This year's Quantum Computing Theory in Practice Conference (QCTiP) is scheduled for 04/20/2026-04/25/2026 in Oxford, UK π¬π§: qctipconf.github.io
Talk submission deadline is just round the corner: 01/11/2026. Looking forward to many exciting contributions and a great time in Hogwartsπͺ, aehm Oxfordπ.
Listening this song while writing my PhD thesis
Happy to share that our paper βComplexity of quantum tomography from genuine non-Gaussian entanglementβ has been published in Nature Communications!
www.nature.com/articles/s41...
Thanks a lot to my great coauthors Xiaobin Zhao, Pengcheng Liao, Ulysse Chabaud, and Quntao Zhuang!
Huge thanks to Filippo Girardi and @ludovicolami.bsky.social for this very efficient collaboration, Iβve really learnt a lot!
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It seems that this random purification channel is such a simple yet conceptually beautiful textbook result! I bet it will have many other applications in quantum information theory.
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We then use this result to obtain a one-line proof of the recently introduced Uhlmannβs theorem for quantum divergences (arxiv.org/abs/2502.01749).
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In todayβs paper, we give a very simple proof of this result, which makes new properties of this βrandom purification channelβ immediately clear.
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This result has already proved to be extremely useful in quantum learning theory (arxiv.org/abs/2511.15806). Their proof, however, is quite complicated, requiring many pages of hard-core representation theory.
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Recently, Ewin Tang, John Wright, and Mark Zhandry proved a beautiful result (arxiv.org/pdf/2510.07622): there exists a quantum channel that, given n copies of an arbitrary mixed state \rho, outputs n copies of a fixed random purification of \rho.
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Given a mixed state, one can always write down its purifications. However, the βpurification operationβ is unphysical, i.e. it cannot be realised via a quantum channel.
arxiv.org/pdf/2511.234...
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This is a publication I am extremely happy about. It is a bit rebellious, and yet it touches upon an old and important question: How can we learn an unknown continuous quantum state from data?
www.nature.com/articles/s41...
We prove that quantum state tomography is a lot harder than anticipated.
Itβs been almost two years that Iβve been constantly thinking about these kinds of questions, and Iβm very happy to continue this journey in the bosonic learning world!
I'm extremely happy about this paper, as it also opened many natural and promising research directions on "quantum learning theory with CV systems". Since its arXiv posting, we have been addressing several of these, but many others still remain unexplored.
Nature Physics has published our work!
nature.com/articles/s4156β¦
Thanks a lot to my amazing coauthors @antonioannamele.bsky.social , Lennart Bittel, @jenseisert.bsky.social, Vittorio Giovannetti, Ludovico Lami, Lorenzo Leone, @sfeoliviero.bsky.social !
So excited that my coauthors and I have three papers accepted at QIP this year!
- Optimising quantum data hiding arxiv.org/abs/2510.03538
- Efficient learning of CV Gaussian unitaries arxiv.org/abs/2510.05531
- Is it Gaussian? Testing CV Gaussian states arxiv.org/pdf/2510.07305
The only thing that could possibly be hotter than Italian twins explaining entanglement in Italian is Italian triplets explaining the GHZ state.
Happy to share our TEDx talk where me and my twin @antonioannamele.bsky.social chat about quantum technologies!
(Italian only)
youtu.be/yzXUsDUPt8A?...
It consists of this cute trophy, $3,500, and a fully funded trip to the Chicago Quantum Summit, an event that brings together academics, companies, and even politicians interested in quantum technologies.
Honoured to have received the Boeing Quantum Creators Prize at the Chicago Quantum Exchange event today!
This prize recognises early-career researchers who advance quantum information in new directions
π
A huge thanks to the great team: Filippo, Freek, Lennart, @sfeoliviero.bsky.social, David, and Michael. It was a wonderful collaboration!
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Iβm very happy to see that the entire toolbox of CV trace-distance bounds weβve developed over the past two years finds concrete applications in this fundamental task in CV quantum information.
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In this paper, we systematically investigate this problem, proving that testing Gaussianity can be done *efficiently* in the *pure-state* setting, but is fundamentally *inefficient* for general *mixed states*.
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Okay, last post on *quantum learning theory with CV systems* (for a few monthsπ«£)
Today's new work tackles another natural and central question in this rapidly developing field: Given an unknown CV state, how to test whether is it Gaussian or not?
arxiv.org/pdf/2510.07305
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Many thanks to my amazing coauthors: Marco, @vishnu-psiyer.bsky.social, Junseo, @antonioannamele.bsky.social! It was fun meeting at odd hours to sync between Europe, Asia, and the US, with our WhatsApp research group constantly active π€£
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This result is the symplectic analogue of the polar decomposition for nearly unitary matrices:
given a matrix X that is epsilon-close to an (unknown) unitary, the polar decomposition efficiently outputs an exact unitary matrix U that remains O(epsilon)-close to X.
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We also introduce a method that may be of independent interest:
Given as input a matrix X that is epsilon-close to an (unknown) symplectic matrix, our method efficiently outputs an (exact) symplectic matrix S that remains O(epsilon)-close to X.
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In our work, we carry out the first rigorous complexity analysis of learning Gaussian unitaries using a physically meaningful distance (the energy-constrained diamond norm), thereby proving that tomography of Gaussian unitary is efficient.
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Useful bounds on the energy-constrained diamond distance between Gaussian unitaries were recently proved by Becker, Lami, Datta, and RouzΓ© (arxiv.org/abs/2006.06659).
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