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.

Speaker(s): Yipeng Huang,

Virtual: https://events.vtools.ieee.org/m/282460