Emilio Onorati

It has been a great pleasure teaming up with Calvin (Yinchen) Liu, James Seddon, @TamaraKohler4 and Toby Cubitt for this wonderful collaboration at @PhasecraftLtd!

We have upgraded our Lindbladian-extraction algorithm with a few extra features: - A projection method to guide reconstruction of Markovian generators. - Gate set tomography to filter out SPAM errors. It is rewarding to see this method evolve! 🧬 scirate.com/arxiv/2507.079…

We're looking for a new addition to our group. Drop me a message if you're interested!

Many thanks to @CambyseRouze , @dsfranca and @JamesDCWatson , it has been great working together on this project!

I am very happy to share that our work on predicting phases of matter is now published in Nature Communications! 😀 nature.com/articles/s4146…

@HyperboIeva Warmest congratulations!! 🎓🥳

#QTech2024, a major conference on #quantumtechnologies, will take place in Berlin on September 10-12, 2024. The objective of the conference is to present the latest developments of quantum technologies in the domains of quantum communication, computation, simulation, sensors and metrology, both in their implementation using various platforms from atoms and ions to solid states, superconducting circuits and optics, as well as in their conceptual and theoretical foundations. Invited plenary speakers include • Antonio Acin (ICFO) • Markus Aspelmeyer (Vienna) • Mete Atatüre (Cambridge) • Stacey Jeffrey (QuSoft Amsterdam) • Richard Kueng (Linz) • Misha Lukin (Harvard), tbc • Thomas Monz (Innsbruck), tbc • Maria Schuld (Xanadu) • Christine Silberhorn (Paderborn) • Andreas Wallraff (ETH), tbc • Jelena Vuckovic (Stanford) • Stephanie Wehner (Delft) and others. The meeting is run and administered by @premc and is associated with #BERLINQUANTUM and the @ERU_Quantum. The academic board is led by @jenseisert , Arno Rauschenbeutel and @phfaist. See the web page lnkd.in/dNnitRDf.

My @QIPConference talk about proving efficient Machine Learning of thermal & more general phases of matter is online: bit.ly/Provable-Machi… Thanks to all the QIP organisers for a fantastic conference, and my wonderful collaborators @EmilioOno @dsfranca @CambyseRouze!

🐈If you are in Berlin, mark your calendar for MQV member @jenseisert's talk on cats, quanta and computers. The presentation promises to be quite exciting! 📅6 June, @UraniaBerlin

And most important: many many thanks to my Collaborators: @jonaskitzinger, @jonashelsen, Marios Ioannou, Albert Werner, Ingo Roth and @jenseisert !! Working together is always an incredibly valuable experience!

The core mathematical ingredient for our scheme is a connection between 'hybrid classical-quantum' objects (like frames for shadow estimation!) and randomized benchmarking, in the fashion of Fourier transforms on irreducible representations. I surely found this link fascinating!

Shadow estimation is surely a handy quantum learning protocol! Unfortunately, it cannot escape the influence of noise 🫨 But there are ways around it! We present a scheme to self-calibrate classical shadows using randomized benchmarking measurements: scirate.com/arxiv/2403.047…

I'm also looking for PhD students! Interested candidates can contact me anytime. A deadline or start date is not fixed for now, but the latter can be arranged relatively soon. Please let interested students know!

@TamaraKohler4 @dr_qubit @UCLQuantum Our work on characterising noisy dynamics is now published in Quantum! The output of our algorithm is the best-fit Lindblad generator to approximate tomographic data. This is suited to accurately assess errors for a wide range of quantum operations doi.org/10.22331/q-202…

Noise is currently one of the main obstacles towards reliable quantum computation. In this joint work with @TamaraKohler4 and @dr_qubit at @UCLQuantum we develop algorithms built on convex-optimization to detect and quantify non-Markovian noisy effects. scirate.com/arxiv/2103.172…

The framework imports the description of Pérez-García and Coser, where states in the same phase are interlinked by rapid Lindbladian evolutions. This allows us to remove the assumption of 'exponential decay of correlations', and naturally includes gapless phases and mixed states.

Can we predict properties of *any* state in a given phase, once we have collected just a few samples from this phase? Yes! And we can do so according to a comprehensive and natural framework of dissipative dynamics! Here all details --> scirate.com/arxiv/2311.075…

@nikobreu This is awesome!! Warmest congratulations, Niko!!🏅🎊

I am very happy to share the news that I have been awarded the Maxwell Medal and Prize. Many thanks go, of course, to my fantastic teachers and collaborators!

@RichardKueng Warmest congratulations for these exceptional and fully deserved achievements, Richard!! 🎉

Although I should know better, I have decided to toss two heavily biased coins over the past year: (🇦🇹) the *FWF START coin*,success chance: ~5%; (🇪🇺) *the ERC Starting coin*, success chance: ~15%. Still can't believe that *both were successful*. 😯😌🥳 What are the odds? 🤔🤔

I find this versatility so fascinating, basically moving all the heavy work to the classical post-processing stage! And it leads to a wide range of applications, e.g. cross-talk tomography and spam-robust channel reconstruction.

Our work on shadow estimation for quantum gate sets is now published in Nature Communications! We show how we can extract information of different type just by tuning the classical post-processing, but still using the same data from quantum measurements. nature.com/articles/s4146…