Abstract
The idea of quantum computation has been raised since 1982, when Richard Feynman first proposed it. Many algorithms targeting various problems demonstrating quantum speedup have been formulated since then. As the popularity of machine learning climbing, people started to think of the possibilities of formulating them in the context of quantum computation, which is the emerging quantum machine learning. The pioneer problem solved is the linear system of equations, named HHL algorithm, formulated in 2009. In this seminar, an algorithm solving linear regression using continuous variable resources - qumodes will be discussed. In addition, the potential applications of different encoding resources such as qumodes inspired from this algorithm in other problems will also be discussed.
Anyone interested is welcome to attend.