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Gaussian boson sampling and multi-particle event optimization by machine learning in the quantum phase space [article]

Claudio Conti
2021 arXiv   pre-print
We also demonstrate that multi-particle events in Gaussian boson sampling can be optimized by a proper design and training of the neural network weights.  ...  We use neural networks to represent the characteristic function of many-body Gaussian states in the quantum phase space.  ...  ACKNOWLEDGMENTS We acknowledge support from Horizon 2020 Framework Programme QuantERA grant QUOMPLEX, by National Research Council (CNR), Grant 731473.  ... 
arXiv:2102.12142v1 fatcat:wtn64gbxbreyfg34gloyn77nkm

Quantum Anomaly Detection for Collider Physics [article]

Sulaiman Alvi, Christian Bauer, Benjamin Nachman
2022 arXiv   pre-print
Quantum Machine Learning (QML) is an exciting tool that has received significant recent attention due in part to advances in quantum computing hardware.  ...  of the LHC and beyond.  ...  Acknowledgments This work was supported by the Department of Energy, Office of Science under contract number DE-AC02-05CH11231.  ... 
arXiv:2206.08391v1 fatcat:wxxjxhcodzagjfapcfrmruw4zq

Variational quantum algorithm for Gaussian discrete solitons and their boson sampling [article]

Claudio Conti
2022 arXiv   pre-print
By training the resulting phase-space quantum machine learning model, we find different soliton solutions varying the number of particles and interaction strength.  ...  We consider Gaussian states that enable measuring the degree of entanglement and sampling the probability distribution of many-particle events.  ...  APPENDIX:GRAPH AND PARAMETERS OF THE MODEL  ... 
arXiv:2110.12379v4 fatcat:vd4t2gvezbaspfltmi2npooch4

When Quantum Computation Meets Data Science: Making Data Science Quantum

Yazhen Wang
2022 Harvard data science review  
Yet because the stochasticity of quantum physics renders quantum computation random, data science can play an important role in the development of quantum computation and quantum information.  ...  Overall, it advocates for the development of quantum data science for advancing quantum computation and quantum information.  ...  The research of Yazhen Wang was supported in part by NSF grants DMS-1707605 and DMS-1913149.  ... 
doi:10.1162/99608f92.ef5d8928 doaj:c54e3645c9e8447aa836a1b6aca75028 fatcat:ofm7xccgwvgh3cf36kfh5htio4

How to GAN LHC events

Anja Butter, Tilman Plehn, Ramon Winterhalder
2019 SciPost Physics  
For top pair production we show how such a network describes intermediate on-shell particles, phase space boundaries, and tails of distributions.  ...  Event generation for the LHC can be supplemented by generative adversarial networks, which generate physical events and avoid highly inefficient event unweighting.  ...  Acknowledgments We are very grateful to Gregor Kasieczka for his collaboration in the early phase of the project and to Jonas Glombitza and Till Bungert for fruitful discussions.  ... 
doi:10.21468/scipostphys.7.6.075 fatcat:dfxuctown5f5bebogxn3s2gy5y

Machine Learning and LHC Event Generation [article]

Anja Butter, Simon Badger, Sascha Caron, Kyle Cranmer, Francesco Armando Di Bello, Etienne Dreyer, Stefano Forte, Sanmay Ganguly, Dorival Gonçalves, Eilam Gross, Theo Heimel, Gudrun Heinrich (+37 others)
2022 arXiv   pre-print
This review illustrates a wide range of applications of modern machine learning to event generation and simulation-based inference, including conceptional developments driven by the specific requirements  ...  New ideas and tools developed at the interface of particle physics and machine learning will improve the speed and precision of forward simulations, handle the complexity of collision data, and enhance  ...  Acknowledgments Anja Butter, Gudrun Heinrich, and Tilman Plehn are supported by the Deutsche Forschungsgemeinschaft under grant 396021762 -TRR 257 Particle Physics Phenomenology after the Higgs Discovery  ... 
arXiv:2203.07460v1 fatcat:yr27ztzdzrgbhggb6kq5fdempu

Integrated photonic quantum technologies

Jianwei Wang, Fabio Sciarrino, Anthony Laing, Mark G. Thompson
2019 Nature Photonics  
, simulations of quantum physical and chemical systems, Boson sampling, and linear-optic quantum information processing.  ...  In the decade after its 2008 inception, the technology of integrated quantum photonics enabled the generation, processing, and detection of quantum states of light, at a steadily increasing scale and level  ...  Both Gaussian and scattershot Boson sampling have been implemented in the same Si-photonic chip [98] . Validation of Boson sampling.  ... 
doi:10.1038/s41566-019-0532-1 fatcat:jxeowasmijeihft36h3fye7dja

Event Generation and Statistical Sampling for Physics with Deep Generative Models and a Density Information Buffer [article]

Sydney Otten, Sascha Caron, Wieske de Swart, Melissa van Beekveld, Luc Hendriks, Caspar van Leeuwen, Damian Podareanu, Roberto Ruiz de Austri, Rob Verheyen
2021 arXiv   pre-print
importance sampling, e.g. for the phase space integration of matrix elements in quantum field theories.  ...  By buffering density information of encoded Monte Carlo events given the encoder of a VAE we are able to construct a prior for the sampling of new events from the decoder that yields distributions that  ...  Events that are generated by a Monte Carlo generator (gray) and several machine learning models for a toy two-body decay in a) and the leptonic Z decay in b).  ... 
arXiv:1901.00875v5 fatcat:6snyq77tlzfxhjhdd3fu7bndau

