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DTSTART;TZID=America/Denver:20231113T161000
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UID:submissions.supercomputing.org_SC23_sess445_misc136@linklings.com
SUMMARY:Invited Talk 5:  Building Quantum Machine Learning for Real-World 
 Applications
DESCRIPTION:Kathleen Hamilton (Oak Ridge National Laboratory (ORNL))\n\nQu
 antum machine learning is a rapidly growing field of quantum computing, an
 d many deep learning models and methods have been adapted into quantum ana
 logues using gate-based or annealing-based platforms.  These methods have 
 been essential for uncovering subtleties in quantum learning dynamics, and
  there are a growing number of examples that can be found in the literatur
 e, implemented in simulated, or actual quantum hardware. The maturity of q
 uantum technology presents opportunities for building, and training larger
  quantum machine models.  But with increasing circuit depth and width, whe
 n working with real-world, classical datasets, the field still faces sever
 al obstacles, namely, how to pre-process data efficiently and effectively 
 for quantum machine learning, and how to post-process the outcomes of meas
 urements.\n\nIn this talk, I will present an overview of several research 
 projects that are ongoing at Oak Ridge National Laboratory in the fields o
 f high energy physics and natural language processing.  I will highlight a
 nd discuss the challenges, and advantages we have encountered when buildin
 g, training and deploying quantum generative models, quantum natural langu
 age processing models, and quantum classifiers, either as standalone model
 s or as components of hybrid workflows.\n\nTag: Algorithms, Heterogeneous 
 Computing, Large Scale Systems\n\nRegistration Category: Workshop Reg Pass
 \n\nSession Chairs: Vassil Alexandrov (Hartree Centre, STFC); Jack Dongarr
 a (University of Tennessee, Knoxville; Oak Ridge National Laboratory (ORNL
 )); Christian Engelmann (Oak Ridge National Laboratory (ORNL)); Al Geist (
 Oak Ridge National Laboratory (ORNL)); and Dieter A. Kranzlmueller (Ludwig
 -Maxmilians-Universität München, Leibniz Supercomputing Centre (LRZ))\n\n
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