Have you tried typst? I just recently found out about it. Looks like overleaf, collaborate for free and instant compilation! Changes appear on the pdf in real time as you type (really awesome!)
Just discovered a Pennylane tutorial on our work on calibrating gates on superconducting devices 🩷
pennylane.ai/qml/demos/tu...
Stoked to see our work on Krylov Quantum Diagonalization (KQD) in print in Nature: nature.com/articles/s41...
We also put together an amazing tutorial to get walk you through the method and run experiments on IBM Quantum's hardware!
quantum.cloud.ibm.com/docs/en/tuto...
I try to avoid spam by only looking at the "following" feed and unfollow accounts that are spamming content I don't care about
Seeing some feedback from people trying to click the link, I realized I forgot an important caveat! The example is available only to IBM Quantum Network members because of the high usage of QPU time.
If there’s high interest I can try to get it to be available for open plan users too!
See my answer here: bsky.app/profile/mirk...
If there’s high interest I can see what I can do about making it open! (Even though you’d run out of QPU time if you tried to reproduce the entire example with an open plan account)
1. It’s easy to fix but 2. Not so much. Tutorials that have a QPU usage beyond the 10minutes allocation that is given for free are accessible only to users who belong to the network (thus have more usage available). Sorry, I should have included a disclaimer!
Sorry, I think I know what’s going on. There may be two things happening:
1. Need to login to your account first
2. The account is not in the IBM Quantum Network
Oh cool, ai’ll try to get venv working and see if that works for me too! Thank you! :D
No worries :) I remember giving uv a shot but getting stuck at not being able to let the editor find the virtual env
Do you have any idea on its compatibility with Jupyter notebooks?
For anyone interested in getting their hands dirty, we have a tutorial which implements the long-range CX with dynamic circuits from the paper here:
learning.quantum.ibm.com/tutorial/lon...
1/n Excited to share the IBM Quantum blog featuring our recent PRX Quantum paper:
"Efficient Long-Range Entanglement using Dynamic Circuits."
🚀 Why does this matter?
Quantum processors are limited by local connectivity, but our research demonstrates how dynamic circuits—leveraging mid-circuit
Realizing the feed was full of stuff I wasn’t interested in seeing (political discourse at that moment) and ads
Haven’t decided whether this is good or bad press for IBM Quantum lol
I would lean towards being a way to represent time-evolution of a quantum system. Tensor network being a particular representation of it
I’ve been interested in this for a while but haven’t found the motivation to start learning a new thing from scratch. Do you know of any good resources to get started? Particularly in the direction of quantum info
Quantum stocks: brrrr
Derek Parfit argued that infinitely many may be a preferable answer. I found his essay really intriguing, one of the few times philosophers use logic to try to answer a difficult question for modern science
www.sfu.ca/~rpyke/cafe/...
I thought you can use it to calculate vibronic spectra and also graph properties (dense subgraph, max cliques)
The barrage of news today reminded me that we do have a solid demonstration of a quantum computer doing something beyond classical (random circuit sampling). Left me wondering whether any applications have been found. After all, its photonic cousin (Gaussian boson sampling) seems to have a few!
Not sure what it is, but there’s nothing better like untangling a good knot lol
Redwood forest (such a magical place!), Alaska (so wild!), Himalaya (of course lol)
I’m eager to hear people’s opinion about this here! Especially if you can point me to more works in the literature that go along these lines!
As much as I appreciated these insights, I’m still left wondering whether more can be said on the topic. I get the feeling this is going into the right direction in showing how quantum systems are more powerful than classical systems for specific tasks
This is just some of the interesting things spelled out in the thesis. The work goes into great details to show these results. It also goes through step by step examples demonstrating these concepts when applied to language modeling.
This wouldn’t work with the usual way to represent probabilities with sets! Once you marginalize, you lose all information about the rest of the system.
And their spectral information can be used to reconstruct the full probability distribution! (I think the caveat here is that we started with a classical distribution encoded into a quantum state)
This uncovers many interesting features! You can compute marginals of the probability distribution (reduce densities) without losing all information about the original system. In fact, the offdiagonals of the reduced densities retain information about the interaction of the subsystem with the rest
