10.1007/s41781-018-0007-y. [54] Gilles Louppe et al. “QCD-aware recursive neural networks for jet physics”. In: Journal of High Energy Physics 2019.1 (2019), p. 57. DOI: 10.1007/JHEP01(2019)057. URL: https://doi.org/10.1007/JHEP01(2019)057. [55] Patrick T. Komiske, Eric M. Metodiev, and Jesse Thaler. “Energy flow networks: deep sets for particle jets”. In: Journal of High Energy Physics 2019.1 (2019), p. 121. DOI: 10.1007/JHEP01(2019)121. URL: https://doi.org/10.1007/JHEP01(2019)121. [56] Huilin Qu, Congqiao Li, and Sitian Qian. Particle Transformer for Jet Tagging. 2024. arXiv: 2202.03772 [hep-ph]. URL: https://arxiv.org/abs/2202.03772. [57] Matthew J. Dolan and Ayodele Ore. “Equivariant energy flow networks for jet tagging”. In: Phys. Rev. D 103 (7 Apr. 2021), p. 074022. DOI: 10.1103/PhysRevD.103.074022. URL: https://link.aps.org/doi/10.1103/PhysRevD.103.074022. [58] Deep Sets based Neural Networks for Impact Parameter Flavour Tagging in ATLAS. Tech. rep. All figures including auxiliary figures are available at https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-PHYS-PUB-2020-014. Geneva: CERN, 2020. URL: https://cds.cern.ch/record/2718948. [59] Benno Käch, Dirk Krücker, and Isabell Melzer-Pellmann. Point Cloud Generation using Transformer Encoders and Normalising Flows. 2022. arXiv: 2211.13623 [hep-ex]. URL: https://arxiv.org/abs/2211.13623. [60] Dimitrios Athanasakos et al. Is infrared-collinear safe information all you need for jet classification? 2023. arXiv: 2305.08979 [hep-ph]. URL: https://arxiv.org/abs/2305.08979. [61] Benno Käch and Isabell Melzer-Pellmann. Attention to Mean-Fields for Particle Cloud Generation. 2023. arXiv: 2305.15254 [hep-ex]. URL: https://arxiv.org/abs/2305.15254. [62] Spandan Mondal, Gaetano Barone, and Alexander Schmidt. PAIReD jet: A multi-pronged resonance tagging strategy across all Lorentz boosts. 2023. arXiv: 2311.11011 [hepex]. URL: https://arxiv.org/abs/2311.11011. [63] Patrick Odagiu et al. Sets are all you need: Ultrafast jet classification on FPGAs for HL-LHC. 2024. arXiv: 2402.01876 [hep-ex]. URL: https://arxiv.org/abs/2402.01876. [64] Rikab Gambhir, Athis Osathapan, and Jesse Thaler. Moments of Clarity: Streamlining Latent Spaces in Machine Learning using Moment Pooling. 2024. arXiv: 2403.08854 [hep-ph]. URL: https://arxiv.org/abs/2403.08854. [65] Wikipedia contributors. Complete graph. Accessed: April 6, 2025. 2025. URL: https://en.wikipedia.org/wiki/Complete_graph. [66] Manzil Zaheer et al. Deep Sets. 2018. arXiv: 1703.06114 [cs.LG]. URL: https://arxiv.org/abs/1703.06114. [67] Yue Wang et al. Dynamic Graph CNN for Learning on Point Clouds. 2019. arXiv: 1801.07829 [cs.CV]. URL: https://arxiv.org/abs/1801.07829. [68] Wikipedia contributors. Bloch sphere. Accessed: April 6, 2025. 2025. URL: https://en.wikipedia.org/wiki/Bloch_sphere. [69] Ali Javadi-Abhari et al. Quantum computing with Qiskit. 