Events at Physics
Events on Wednesday, November 4th, 2020
- Wisconsin Quantum Institute
- QuantHEP Seminar
- APPLICATION OF QUANTUM MACHINE LEARNING TO HIGH ENERGY PHYSICS ANALYSIS AT LHC USING QUANTUM COMPUTER SIMULATORS AND QUANTUM COMPUTER HARDWARE
- Time: 10:00 am
- Place: Livestreaming on QuantHEP Seminar YouTube channel:
- Speaker: Sau Lan Wu, UW–Madison Physics, CERN
- Abstract: Machine learning enjoys widespread success in High Energy Physics (HEP) analysis at LHC. However the ambitious HL-LHC program will require much more computing resources in the next two decades. Quantum computing may offer speed-up for HEP physics analysis at HL-LHC, and can be a new computational paradigm for big data analysis in High Energy Physics.
We have successfully employed Variational Quantum Classifier (VQC) method, Quantum Support Vector Machine Kernel (QSVM-kernel) method and Quantum Neural Network (QNN) method for two LHC flagship analyses: ttH (Higgs production in association with two top quarks) and H->mumu (Higgs decay to two muons, the second generation fermions).
We will present our experiences and results of a study on LHC High Energy Physics data analysis with IBM Quantum Simulator and Quantum Hardware (using IBM Qiskit framework), Google Quantum Simulator (using Google Cirq framework), and Amazon Quantum Simulator (using Amazon Braket cloud service). The work is in the context of a Qubit platform. Taking into account the present limitation of hardware access, different quantum machine learning methods are studied on simulators and the results are compared with classical machine learning methods (BDT, classical Support Vector Machine and classical Neural Network). Furthermore, we do apply quantum machine learning on IBM quantum hardware to compare performance between quantum simulator and quantum hardware.
The work is performed by an international and interdisciplinary collaboration with the Department of Physics and Department of Computer Sciences of University of Wisconsin, CERN Quantum Technology Initiative, IBM Research Zurich, Fermilab Quantum Institute, BNL Computational Science Initiative, State University of New York at Stony Brook, and Quantum Computing and AI Research of Amazon Web Services.
This work pioneers a close collaboration of academic institutions with industrial corporations in a High Energy Physics analysis effort.
Although the era of efficient quantum computing may still be years away, we have made promising progress and obtained preliminary results in applying quantum machine learning to High Energy Physics. A PROOF OF PRINCIPLE.
- NPAC (Nuclear/Particle/Astro/Cosmo) Forum
- QuantHEP – Quantum Computing Solutions for High-Energy Physics
- Time: 10:00 am
- Speaker: Prof. Sau Lan Wu, University of Wisconsin - Madison
- Abstract: APPLICATION OF QUANTUM MACHINE LEARNING TO HIGH ENERGY PHYSICS ANALYSIS AT LHC USING QUANTUM COMPUTER SIMULATORS AND QUANTUM COMPUTER HARDWARE
- Physics ∩ ML Seminar
- Flow-based likelihoods for non-Gaussian inference
- Time: 11:00 am
- Place: Online Seminar: Please sign up for our mailing list at www.physicsmeetsml.org for zoom link
- Speaker: Ana Diaz Rivero, Harvard University
- Abstract: We investigate the use of data-driven likelihoods to bypass a key assumption made in many scientific analyses, which is that the true likelihood of the data is Gaussian. In particular, we suggest using the optimization targets of flow-based generative models, a class of models that can capture complex distributions by transforming a simple base distribution through layers of nonlinearities. We call these flow-based likelihoods (FBL). We analyze the accuracy and precision of the reconstructed likelihoods on mock Gaussian data, and show that simply gauging the quality of samples drawn from the trained model is not a sufficient indicator that the true likelihood has been learned. We nevertheless demonstrate that the likelihood can be reconstructed to a precision equal to that of sampling error due to a finite sample size. We then apply FBLs to mock weak lensing convergence power spectra, a cosmological observable that is significantly non-Gaussian (NG). We find that the FBL captures the NG signatures in the data extremely well, while other commonly-used data-driven likelihoods, such as Gaussian mixture models and independent component analysis, fail to do so. This suggests that works that have found small posterior shifts in NG data with data-driven likelihoods such as these could be underestimating the impact of non-Gaussianity in parameter constraints. By introducing a suite of tests that can capture different levels of NG in the data, we show that the success or failure of traditional data-driven likelihoods can be tied back to the structure of the NG in the data. Unlike other methods, the flexibility of the FBL makes it successful at tackling different types of NG simultaneously. Because of this, and consequently their likely applicability across datasets and domains, we encourage their use for inference when sufficient mock data are available for training.
- Host: Gary Shiu
- Department Meeting
- Time: 12:15 pm
- Place: Virtual see "abstract" for connection info
- Speaker: Sridhara Dasu, Department Chair, UW-Madison
- Meeting Coordinates: Meeting number: 120 392 9242 Password: Q5EjaTz3Pk3 (75352893 from phones) Join by video system Dial firstname.lastname@example.org You can also dial 126.96.36.199 and enter your meeting number. Join by phone +1-415-655-0001 US Toll +1-312-535-8110 United States Toll (Chicago) Access code: 120 392 9242
- Host: Sridhara Dasu, Department Chair