Excited about this collaboration with Oxford Ionics. LDPC + trapped ions is a very promising path to fault tolerance!

Excited to announce that I’ve started a new position as a Member of Technical Staff @IcebergQuantum!

New paper outlining my Julia package QuantumACES now out in the Journal of Open Source Software! https://t.co/PXO9hUt0Y9

For more, check out my new paper with @AC_Doherty and Robin Harper! And look forward to some experimental results soon :) https://t.co/igjwMRw1MD

Stim and PyMatching make this super easy. Characterise a circuit-level Pauli noise model with ACES, throw the noise estimates into your Stim circuit, and then it all just works—thanks @CraigGidney and @oscarhiggott! Code for this is now in QuantumACES: https://t.co/Cdinl9c3IU

Yes! Gate times in superconducting architectures indicate that ACES noise characterisation experiments performed and processed in just seconds should suffice. At tens of seconds, ACES noise estimates are nearly indistinguishable from the true noise model for decoding.

This means the reduction in logical error rates from noise-aware decoding increases exponentially with the code distance. While gains are limited for small codes, they're substantial for large ones. But is noise-aware decoding practical at the scales where it's most helpful?

Why characterise noise in syndrome extraction circuits? One reason: directly improving quantum error correction! In simulations of the surface code, we find that noise-aware decoding—calibrating the decoder with noise estimates—improves the code's error suppression factor.

My first paper—with @AC_Doherty and Robin Harper—is now out in PRX Quantum! More to come soon :)

📣 Announcement! New grant awarded to Evan Hockings for the project QuantumACES.jl. 📄 Learn more about the project here: https://t.co/DR67jVHgQH 🎓 Learn more about our UF Grants here: https://t.co/ogvBrwKjFq 📝 Apply for a microgrant here: https://t.co/GK4BXitiNy

My brief thoughts! https://t.co/lUhKmE2KHf

I recently read @hannu's The Quantum Thief trilogy, books which are wonderfully rewarding for those few familiar both with singularitarian thought and quantum information theory. I loved them, and maybe you will too, so I wrote a few words on them which I've linked below.

For more, check out my new paper with @AC_Doherty and Robin Harper! https://t.co/FqxaNIsNeb

So we optimise the design of our noise characterisation experiments, where that's tractable, then apply them at scale. This lets us simulate noise characterisation of the syndrome extraction circuit of a distance 25 surface code with 1249 qubits—in a few hours on a laptop!

But why the focus on sets of rearrangements? Because a set that efficiently estimates noise for the syndrome extraction circuit of a distance 3 surface code does so across all code distances—and we can precisely predict the scaling!

The ones that most efficiently estimate the noise, of course! So we derive a figure of merit for a set of rearrangements (evaluated with respect to some average error rates) and use that to optimise the set. Improvements persist even as the error rates or noise models differ!

We need quite a lot of data to learn the noise on each gate in the circuit. To get it, we learn the noise on circuits constructed by *rearranging* (with replacement) the *layers* shown in the original circuit. So what set of rearrangements should we pick?

In fault-tolerant quantum computers, physical circuits implement encoded logical operations. Typically, the bulk of these physical circuits are syndrome extraction circuits, such as this one for the surface code. This makes them a key target for noise characterisation!