2024. DOI: 10.48550/arXiv.2405.08810. arXiv: 2405.08810 [quant-ph]. [70] S.E. Rasmussen et al. “Superconducting Circuit Companion—an Introduction with Worked Examples”. In: PRX Quantum 2 (Dec. 2021). DOI: 10.1103/PRXQuantum.2.040204. [71] S.E. Rasmussen et al. “Superconducting Circuit Companion—an Introduction with Worked Examples”. In: PRX Quantum 2 (4 Dec. 2021), p. 040204. DOI: 10.1103/PRXQuantum.2.040204. URL: https://link.aps.org/doi/10.1103/PRXQuantum.2.040204. [72] Thomas E. Roth, Ruichao Ma, and Weng C. Chew. “The Transmon Qubit for Electromagnetics Engineers: An introduction”. In: IEEE Antennas and Propagation Magazine 65.2 (2023), pp. 8–20. DOI: 10.1109/MAP.2022.3176593. [73] Oxford. Oxford instruments - nanoscience. Jun 4, 2025. URL: https://nanoscience.oxinst.com/products/proteoxmx. [74] Jerry Chow Pat Gumann. IBM scientists cool down the world＇s largest quantum-ready cryogenic concept system. 8 Sep 2022. URL: https://www.ibm.com/quantum/blog/goldeneye-cryogenic-concept-system. [75] Maria Schuld et al. “Evaluating analytic gradients on quantum hardware”. In: Phys. Rev. A 99 (3 Mar. 2019), p. 032331. DOI: 10.1103/PhysRevA.99.032331. URL: https://link.aps.org/doi/10.1103/PhysRevA.99.032331. [76] David Wierichs et al. “General parameter-shift rules for quantum gradients”. In: Quantum 6 (Mar. 2022), p. 677. ISSN: 2521-327X. DOI: 10.22331/q-2022-03-30-677. URL: https://doi.org/10.22331/q-2022-03-30-677. [77] Gavin E. Crooks. Gradients of parameterized quantum gates using the parameter-shift rule and gate decomposition. 2019. arXiv: 1905.13311 [quant-ph]. [78] Adrián Pérez-Salinas et al. “Data re-uploading for a universal quantum classifier”. In: Quantum 4 (Feb. 2020), p. 226. ISSN: 2521-327X. DOI: 10.22331/q-2020-02-06-226. URL: https://doi.org/10.22331/q-2020-02-06-226. [79] Maria Schuld, Ryan Sweke, and Johannes Jakob Meyer. “Effect of data encoding on the expressive power of variational quantum-machine-learning models”. In: Phys. Rev. A 103 (3 Mar. 2021), p. 032430. DOI: 10.1103/PhysRevA.103.032430. URL: https://link.aps.org/doi/10.1103/PhysRevA.103.032430. [80] Alok Shukla and Prakash Vedula. “An efficient quantum algorithm for preparation of uniform quantum superposition states”. In: Quantum Information Processing 23.2 (Jan.2024). ISSN: 1573-1332. DOI: 10.1007/s11128-024-04258-4. URL: http://dx.doi.org/10.1007/s11128-024-04258-4. [81] Ryan Babbush et al. “Encoding Electronic Spectra in Quantum Circuits with Linear T Complexity”. In: Physical Review X 8.4 (Oct. 2018). ISSN: 2160-3308. DOI: 10.1103/physrevx.8.041015. URL: http://dx.doi.org/10.1103/PhysRevX.8.041015. [82] Ville Bergholm et al. PennyLane: Automatic differentiation of hybrid quantum-classical computations. 2022. arXiv: 1811.04968 [quant-ph]. [83] Michael A Nielsen and Isaac L Chuang. “Controlled operations”. In: Quantum Computation and Quantum Information. Cambridge University Press, 2007. Chap. 4. [84] Vojtěch Havlíček et al. “Supervised learning with quantum-enhanced feature spaces”. In: Nature 567.7747 (2019), pp. 209–212. DOI: 10.1038/s41586-019-0980-2. URL: https://doi.org/10.1038/s41586-019-0980-2. [85] Maria Schuld. Supervised quantum machine learning models are kernel methods. 