Fun little paper to appear tonight on the arXiv.

How to do Hamiltonian Monte Carlo on digital Quantum Computers.

As physics-based probabilistic ML accelerators are on the horizon, important to test how QC's could try to compete.

Best way to predict future is to invent it.🙂





@FilArons Work done w/ Owen Lockwood, @FilArons, & @Peterweiss9700 a few summers ago (before founding Extropic).

Stay tuned for arXiv link!

Code is open source, built in TensorFlow Quantum, available here:
github.com/gillverd/cv-tfq

"Can quantum androids dream of quantum electrodynmic sheep?"🤔🤖⚛️💭🐑⚡️

We explore this in a new paper on quantum-probabilistic generative models 🎲⚛️🤖💭 and information geometry 🌐📏 from our former QML group @Theteamatx 🥳

Time for an epic thread 👇
scirate.com/arxiv/2206.046… For some context: quantum-probabilistic generative models (QPGM) are a class of hybrid ML models which combine classical probabilistic ML models (e.g. EBM's) and quantum neural networks (QNNs). As we show in this new paper, turns out these types of models are optimal in many ways

In January, @PetarV_93 & @mmbronstein graciously invited me to share what I found most promising for 2022 progress in QML (below).

This week, a total of 3 (!) papers extending QGNN's into Geometric QDL were posted!

See below for related threads 👇

towardsdatascience.com/predictions-an… @PetarV_93 @mmbronstein

https://twitter.com/MartinLaroo/status/1522374082435727360?s=20&t=0Cyb8rUHaE9s315QEOJPqQ

To understand the state of the quantum industry: imagine if there were 200+ companies trying to be @SpaceX the same year Sputnik made it to orbit, before @NASA even landed on the Moon. Meanwhile, fully aware that the tech has to mature so that the market for commercial spaceflight reaches sufficiently substantial numbers to warrant high valuations, startups pitch to commercial freight companies that the future of shipping is rocket-based.