Mikel Sanz

Over the recent weeks and months, John Preskill and I sat down to think about where we are in quantum computing. While the noisy intermediate-scale quantum (#NISQ) era is just unfolding as we speak, the time seems right to look ahead to the next steps to come.

scirate.com/arxiv/2510.1...

(I/III) We're excited to announce a new tenure track opening! The position is called 'quantum informatics' and is affiliated with our QUICK group within the CS+AI division at @jku.at 🇦🇹. Application deadline is November 30th, 2025: www.jku.at/en/the-jku/w...

This week we had the pleasure of hosting @mvscerezo.bsky.social in our group. We enjoyed tons of fun, engaging discussions, and progress on several projects. Looking forward to having you back soon! 🚀

#IPAM (the institute for pure and applied mathematics) is facing a critical shortfall for operating expenses due to an unexpected suspension of NSF funding www.ipam.ucla.edu/news/nsf-fun... . Donations for emergency continuity of operations funding can be made at

giving.ucla.edu/Campaign/Donat

go.bsky.app/75SvR2M - Who am I missing?

~got bored this afternoon, do forgive me. But on a serious note would love to know who I haven't discovered on here yet (or mistakenly omitted due to the sheer amount of people)

🔗 journals.aps.org/prresearch/a...

🏫 @ehu.eus @tecnalia.bsky.social BCAM

💶 OpenSuperQPlus @quantumspain.bsky.social

🚀 Excited to share our latest work "Neural quantum kernels: Training quantum kernels with quantum neural networks", published in #PhysicalReviewResearch, on advancing quantum machine learning with neural quantum kernels! Great work by Pablo Rodriguez-Grasa and great collaboration with Yue Ban!

Great work by Javier González-Conde, @dylanle.bsky.social and Sachin S. Bharadwaj!

@ehu.eus BCAM Ikerbasque @nquirec.bsky.social OpenSuperQPlus @quantumspain.bsky.social

📊 We divide the N-Re space into 5 regions — from guaranteed efficient quantum simulations to zones where we’re out of luck and it seems that efficiency is only provable when Re is small. In other words, we give a new insight into when quantum computers might beat classical ones at simulating fluids.

That scale tells you how fine your simulation grid needs to be to catch all the dynamics. Too coarse? You miss key physics. Too fine? You are wasting resources. The result is a mapping between:

Number of grid points N 🧮

Reynolds number Re 💨

Quantum efficiency 🔮

💡 Turns out, it's not just about the math, it’s about the physics. Specifically, the Reynolds number 🌀 (Re), which characterizes how turbulent a flow is, becomes central to estimating the efficiency by relating QCL to the Kolmogorov scale, the smallest length scale in turbulent flows.

Quantum Carleman Linearization (QCL) is a method that transforms nonlinear PDEs into (truncated) linear systems. Why linear? Because quantum computers are REALLY good at solving linear systems exponentially fast in some cases!⚡
But when is this QCL method actually efficient❓❓

🔥 New paper: "Quantum Carleman linearization efficiency in nonlinear fluid dynamics" in #PhysRevResearch. doi.org/10.1103/Phys...
Fluid dynamics is hard when things go nonlinear: turbulence, shocks, and chaotic flows. Solving it requires huge computational resources. Could quantum computing help?

The non-Clifford cost of random unitaries

scirate.com/arxiv/2505.1...

In this work, we explore the ensemble of t-#doped random quantum #Clifford circuits on n qubits, consisting of Clifford circuits interspersed with t single-qubit non-Clifford gates.

Impact and mitigation of Hamiltonian characterization errors in digital-analog quantum computation Digital-analog is a universal quantum computing paradigm which employs the natural entangling Hamiltonian of the system and single-qubit gates as resources. Here, we study the stability of these proto...

I’m happy to announce the results a collaboration with Adrian Franco Rubio, Alatz Alvarez Ahedo and Mikel Sanz, “Impact and mitigation of Hamiltonian characterization errors in digital-analog quantum computation”, arxiv.org/abs/2505.03642. The title is self explanatory, but there is more to it

Using illustrative examples, we compare these bounds to both the traditional Cramér-Rao and Bayesian approaches, offering insights into when and how each method is valid or useful for quantum parameter estimation.

To address this limitation, we focus on hashtag#Bhattacharyya bounds, which are more robust when prior knowledge is imprecise. These bounds incorporate additional mathematical constraints, making them potentially more reliable in practical scenarios.

This work explores fundamental limits on how precisely we can estimate unknown parameters in quantum systems. While the Cramér-Rao bound sets a lower limit on the mean square error of an estimator, it assumes that we already have highly accurate prior knowledge of the parameter.

New article published today in #PhysicalReviewResearch entitled "Existence of unbiased estimators in discrete quantum systems" with Javier Navarro Navarro and Ricard Ravell Rodríguez.

link.aps.org/doi/10.1103/...

@upvehu.bsky.social @nquirec.bsky.social

I’m also really glad you’ll be staying with us for another year as a postdoc to wrap up the many projects we have underway together.

A big thank-you as well to the panel Mario Berta, Erik Torrontegui and Sofía Martínez-Garaot for their time and for the excellent questions.

@upvehu.bsky.social

Today, Javier Gonzalez-Conde defended his thesis, “Quantum Algorithms for Differential Equations in Finance and Physics.” It’s been a long journey—at times a tough one—especially with the pandemic lockdown hitting just as he joined the group. But the work has been outstanding. Congratulations!

This paper marks the beginning of a new (and hopefully long-lasting) theoretical-experimental collaboration with David Novoa and his outstanding group focused on developing quantum technologies based on the hollow-core optical fiber platform—a field that remains largely unexplored to date.

We propose here the first quantum model of light-matter interaction in gas-filled hollow-core fibers. We show that, in the semi-classical limit, the model recovers the traditional equations, while also enabling the prediction of entanglement dynamics during the Raman transduction process.

Our new paper "Entanglement transfer during quantum frequency conversion in gas-filled hollow-core fibers" was published this week in APL Photonics. Great work by Tasio Gonzalez-Raya (as usual), Arturo Mena López and Miriam Lazo. doi.org/10.1063/5.02...
@upvehu.bsky.social BCAM Ikerbasque

Quantum approximated cloning-assisted density matrix exponentiation, Pablo Rodriguez-Grasa, Ruben Ibarrondo, Javier Gonzalez-Conde, Yue Ban, Patrick Rebentrost, and Mikel Sanz @pablones8.bsky.social @conzavin.bsky.social
@qmisanz.bsky.social @nquirec.bsky.social #Quantum
Link below ⬇️

In this paper, we propose a method to circumvent this limitation by introducing imperfect quantum copies, which significantly improve the performance of the LMR when the eigenvectors are known.

The Lloyd-Mohseni-Rebentrost (LMR) protocol allows for implementing matrix exponentiation when copies of a quantum state are available. However, in a realistic scenario, the copies are limited and the noncloning thm prevents one from producing exact copies to increase the accuracy of the protocol.

New paper published today in Phys. Rev. Research doi.org/10.1103/Phys... “Quantum approximated cloning-assisted density matrix exponentiation”. Congrats to @pablones8.bsky.social Rubén Ibarrondo, J. González-Conde, Y. Ban and P. Rebentrost! @upvehu.bsky.social #BCAM #Ikerbasque

Last fall I taught two big classes on **computational complexity** (left selfie) and **quantum computing** (right selfie). We had a blast! Lecture notes are here: www.jku.at/fileadmin/gr..., www.jku.at/fileadmin/gr....
It was a lot of work, but now I am happy+proud to share them. Feedback welcome!

Congrats!!