2021. arXiv: 2101.11020 [quant-ph]. URL: https://arxiv.org/abs/2101.11020. [86] Arthur Jacot, Franck Gabriel, and Clément Hongler. Neural Tangent Kernel: Convergence and Generalization in Neural Networks. 2020. arXiv: 1806 . 07572 [cs.LG]. URL: https://arxiv.org/abs/1806.07572. [87] Pedro Domingos. Every Model Learned by Gradient Descent Is Approximately a Kernel Machine. 2020. arXiv: 2012.00152 [cs.LG]. URL: https://arxiv.org/abs/2012.00152. [88] Hsin-Yuan Huang et al. “Power of data in quantum machine learning”. In: Nature Communications 12.1 (2021), p. 2631. DOI: 10.1038/s41467-021-22539-9. URL: https://doi.org/10.1038/s41467-021-22539-9. [89] PyTorch Team. PyTorch. URL: https://pytorch.org. [90] Raghav Kansal et al. JetNet. Version 2. Zenodo, Aug. 2022. DOI: 10.5281/zenodo.6975118. URL: https://doi.org/10.5281/zenodo.6975118. [91] Matteo Cacciari, Gavin P. Salam, and Gregory Soyez. “FastJet user manual”. In: The European Physical Journal C 72.3 (2012), p. 1896. DOI: 10.1140/epjc/s10052-012-1896-2. URL: https://doi.org/10.1140/epjc/s10052-012-1896-2. [92] Christian Bierlich et al. A comprehensive guide to the physics and usage of PYTHIA 8.3. 2022. arXiv: 2203.11601 [hep-ph]. [93] S. Ovyn, X. Rouby, and V. Lemaitre. Delphes, a framework for fast simulation of a generic collider experiment. 2010. arXiv: 0903.2225 [hep-ph]. [94] J. de Favereau et al. “DELPHES 3, A modular framework for fast simulation of a generic collider experiment”. In: JHEP 02 (2014), p. 057. DOI: 10.1007/JHEP02(2014)057. arXiv: 1307.6346 [hep-ex]. [95] Gregor Kasieczka et al. “The Machine Learning landscape of top taggers”. In: SciPost Physics 7.1 (July 2019). ISSN: 2542-4653. DOI: 10.21468/scipostphys.7.1.014. URL: http://dx.doi.org/10.21468/SciPostPhys.7.1.014. [96] J. Alwall et al. “The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations”. In: JHEP 07 (2014), p. 079. DOI: 10.1007/JHEP07(2014)079. arXiv: 1405.0301 [hep-ph]. [97] Raghav Kansal et al. Particle Cloud Generation with Message Passing Generative Adversarial Networks. 2022. arXiv: 2106.11535 [cs.LG]. URL: https://arxiv.org/abs/2106.11535. [98] Maria Schuld et al. “Circuit-centric quantum classifiers”. In: Phys. Rev. A 101 (3 Mar. 2020), p. 032308. DOI: 10.1103/PhysRevA.101.032308. URL: https://link.aps.org/doi/10.1103/PhysRevA.101.032308. [99] Abien Fred Agarap. Deep Learning using Rectified Linear Units (ReLU). 2019. arXiv: 1803.08375 [cs.NE]. URL: https://arxiv.org/abs/1803.08375. [100] Matthias Fey and Jan E. Lenssen. “Fast Graph Representation Learning with PyTorch Geometric”. In: ICLR Workshop on Representation Learning on Graphs and Manifolds. 2019. [101] Diederik P. Kingma and Jimmy Ba. Adam: A Method for Stochastic Optimization. 2017. arXiv: 1412.6980 [cs.LG]. URL: https://arxiv.org/abs/1412.6980. [102] John Preskill. “Quantum Computing in the NISQ era and beyond”. In: Quantum 2 (Aug. 2018), p. 79. ISSN: 2521-327X. DOI: 10.22331/q- 2018- 08- 06- 79. URL: https://doi.org/10.22331/q-2018-08-06-79. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/98139 | - |
| dc.description.abstract | 機器學習技術，尤其是深度神經網路，近年來逐漸成為高能物理領域當中分析的重要工具。這些方法已在多項應用中展現出驚人的效果且強大的潛力。隨著機器學習與量子運算的整合，一個特別的新研究領域「量子機器學習」逐漸成形，預期能在處理更複雜的資料時提供一定的優勢。
