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Invited Talk 5: Building Quantum Machine Learning for Real-World Applications
DescriptionQuantum machine learning is a rapidly growing field of quantum computing, and many deep learning models and methods have been adapted into quantum analogues 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 literature, implemented in simulated, or actual quantum hardware. The maturity of quantum technology presents opportunities for building, and training larger quantum machine models. But with increasing circuit depth and width, when working with real-world, classical datasets, the field still faces several obstacles, namely, how to pre-process data efficiently and effectively for quantum machine learning, and how to post-process the outcomes of measurements.
In this talk, I will present an overview of several research projects that are ongoing at Oak Ridge National Laboratory in the fields of high energy physics and natural language processing. I will highlight and discuss the challenges, and advantages we have encountered when building, training and deploying quantum generative models, quantum natural language processing models, and quantum classifiers, either as standalone models or as components of hybrid workflows.
In this talk, I will present an overview of several research projects that are ongoing at Oak Ridge National Laboratory in the fields of high energy physics and natural language processing. I will highlight and discuss the challenges, and advantages we have encountered when building, training and deploying quantum generative models, quantum natural language processing models, and quantum classifiers, either as standalone models or as components of hybrid workflows.
Event Type
Workshop
TimeMonday, 13 November 20234:10pm - 4:50pm MST
Location710
Algorithms
Heterogeneous Computing
Large Scale Systems
W