Particularly interesting to us was witnessing how slowly loop correlations build up in heavy-hex processors. Loop correlations are what make loopy networks potentially significantly harder to use than loop-free MPS and tree-tensor networks.

We also introduce a bunch of metrics to certify that the samples are of high-quality. This way, we verified that we solved the biggest circuit in IBM's recent quantum chemistry experiment to numerical precision.

We combine some existing ideas with ITensorNetworks.jl and @joeytindall.bsky.social's flexible boundary MPS code to adapt to any planar geometry.

With our open-source (but not completely polished) software, you can start simulating and sampling 2D circuits today: github.com/JoeyT1994/Te...

In case you thought you can't efficiently simulate and sample quantum circuits with 2D tensor networks... Nope, you can.

Link: scirate.com/arxiv/2507.1...

We simulate IBM's recent quantum chemistry experiments and Williow + heavy-hex Heisenberg dynamics, and showcase modern, verifiable techniques.

I don't see any reason why not every circuit executed on hardware should be compressed. The approach can also be used to re-compile into a different gate set or topology.

Classical simulation is not just here to compete with quantum devices.

Last week, we published a paper that really excites me:
"Circuit compression for 2D quantum dynamics"

Using Pauli propagation, we (Matteo) were able to compress Trotter circuits for systems up to 30x30 with depth reductions of x2 to x13.

Link: arxiv.org/abs/2507.01883

This paper was meant to go live yesterday (still love you arXiv), but who doesn't scroll social media on a holiday 💁

Thanks to my amazing group and co-authors Tyson Jones, Yanting Teng (@yteng.bsky.social), Armando Angrisani (@aangrisani.bsky.social), and Zoë Holmes (@qzoeholmes.bsky.social)!

Pauli propagation is naturally interfaced with both quantum computers and other classical simulation methods - the perfect team player!

I love improving classical algorithms for simulating quantum computations, and I truly believe performant classical methods are good for everyone.

This "compact" 30-page main text manuscript summarizes much of what we have learned about Pauli propagation (PP) and where its strengths lie.

From the general framework description, over theoretical guarantees, to the nitty-gritty implementation details that you are happy to not have to deal with.

❗New paper and open-source library❗

PauliPropagation.jl is your go-to library for simulating quantum circuits via Pauli propagation. Our paper provides a thorough overview of this new classical simulation method.

Paper: scirate.com/arxiv/2505.21606
Library: github.com/MSRudolph/PauliPropagation.jl

UnitaryHACK 2025 has begun - and we are part of it!

If you are registered, earn real money by closing GitHub issues in our new library PauliPropagation.jl (github.com/MSRudolph/PauliPropagation.jl).

We were supposed to have a nice and "compact" paper out today, but the arXiv gods were not with us.

Our Pauli propagation code can always simulate at least some part of the quantum circuit, and then be patched up with a quantum device.

Even on its own, our classical algorithm can accurately simulate 127-qubit problems in seconds, outperforming most modern quantum hardware.

A quantum device might be initially needed to collect measurements, followed by classical simulation. Depending on the problem, the quantum device needs to run more or less complicated circuits (or none at all).

We give average-case and worst-case guarantees for both the classical and quantum side.

Kicking off my existence on this platform with a paper release 🔥

We propose a framework for the quantum-enhanced classical simulation of small expectation landscape "patches".
In short: You can do more classically than you might have thought.

scirate.com/arxiv/2411.1...
#quantum