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Efficient preparation of thermal states of quantum systems: natural or artificial

Lecturer: Aram Harrow

Scribes: Sinho Chewi, William Moses, Tasha Schoenstein, Ary Swaminathan

November 9, 2018


Sampling from thermal states was one of the first and (initially) most important uses of computers. In this blog post, we will discuss both classical and quantum Gibbs distributions, also known as thermal equilibrium states. We will then discuss Markov chains that have Gibbs distributions as stationary distributions. This leads into a discussion of the equivalence of mixing in time (i.e. the Markov chain quickly equilibrates over time) and mixing in space (i.e. sites that are far apart have small correlation). For the classical case, this equivalence is known. After discussing what is known classically, we will discuss difficulties that arise in the quantum case, including (approximate) Quantum Markov states and the equivalence of mixing in the quantum case.

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Sum some sums!

The other day my friend Miguel got an email back from a Google recruiter asking if he’d like to interview.

Being a TA for the MIT’s introductory algorithms class, and generally knowledgable about algorithms (I often help prep friends for algo interviews), he asked if I could help him prepare.

I was more than happy to oblige.

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Tapir is a new framework for compilers that offers the ability to do a variety of optimizations on parallel code, created by myself, TB Schardl, and Charles Leiserson [1].

Since we’re getting a number of questions about it, I thought I’d write this blog post to talk about some of its features!

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