🔗 journals.aps.org/prresearch/a...

🏫 @ehu.eus @tecnalia.bsky.social BCAM

💶 OpenSuperQPlus @quantumspain.bsky.social

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❓❓

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.

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

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.