在本論文中，我們提出一種名為「量子完全圖神經網路（QCGNN）」的方法，它是一種建立在變分量子電路所設計的演算法，目標是處理完全圖上的學習任務。該模型透過參數化的量子邏輯閘達成資料編碼，並利用量子平行性的特性，期許在一些特別場景中能比古典方法帶來更有效率的運算。 為了比較，我們將此模型應用於大型強子對撞機（LHC）中的質子－質子碰撞事件，處理噴流分類的任務。在這個情境下，噴流是由高能夸克或膠子所產生的粒子集合，並可建構為完全圖的形式。由於噴流分類對理解粒子交互作用中有著關鍵性的影響，所以更快速且精準的模型來辨識不同來源的噴流可以有更好的分析結果。QCGNN 展現出能有效處理基於圖形結構的噴流資料的能力，有望在未來高亮度 LHC 實驗中發揮其應用潛力。 此外，我們也與古典深度學習模型進行比較分析，以評估 QCGNN 的效能表現。我們也在 IBM 的量子硬體上實作並測試此模型，以探討其在真實量子設備上的可行性。整體而言，QCGNN 不僅對圖形方法中常見的運算挑戰提出了解法，也展現出在量子運算與高能物理交叉領域中具有的研究潛力。 | zh_TW |
| dc.description.abstract | Machine learning techniques, especially deep neural networks, have gradually become important tools in the field of high-energy physics. These methods have shown encouraging results in various applications. In recent years, the combination of machine learning and quantum computing has led to the development of a new field of research field called the "Quantum Machine Learning", which is expected to offer advantages in handling increasingly complex data.
This dissertation proposes the Quantum Complete Graph Neural Network (QCGNN) designed for learning tasks on fully connected graphs, which is a VQC-based algorithm, where VQC stands for "Variational Quantum Circuit". The QCGNN makes use of parameterized quantum circuits and aims to benefit from quantum parallelism, potentially providing computational advantages compared to classical approaches. We apply the QCGNN to the task of jet discrimination in proton-proton collisions at the Large Hadron Collider (LHC). In this context, jets, which are collections of particles originating from energetic partons, are represented as complete graphs. Since jet classification plays a crucial role in understanding particle interactions, an effective model is necessary to distinguish between different jet types. The QCGNN shows the ability to process graph-based jet data efficiently, which may be helpful for future analyses at the high-luminosity LHC. In addition, we compare the QCGNN with classical deep learning models to evaluate its performance. We also test the implementation of QCGNN on IBM quantum hardware to examine its feasibility on real devices. The results suggest that the QCGNN has potential for further exploration in both quantum computing and high-energy physics research. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-07-30T16:04:43Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-07-30T16:04:43Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | Acknowledgements iii
摘要 v Abstract vii Contents ix List of Figures xiii List of Tables xix Denotation xxi Chapter 1 Introduction 1 1.1 High-Energy Physics (HEP) 1 1.2 Machine Learning (ML) 3 1.3 Jet Discrimination with ML in HEP 4 Chapter 2 Jet in High-Energy Physics 7 2.1 Hadronization and Jet Discrimination 7 2.2 Cylindrical Coordinate System 10 2.3 Anti-$k_T$ Algorithm 12 2.4 Particle Flow Features 13 Chapter 3 Classical Machine Learning 17 3.1 An Overview of Applications in Jet Discrimination 17 3.2 Graph Representation 20 3.3 The Deep Set Theorem 21 3.4 Message-Passing Graph Neural