John M. Martyn

A recent passion project of mine has been to communicate a research result through an animated video, in an intuitive and pedagogical format. I’m happy to finally share this video, in which I discuss our recent work on neural-network quantum field states: youtu.be/rrvZDZMii-0

Glad to see this paper out! It was definitely the largest collaboration (and paper) I’ve ever been a part of!

Over the recent weeks and months, @preskill and I sat down to think about where we are in quantum computing. While the noisy intermediate-scale quantum (#NISQ) era — a term coined by John in 2018 — is just unfolding as we speak, the time seems right to look ahead to the next steps to come. scirate.com/arxiv/2510.199… In this perspectives article, we try to sketch the fraught road to quantum advantage — the path toward fault-tolerant, application-scale quantum (#FASQ) computers. There are several gaps ahead of us, and we try to faithfully and honestly hint at how one may be able to "mind those gaps", and suggest a few important intermediate steps along the way. I have been impressed by the ease and efficiency with which we have been able to put this together. Warm thanks to John for the great collaboration and wonderful team work.

Decoding + state preparation is all you need for Hamiltonians scirate.com/arxiv/2510.079… In 2024, we learned that classical optimization problems reduce to decoding. Do quantum optimization problems also yield to this technique? Are Hamiltonians hiding useful code structure?!

The infectious enthusiasm of my wonderful co-authors (@RobertHuangHY, Soonwon Choi, @JarrodMcclean) drove this upbeat assessment of “The Vast World of Quantum Advantage." arxiv.org/abs/2508.05720

@ZimingLiu11 @AToliasLab @Stanford Congrats Ziming! 🥳

I’ve defended my PhD thesis! 🥳 Next step: I’m excited to join @AToliasLab at @Stanford as a postdoc. Looking forward to meeting old and new friends in CA!

@hailceasa ✊🏽

@JohnMMartyn The people’s champ!

It’s been a very happening first half of the year— happy to share that I’ve defended my thesis, graduated, and celebrated with an expedition to the Arctic! 🥳

@sturmerph Thanks Philipp!

@JohnMMartyn Congratulations!

@ashlyyy_webb Thank you!

@JohnMMartyn congratulations!!

@jack_ceroni Appreciate it Jack, best of luck starting your PhD!

@JohnMMartyn Congratulations John, that's awesome

@Shraddha9511 Great to see, congrats Shraddha!

Thrilled to share my PhD thesis is now public! 📖 📎 proquest.com/docview/322535… Aside from an introduction to quantum computing in CV and DV architectures, some unpublished ideas include:

Here is an article research.ibm.com/blog/dmitry-kr… written by @kimmartineau @IBMResearch about @DimaKrotov and his works on Dense Associative Memories or modern Hopfield Networks, which extend the works done by the great @HopfieldJohn. It tells a wonderful story on how Hopfield Networks were revitalized. Check it out! Shoutout to @Ben_Hoov and @Leokoz8, who are also in the article.

I'm often asked if I'll redo the 2019 quantum factoring estimate. Denser storage by yokes, smaller magic factories by cultivation, slimmer approx arithmetic by Chevignard et al… surely the cost is lower now? Yes, it's lower now. security.googleblog.com/2025/05/tracki… arxiv.org/abs/2505.15917

Take a look at our latest work on non-abelian quantum signal processing: arxiv.org/abs/2504.19992 Excited to share that I’ll be presenting it at #TQC_2025 this year!

The most viewed video I've ever made is a short about two colliding blocks computing π. I just made a new edition of the explanation for why π shows up there, setting things up for a (coming soon) follow-on connecting it to quantum computing. youtu.be/6dTyOl1fmDo

🛠️ if interested in recent designs for fault-tolerant quantum computing, stop by our session Thursday March 20th 3:12-3:48 PM :) lnkd.in/gXiX_Q7G

Attention quantum signal processing (QSP) ⚛️ and Python 🐍 enjoyers: emerging from radio-silence to say that the pyQSP package 📦 (github.com/ichuang/pyqsp) has been overhauled to greatly improve numerical stability, incorporate better phase-finding methods, and be easier to use!

Pulling from the archive here, the triangle of power

It turns out optimal transport is easy! The "Spectral EMD" lets you compute optimal transport quickly and exactly in closed form -- incredibly useful for QCD and collider physics! arxiv.org/abs/2410.05379

BREAKING NEWS The Royal Swedish Academy of Sciences has decided to award the 2024 #NobelPrize in Physics to John J. Hopfield and Geoffrey E. Hinton “for foundational discoveries and inventions that enable machine learning with artificial neural networks.”