Getting to quantum machine learning

Thank you for tuning in to this audio only podcast presentation. This is week 95 of The Lindahl Letter publication. A new edition arrives every Friday. This week the topic under consideration for The Lindahl Letter is, “Getting to quantum machine learning.”

We are living on the edge of meeting the weekly Friday publishing deadline at this point. As we quickly approach the 104th post and the major two-year milestone I’m still working on the same Saturday and Sunday schedule of early morning writing. I’m just having to be extra mindful of making sure I don’t get sidetracked into working on other things. This week’s topic could be an entire book full of insight. People are certainly going to fill the shelves with quantum machine learning books in the coming years. It is certainly starting to turn into whole conferences and other gatherings of people interested in telling people all about and sharing stories of quantum machine learning. 

Nobody is really bringing a quantum computer to any of these events. These are not something that is going to fit in your car and be ready to head out to a conference event. Seriously, to the best of my knowledge no laptop or portable quantum computer exists at this time and if it did nobody is using it for machine learning. They would probably be taking it to conferences or other gathering to show people how delightfully wonderful it is to be able to carry around such a power computing device. I imagine that it will be like Steve Wozniak showing up and assembling an early homebrew computer club kit. Those were moments of endless possibility, delight, and wonder. Bringing back the chance at some type of epic moment like that is certainly something that could very well happen within this space. My money is on the team over at IBM making that happen at some point. 

Let’s rewind the coverage here for a moment and reflect on two of my previous Substack posts:

Week 42: Time crystals and machine learning (this one was epic)

Week 77: Is quantum machine learning gaining momentum?

You may have forgotten from the first paragraph that this is week 95 of The Lindahl Letter and quantum computing has received 3 different weeks of coverage. You can tell from that degree of focus that I believe it is a topic that will eventually change the nature of compute.  

All right let’s jump right into the best academic articles about quantum machine learning.

Biamonte, J., Wittek, P., Pancotti, N., Rebentrost, P., Wiebe, N., & Lloyd, S. (2017). Quantum machine learning. Nature549(7671), 195-202.

Schuld, M., Sinayskiy, I., & Petruccione, F. (2015). An introduction to quantum machine learning. Contemporary Physics56(2), 172-185.

I did enjoy this 35-minute YouTube video about quantum computing from TechTechPotato which is hosted by Dr. Ian Cutress, “Quantum Computing: Now Widely Available!”

Being able to see some of these videos with the crew from IBM make me feel better about this technology actually existing. This would be much easier to accept as science fiction. Within that video the part of the coverage that caught my attention the most was that really it was academics and some startups that were coding for practical use cases to do something with quantum computing. For the most part, the type of machine learning efforts that would be easily transition over into this type of compute are not called out super clearly. 

One of the things I’m considering for next year is maybe coding something simple up and running it on one of these IBM systems. To really get a handle on what is happening within this quantum computing system it feels like only some hands work will close the gap for me here. 

Links and thoughts:

“The Uses of IBM’s Next Generation 433 Qubit Chip”

“#036 – Max Welling: Quantum, Manifolds & Symmetries in ML”

This is a podcast from the folks who are in the room with Twitter… “E104: FTX collapse with Coinbase CEO Brian Armstrong + election results, macro update & more”

Top 5 Tweets of the week:



What’s next for The Lindahl Letter?

  • Week 96: Where are large language models going?
  • Week 97: MIT’s Twist Quantum programming language
  • Week 98: Deep generative models
  • Week 99: Overcrowding and ML
  • Week 100: Back to the ROI for ML

I’ll try to keep the what’s next list forward looking with at least five weeks of posts in planning or review. If you enjoyed this content, then please take a moment and share it with a friend. If you are new to The Lindahl Letter, then please consider subscribing. New editions arrive every Friday. Thank you and enjoy the week ahead.

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