Rajiv Krishnakumar

Does this mean the coin flipping guys get to publish a paper too?

(Sidenote: I am unironically a big fan of trickshot videos. The joy in their faces when they finally get it is fantastic!)

I think a way to fight blog posts is to preach to non-experts to

1. Find the link to the scientific paper in the blog post
2. Upload just the paper (not blog post or any other media) into NotebookLM
3. Ask NotebookLM your questions

It's not perfect but (so far) it's pretty decent!

YouTube video by A Day in the Life A Day in the Life of a Quantum Information Science Professor with Prabha Mandayam | Ep. 5

Very nice interview with Prof. Prabha Mandayam where she shares with us some fascinating life experiences and insights, like how she got into quantum, what she enjoys most about her job, recent QEC breakthroughs, and more! Very fun, informal and a relaxing listen :)

www.youtube.com/watch?v=nejx...

Super interesting! Are there/will there be any talks on this available on youtube?

Nice one 🤣🤣 alternatively you need to search through 3 items

Thanks Nick, appreciate the shout 🙂 we're hoping it can entice more people into digging deeper into how to move forward in understanding which (parts of) problems in healthcare/lifesciences quantum algorithms could be have a potential to help out in!

Glad to see our work on potential applications of quantum computing to biomarker discovery out in the @cellpress.bsky.social Patterns journal :)

www.cell.com/patterns/ful...

Looking forward to it coming out!

It's a word play on "hope"

So your conclusion is that "this suggests that the firewall paradox may be a symptom of the same fundamental issue that leads to the extended Wigner's friend paradox" is bullshit? Or am I misunderstanding?

Pilot wave theory - Wikipedia

Indeed! I think that's a pretty OG one en.m.wikipedia.org/wiki/Pilot_w...

Oh I didn't know this was an additional feed I could add, this is really neat!

It's similar on my timeline (not with Tesla specifically but a lot of Trump related posts). My solution is to just stick to the "Following" tab and ignore the "Discover" tab. Unfortunately I only discover new accounts when people I know repost them, so exploration is slow but I'm OK with it for now

Genuine question: what makes this paper bullshit? You often times point to a quote or give like a two-word context so happy to get that from you this time as well (I have no opinion or stake in this paper, just curious)

Extreme Learning Machines for Attention-based Multiple Instance Learning in Whole-Slide Image Classification Whole-slide image classification represents a key challenge in computational pathology and medicine. Attention-based multiple instance learning (MIL) has emerged as an effective approach for this prob...

(8/8) Model complexity reduced by a factor of 5, making it more efficient and scalable.

Read more arxiv.org/abs/2503.10510 & share your thoughts!

We welcome feedback, questions, and collaborations. Stay tuned for more insights from our work at QuantumBasel!

(7/8) 2.     We apply parameter freezing, inspired by extreme learning machines, to improve generalization and reduce model complexity.

📊 Essential Outcomes:

AUC increased by over 10%, demonstrating superior detection accuracy.

(6/8) each labeled as containing no CRCs (0) or at least one CRC (1).

How have we improve deep MIL models?

1.     Instead of using raw feature vectors, we aggregate high-dimensional hidden layer outputs—enhancing representation quality.

(5/8) Our work looked at detecting a specific CRC type (erythroblasts), which is linked to inflammation, hypoxia, cancer infiltration, and increased mortality risk.
We used individual blood cell images from different patients to create whole slide images (WSIs) of blood —

(4/8) 🔍 Understanding Our Approach

What is the challenge?

In multiple instance learning (MIL), individual data points (e.g., single cells) often lack labels, but “bag-level” labels (e.g., entire blood samples) are more accessible.

(3/8) - More efficiency: We reduced the number of trained parameters by a factor of 5.

- Future potential: We outline a possible pathway for leveraging quantum algorithms to further enhance MIL-based AI models.

(2/8) for the early detection of circulating rare cells (CRCs) in blood—key indicators of various biological and pathological conditions, including various cancer diseases.

Key results:

- Performance boost: Across multiple metrics - including AUC by over 10%.

Extreme Learning Machines for Attention-based Multiple Instance Learning in Whole-Slide Image Classification Whole-slide image classification represents a key challenge in computational pathology and medicine. Attention-based multiple instance learning (MIL) has emerged as an effective approach for this prob...

(1/8) New Preprint: Advancing AI for Detecting Circulating Rare Cells (CRCs) – A Collaboration Between Moonlight AI & QuantumBasel

arxiv.org/abs/2503.10510

📜 In this paper, we demonstrate an improvement of existing attention-based deep multiple instance learning (MIL) AI methods

Same sentiment here! Really enjoyed this video and as someone in the field of quantum computing but a complete non-expert in Majorana qubits, I really appreciated the time and effort taken by @spinespresso.bsky.social and @vincentmourik.bsky.social to break this all down for us 🙏🏾

Oh no I'm sad to miss out! Will you be putting up the recording on youtube?

YouTube video by QIndia A Day in the Life of a Co-founder and CEO of a Quantum Start-up with Kanav Setia

(Re-)introducing A Day in the Life, our podcast where we chat with all sorts of quantum folks to get a peak into what life is like in their shoes, their thoughts on quantum stuff, and more!

Thanks to Kanav Setia from qBraid, it was a blast! www.youtube.com/watch?v=kiQh...

And also practically it's a business matter too - even if you get people's attention in the short term, if you can't back it up with a real deliverable, the trust will continue to dissappear along with funding and eventual customers

Perhaps! My reading from the Google announcement is that they believe that quantum computers will be useful for one or more real-world tasks in 5 years, but let's see! Also interesting take on AGI, let's see about that too (no strong opinions on my side!)

Not at all a stupid question! But unfortunately we (i.e. the whole quantum community) are still far away from running any real QLLMs on a quantum computer - not enough qubits and too noisy. I'm not an expert in QLLMs but I imagine we will need something like millions of qubits

Thanks for the reply! I tried to sign up again and I got this message... so I'm not sure if it's gone through or not

QuantumBasel - Center of Competence for Quantum Computing and AI Find out about QuantumBasel's latest job offers.

We're looking for a quantum algorithms researcher to join our team at quantumbasel! quantumbasel.com/about/jobs - @quantiki.bsky.social I have asked to create a new account to post the annoumcement on there but I'm not sure how long it will take to get approval so letting you know about this here :)