E Pluribus Unum Ex Machina: Learning from Many Collider Events at Once [article]

Benjamin Nachman, Jesse Thaler
2021 arXiv   pre-print
There have been a number of recent proposals to enhance the performance of machine learning strategies for collider physics by combining many distinct events into a single ensemble feature.  ...  We show how one can build optimal multi-event classifiers from single-event classifiers, and we also show how to construct multi-event classifiers such that they produce optimal single-event classifiers  ...  For this reason, there need not be a gain from using multi-event strategies over single-event strategies in the context of machine learning.  ... 
arXiv:2101.07263v2 fatcat:hf3lraxh4nfo5ijiqdzmd7nuye

Advanced Multi-Variate Analysis Methods for New Physics Searches at the Large Hadron Collider [article]

Anna Stakia, Tommaso Dorigo, Giovanni Banelli, Daniela Bortoletto, Alessandro Casa, Pablo de Castro, Christophe Delaere, Julien Donini, Livio Finos, Michele Gallinaro, Andrea Giammanco, Alexander Held (+13 others)
2021 arXiv   pre-print
Many of those methods were successfully used to improve the sensitivity of data analyses performed by the ATLAS and CMS experiments at the CERN Large Hadron Collider; several others, still in the testing  ...  phase, promise to further improve the precision of measurements of fundamental physics parameters and the reach of searches for new phenomena.  ...  Acknowledgements This work is part of a project that has received funding from the European  ... 
arXiv:2105.07530v1 fatcat:wpwknhdgljc6lkj6uc36tbm4qq

Event generation and statistical sampling for physics with deep generative models and a density information buffer

Sydney Otten, Sascha Caron, Wieske de Swart, Melissa van Beekveld, Luc Hendriks, Caspar van Leeuwen, Damian Podareanu, Roberto Ruiz de Austri, Rob Verheyen
2021 Nature Communications  
importance sampling, e.g., for the phase space integration of matrix elements in quantum field theories.  ...  We investigate the feasibility of learning the event generation and the frequency of occurrence with several generative machine learning models to produce events like Monte Carlo generators.  ...  Acknowledgements This work was partly funded by and carried out in the SURF Open Innovation Lab project "Machine learning enhanced high-performance computing applications and computations" and was partly  ... 
doi:10.1038/s41467-021-22616-z pmid:34016982 fatcat:dh2ef6z7czcfpbqeyema3ri2ae

Generative Networks for LHC events [article]

Anja Butter, Tilman Plehn
2020 arXiv   pre-print
Modern machine learning, specifically generative networks, will help us tackle simulation challenges for the coming LHC runs.  ...  Since neural networks can be inverted, they also open new avenues in LHC analyses.  ...  Our research is supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under grant 396021762 -TRR 257 Particle Physics Phenomenology after the Higgs Discovery.  ... 
arXiv:2008.08558v1 fatcat:nnub35g6tve4dam67pdew65zwi

Exploring phase space with Neural Importance Sampling

Enrico Bothmann, Timo Janßen, Max Knobbe, Tobias Schmale, Steffen Schumann
2020 SciPost Physics  
The method guarantees full phase space coverage and the exact reproduction of the desired target distribution, in our case given by the squared transition matrix element.  ...  We present a novel approach for the integration of scattering cross sections and the generation of partonic event samples in high-energy physics.  ...  SS acknowledges support through the Fulbright-Cottrell Award and from BMBF (contract 05H18MGCA1).  ... 
doi:10.21468/scipostphys.8.4.069 fatcat:xtekkpkvczg75l4icopxnniy2q

Snowmass White Paper: Quantum Computing Systems and Software for High-energy Physics Research [article]

Travis S. Humble, Andrea Delgado, Raphael Pooser, Christopher Seck, Ryan Bennink, Vicente Leyton-Ortega, C.-C. Joseph Wang, Eugene Dumitrescu, Titus Morris, Kathleen Hamilton, Dmitry Lyakh, Prasanna Date (+14 others)
2022 arXiv   pre-print
While the emerging hardware, software, and applications of quantum computing are exciting opportunities, significant gaps remain in integrating such techniques into the HEP community research programs.  ...  Quantum computing offers a new paradigm for advancing high-energy physics research by enabling novel methods for representing and reasoning about fundamental quantum mechanical phenomena.  ...  ACKNOWLEDGEMENTS This work was supported by supported by the U.  ... 
arXiv:2203.07091v1 fatcat:r4sf3d62n5hctbnsue46c2u2cq

Multivariate Analysis Methods in Particle Physics

Pushpalatha C. Bhat
2011 Annual Review of Nuclear and Particle Science  
The spectacular performance of the Tevatron and the beginning of operations of the Large Hadron Collider have placed us at the threshold of a new era in particle physics.  ...  The use of advanced analysis techniques is crucial in achieving this goal. In this review, I discuss the concepts of optimal analysis, some important advanced analysis methods and a few examples.  ...  In HEP, the 3 I use feature vectors and inputs, interchangeably. Machine Learning: Machine Learning is the paradigm for automated learning from data using computer algorithms.  ... 
doi:10.1146/annurev.nucl.012809.104427 fatcat:ybwjoedumjhofdspwqvyqweuqe
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