Zoe Holmes
@qzoeholmes.bsky.social
970 followers 790 following 65 posts
Quantum physicist. Assistant Prof at EPFL. Climber.
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qzoeholmes.bsky.social
This is the paper: scirate.com/arxiv/2510.0...

Thanks for the fun collaboration Sasha (
@sheffield-qc.bsky.social
) and Chiddy !
scirate.com
qzoeholmes.bsky.social
FYI, our results here don’t contradict arXiv:2312.09121 which focus on loss estimation, proofs, and continuous protocols

Of these, the most intriguing/significant is the switch to discrete optimization

& maybe the path to adaptive quantum advantage is all about finding those discrete sweet spots 😉
qzoeholmes.bsky.social
And we have found one… we provide numerical evidence that our problem lives in the Goldilocks zone:

- trainable (no exponential concentration)

- not classically surrogatable (thanks to high entanglement + magic)
qzoeholmes.bsky.social
Of course, this being quantum, we face two extra demons…

- Exponential concentration (barren plateaus + shot noise)

- Classical surrogation (can classical shadows fake the landscape?)

For a real separation, we need a sweet spot that dodges both.
qzoeholmes.bsky.social
More concretely, we show that for a range of moderate entangling strengths the landscape is unimodal but non-separable landscapes.

Then numerics show adaptive hill-climbing converges efficiently

But non-adaptive approaches - blow up exponentially.
qzoeholmes.bsky.social
We translate that logic into a quantum recompilation task

The hidden string = the placement of T-gates between layers of semi-random unitaries

Goal = uncover the T gates positions

As in LeadingOnes, identifying early T-gates helps you make progress, but you can’t optimize each gate independently
qzoeholmes.bsky.social
This is the canonical “adaptivity pays” task

Its unimodal (no local minima) but non-separable (each bit cannot be trained independently)

- Adaptive strategies can flip one bit at a time, use the feedback, and find the string in O(n) queries.

- Non-adaptive strategies need exponentially many.
qzoeholmes.bsky.social
Our task is a quantum twist on the classic LeadingOnes-OneMax problem.

In this problem you're trying to learn a hidden bitstring.

Your score = how many leading bits match the target before the first mismatch.

So 1110… matches 1101 better than 1011… even if they have the same Hamming weight.
qzoeholmes.bsky.social
We provide evidence of an exponential gap between adaptive & nonadaptive strategies for a quantum recompilation task

Key takeaways:

- Entanglement isn’t always a roadblock: its degree can aid training

- Discrete optimization may be key to finding sweet spots between concentration & surrogation
Reposted by Zoe Holmes
decodoku.bsky.social
Tenure track assistant prof job up for grabs in Basel. It's quantum computing theory, based in computer science, and part of the NCCR SPIN.

Whoever gets this job will probably have to collaborate with me. Whether you see that as a pro or con is up to you.

jobs.unibas.ch/offene-stell...
Universität Basel: Tenure-track Assistant Professorship in Theoretical Quantum Computing
The University of Basel, Switzerland, invites applications for a professorship in theoretical quantum computing. The University of Basel is the home institution of the Swiss National Center of Compete...
jobs.unibas.ch
qzoeholmes.bsky.social
Also, sharing for friends:

Anyone fancy a (minimal teaching load) tenure track position on quantum computing on a sunny island 🌤️ 🏝️? The Cyprus Institute are also hiring a tenure track assistant proff on quantum algorithms.

onlinerecruitment.exelsyslive.com?c=6E7274A2-8...

(Deadline next week)
Exelsys - Online Job Application
onlinerecruitment.exelsyslive.com
Reposted by Zoe Holmes
qzoeholmes.bsky.social
@joachimfavre.bsky.social also has a bunch of cool projects being written up - and is currently looking for a PhD - so I recommend following him :)
qzoeholmes.bsky.social
I highly recommend @joachimfavre.bsky.social’s typed lecture notes for my quantum info course and on (quantum) computer science more generally 👇

(To give credit where it’s due my quantum info lectures built in turn on @davidbjennings.bsky.social’s brilliant quantum info lecture notes)
joachimfavre.bsky.social
I recently published the LaTeX notes I took in three amazing classes this semester:
- Computational quantum physics (Prof. @gppcarleo.bsky.social)
- Quantum information theory (Prof. @qzoeholmes.bsky.social)
- Sublinear algorithms for big data analysis (Prof. Michael Kapralov)
Link below👇
Two-panel drake meme, titled "Morris' algorithm be like".
Top panel: Drake looks away, disapprovingly, next to the legend "Counting to n in O(log n) space".
Bottom panel: Drake points approvingly at the legend "Counting to n in O(loglog n) space.
qzoeholmes.bsky.social
Hey! We've put out a paper discussing some pitfalls in attempts to avoid barren plateaus/exponential concentration in variational quantum algortithms and QML.

🧵👇
qzoeholmes.bsky.social
thanks! very helpful - will add.
qzoeholmes.bsky.social
We thank the reviewer for bringing this citation to our attention...
qzoeholmes.bsky.social
( And finally finally - if there are any citations you think we've missed - observations that are somewhat analogous to ours hidden in older papers/appendices please do shout! )
qzoeholmes.bsky.social
Why should you care about any of this?

In all honesty, we partially just think these are cool observations.

But also we need more quantum algorithmic primitives but coming up with them is hard!

And we hope that maybe these thermodynamic/geometric insights could help with novel alg design?
qzoeholmes.bsky.social
Finally, we show that quantum signal processing can be used to implement imaginary time evolution for unstructured search without post selection.

And this enables us to design a new `fixed-point' quantum search algorithm

i.e., a Grover type algorithm that never overshoots the solution