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Quantum Natural Language Processing (QNLP) is a very nascent field which deals with using quantum computers to solve natural language processing problems. Quantum advantage (speed-up) for QNLP tasks has already been established in literature and has been attributed to the fact that quantum models for natural language processing canonically incorporate linguistic meanings with rich linguistic structure, most notably grammar. The fact that it takes a quantum-like model to combine meaning and structure, establishes QNLP as quantum-native, on par with simulation of quantum systems. Moreover, the now leading Noisy Intermediate-Scale Quantum (NISQ) paradigm for encoding classical data on quantum hardware, variational quantum circuits, makes NISQ exceptionally QNLP-friendly: linguistic structure can be encoded as a free lunch, in contrast to the apparently exponentially expensive classical encoding of grammar.
In this talk, Mitch will first introduce some quantum mechanics enroute to talking about quantum computation and QNLP. Specifically, he will talk about quantum algorithms for incorporating compositionality, providing some basic conceptual and mathematical foundations needed to understand QNLP, and do so in computer scientist friendly terms. He will end with talking about the state of the art in QNLP.
Mitch is a post-doctoral researcher at ISI working with Marjorie Freedman in the networking and cyber security division. Mitch, recently graduated from his PhD from University of Arizona before joining ISI as a postdoc. Before that, he worked in the software industry for 10+ years. He got his masters and undergrad degrees from Birla Institute of Technology and Science(BITS), Pilani, India. His research interests include natural language processing, cyber security and quantum computation.
The recording for this AI Seminar talk will be posted on our USC/ISI YouTube page within 1-2 business days: https://www.youtube.com/user/USCISI.
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