Quantum Machine Learning
Highlights and Reflections
Sharing here a slide deck I prepared for potential presentation to a local (Houston) machine learning meet-up. The deck is an adaptation / review of a recently published paper addressing the intersection of emerging technologies in quantum computing and machine learning. Although a version of this paper was recently published in Nature journal, I am instead basing this review on on a prior 2016 draft that is available on arXiv [here]— I would feel a little weird giving this treatment for a version that is behind a paywall, so the hope is that by limiting my review to the older public version the authors will grant me a little leniency in sharing this work in a public forum. For those with professional interest or who need up to date information I encourage you to seek the Nature version that is behind a paywall [here].
Most of the points here are highlights and excerpts from the paper itself, often edited for condensed prose or to combine points. In a few cases I have incorporated some additional language meant to address more elementary concepts for explanatory purposes (most of the items in italic font). I would consider my most useful contribution to be the updating of the selected citations with paper titles in colored font for ease of scanning along with the incorporation of arXiv hyperlinks throughout. The version uploaded to medium below will be image files only (no active hyperlinks or selectable text). Parallel I will upload a pdf version of this deck with hyperlinks intact to LinkedIn [here], and would encourage any reader with interests in digging deeper to pull this version for ease of clickable links in citations — there is a wealth of material here and I would expect some very useful nuggets buried in these citations, at the minimum certainly a clear path to some cutting edge industry expertise. I was surprised at the extent to which the cited papers are freely available on arXiv (or in a few cases some other archive), this trend for academics to share works in a public forum like arXiv is certainly a blessing to amateurs like me without access to a university library.
Just fair warning, as the content here could foreseeably be a touch dry for those new the field, I’ve made liberal use of (sometime only vaguely related (if at all)) images to add a little color. I’ve also included here an embedded YouTube album which could be useful as a soundtrack for ambience during review.