Network 23 3.4.1 Particle Flow Network (PFN) 25 3.4.2 Particle Net (PNet) 26 3.4.3 Particle Transformer (ParT) 28 Chapter 4 Quantum Machine Learning 31 4.1 A Review on Fundamentals of Quantum Computing 31 4.1.1 Qubits and Quantum Entanglement 31 4.1.2 Unitary Transformations and the Quantum Gates 33 4.1.3 Measurements and Observables 36 4.2 IBM Quantum Computers 37 4.2.1 Superconducting Qubits and Circuit Hamiltonian 38 4.2.2 Qubit Control via Microwave Drive 41 4.2.3 Cryogenic Environment and Dilution Refrigeration 42 4.3 Variational Quantum Circuit (VQC) 44 4.3.1 The Ansatz of Variational Quantum Circuit 44 4.3.2 The Parameter-Shift Rule 46 4.3.3 Data-Reuploading Technique 48 Chapter 5 Quantum Complete Graph Neural Network (QCGNN) 51 5.1 Model Architecture of QCGNN 51 5.1.1 Uniform State Oracle 56 5.1.2 Multi-Controlled Quantum Gates 60 5.2 Formulating QCGNN within the MP-GNN Framework 63 5.3 Connections Between QCGNN and Kernel Methods 64 5.4 Computational Complexity Analysis of QCGNN 66 5.5 Extending QCGNN for Sequential and Temporal Data 68 5.6 Generalizing QCGNN to Arbitrary Graph Topologies 68 Chapter 6 Benchmark on Jet Discrimination 71 6.1 Monte Carlo Simulated Jet Datasets 71 6.2 Classical and Quantum Models 82 6.2.1 QCGNN and MP-GNN Setup 82 6.2.2 Number of Parameters in QCGNN and MP-GNN 85 6.2.3 State-of-the-art Classical Models 86 6.3 Training Results of Jet Discrimination 89 6.3.1 Justification of the Transverse Momentum Threshold 90 6.3.2 Classical Models and QCGNN on Simulators 93 6.3.3 Pre-trained QCGNN on IBMQ 93 6.3.4 QCGNN Quantum Gate Runtime Analysis 96 Chapter 7 Conclusion and Future Prospect 99 7.1 Summary about QCGNN 99 7.2 Future Work 100 Bibliography 103 | - |
| dc.language.iso | en | - |
| dc.subject | 量子機器學習 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 高能物理 | zh_TW |
| dc.subject | 噴流辨識 | zh_TW |
| dc.subject | 量子電腦 | zh_TW |
| dc.subject | Quantum Computation | en |
| dc.subject | High-Energy Physics | en |
| dc.subject | Jet Discrimination | en |
| dc.subject | Machine Learnning | en |
| dc.subject | Quantum Machine Learning | en |
| dc.title | 量子完全圖神經網路在高能物理噴流辨識的應用及量子優勢之探討 | zh_TW |
| dc.title | Quantum Complete Graph Neural Network in the Jet Discrimination of High-Energy Physics and Discussion on its Quantum Advantage | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 博士 | - |
| dc.contributor.oralexamcommittee | 呂榮祥;管希聖;蔣正偉;林俊達;許琇娟 | zh_TW |
| dc.contributor.oralexamcommittee | Rong-Shyang Lu;Hsi-Sheng Goan;Cheng-Wei Chiang;Guin-Dar Lin;Hsiu-Chuan Hsu | en |
| dc.subject.keyword | 機器學習,量子機器學習,量子電腦,噴流辨識,高能物理, | zh_TW |
| dc.subject.keyword | Machine Learnning,Quantum Machine Learning,Quantum Computation,Jet Discrimination,High-Energy Physics, | en |
| dc.relation.page | 110 | - |
| dc.identifier.doi | 10.6342/NTU202501658 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2025-07-17 | - |
| dc.contributor.author-college | 理學院 | - |
| dc.contributor.author-dept | 物理學系 | - |
| dc.date.embargo-lift | 2025-07-31 | - |
| 顯示於系所單位： | 物理學系 | |
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