Graphical Models and Logical Abstractions for Quantum Systems
Everyone is talking about quantum computers, one of today’s most interesting computation models.
Quantum computers now have the size and reliability to allow more widespread experimentation and educational outreach. With more research and students using quantum computers, a natural question lies in how to simulate, reason about, and debug quantum programs and circuits. Probabilistic graphical models such as Bayesian networks offer a natural description of a quantum computer’s quantum states and probabilistic noise. This talk will discuss a case study presented at ASPLOS 2021 in simulating variational algorithms. The talk will also explain some extensions for modeling correlated noise and higher-dimensional quantum states.
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- Date: 18 Nov 2021
- Time: 08:00 PM to 09:30 PM
- All times are (UTC-05:00) Eastern Time (US & Canada)
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Speakers
Yipeng Huang
Yipeng Huang joined Rutgers University as an assistant professor in 2020. His research and teaching are in quantum computing and emerging computer architectures. He is interested in quantum computer systems: how to program them, simulate them, and debug them. His research has previously been supported by a DARPA STTR grant, and his work had been cited among IEEE MICRO top picks. Prior to joining Rutgers, Yipeng was a postdoc at Princeton University with Dr. Margaret Martonosi, and he was a PhD student at Columbia University with Dr. Simha Sethumadhavan.