Lightnews — Scholar-powered news

Armando Angrisani @aangrisani.bsky.social · 1d

This was a truly funny project, merging different backgrounds: CV quantum information (@Varun, @ulyssechabaud.bsky.social) and simulation of DV circuits (@qzoeholmes.bsky.social and myself). Looking forward to other enriching collaborations in the future.
In the meantime, check out our preprint:

When quantum resources backfire: Non-gaussianity and symplectic coherence in noisy bosonic circuits

Analyzing the impact of noise is of fundamental importance to understand the advantages provided by quantum systems. While the classical simulability of noisy discrete-variable systems is increasingly...

scirate.com

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Armando Angrisani @aangrisani.bsky.social · 1d

The intertwined action of non-Gaussianity, symplectic coherence, noise rate, and energy determine the boundary between quantum and classical regimes.
Balancing these quantum resources — not just reducing noise — will be key to future demonstrations of bosonic quantum advantage.

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Armando Angrisani @aangrisani.bsky.social · 1d

This interplay yields striking phase diagrams: in the blue regions, bosonic circuits are classically simulable with runtime linear in depth, ruling out quantum advantage. Intriguingly, both too low (bottom row) and too high (top and middle rows) cubic gate rates can destroy advantage under noise.

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Armando Angrisani @aangrisani.bsky.social · 1d

Using this tool, we uncover a surprising result:
quantum resources usually linked to bosonic advantage — non-Gaussianity and symplectic coherence — can actually make classical simulation easier in the presence of noise.

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Armando Angrisani @aangrisani.bsky.social · 1d

To this end, we introduce the displacement propagation algorithm—a continuous-variable analogue of Pauli propagation. It uses Markov chain Monte Carlo methods to propagate displacement operators through noisy circuit layers, revealing when bosonic circuits are classically simulable.

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Armando Angrisani @aangrisani.bsky.social · 1d

In recent years, bosonic platforms have surged in importance, powering advances in quantum error correction, quantum machine learning, and quantum chemistry.
But their infinite-dimensional, continuous-variable (CV) nature makes them far harder to analyze or simulate.

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Armando Angrisani @aangrisani.bsky.social · 1d

Can your quantum device still provide quantum advantage in the presence of noise? This is well-studied for spin systems, but less so for bosonic systems—photonics, superconducting resonators, or motional modes of trapped ions and atoms. We address this question in our new paper!

🧵⬇️Thread below

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Armando Angrisani @aangrisani.bsky.social · Jul 30

quantum-journal.org/papers/q-202...

Learning unitaries with quantum statistical queries

Armando Angrisani, Quantum 9, 1817 (2025). We propose several algorithms for learning unitary operators from quantum statistical queries with respect to their Choi-Jamiolkowski state. Quantum statisti...

quantum-journal.org

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Armando Angrisani @aangrisani.bsky.social · Jul 30

This work extends the theory of quantum statistical query (QSQ) learning to the task of learning unitary operators. One of the main advantages of this model is its robustness to noise — the proposed algorithms can handle both shot noise and certain systematic measurement errors.

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Armando Angrisani @aangrisani.bsky.social · Jul 30

Excited to share that this paper is now published in @quantum-journal.bsky.social !

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Reposted by Armando Angrisani

Dulwich Quantum Computing @dulwichquantum.bsky.social · May 29

Our suggestion for how to improve Fig 1.

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Armando Angrisani @aangrisani.bsky.social · May 29

Happy to see this finally on the arXiv! This paper provides both a self-contained review of Pauli propagation methods and an introduction to PauliPropagation.jl, a high-performance library developed by my teammates at EPFL

Manuel Rudolph @quantummanuel.bsky.social · May 29

❗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

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Reposted by Armando Angrisani

Manuel Rudolph @quantummanuel.bsky.social · May 28

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.

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Reposted by Armando Angrisani

Supanut Thanasilp @supanut-thanasilp.bsky.social · May 17

Once upon a time a myth in Quantum Reservoir Processing (QRP) goes by “more chaos = richer feature map = better”

Doomed by their own chaotic dynamics, QRP may not scale in the extreme scrambling limit.

Check out our new Star Wa… I mean paper on arxiv: scirate.com/arxiv/2505.1...

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Armando Angrisani @aangrisani.bsky.social · May 2

Thrilled to announce that our work on classical simulation via Pauli Propagation was accepted for an oral presentation at TQC! See you in Bengaluru!

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Armando Angrisani @aangrisani.bsky.social · Jan 29

If the noise is nonunital, there are at least two issues: (i) the output distribution is not anticoncentrated (arxiv.org/abs/2306.16659) and (ii) the noise creates additional Pauli paths, increasing the runtime of the previous sampling algorithm from polynomial to quasi-polynomial

Effect of non-unital noise on random circuit sampling

In this work, drawing inspiration from the type of noise present in real hardware, we study the output distribution of random quantum circuits under practical non-unital noise sources with constant no...

arxiv.org

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Armando Angrisani @aangrisani.bsky.social · Jan 29

Thanks :) Yes, Pauli-path methods can definitely be used for sampling from noisy random circuits, as shown in this pioneering work arxiv.org/abs/2211.03999. However, this result holds for local depolarizing noise and provided that the output distribution is anticoncentrated.

A polynomial-time classical algorithm for noisy random circuit sampling

We give a polynomial time classical algorithm for sampling from the output distribution of a noisy random quantum circuit in the regime of anti-concentration to within inverse polynomial total variati...

arxiv.org

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Reposted by Armando Angrisani

Marco Cerezo @mvscerezo.bsky.social · Jan 24

Another amazing paper from our Summer School student @antonioannamele.bsky.social (is that 3 papers already?!) and from our collaboration with the power house that are @aangrisani.bsky.social and @quantummanuel.bsky.social, from @qzoeholmes.bsky.social! 's incredible group.

arxiv.org/abs/2501.13101

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Armando Angrisani @aangrisani.bsky.social · Jan 24

Taken together, these works close a gap in understanding the link between barren plateaus and classical simulability. While non-unital noise enhances trainability by avoiding barren plateaus, we also prove it enables efficient classical simulation of generic circuits!

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Armando Angrisani @aangrisani.bsky.social · Jan 24

Our approach leverages a new Pauli Propagation algorithm, specifically tailored to this setting, which stochastically prunes the Pauli tree.

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Armando Angrisani @aangrisani.bsky.social · Jan 24

In arXiv:2501.13050, we relax the random circuit assumption by using a fixed ansatz with Clifford gates and Pauli rotations at random angles.

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Armando Angrisani @aangrisani.bsky.social · Jan 24

Check out the open-source library at github.com/MSRudolph/Pa...!

GitHub - MSRudolph/PauliPropagation.jl: A Julia library for Pauli propagation simulation of quantum circuits and quantum systems.

A Julia library for Pauli propagation simulation of quantum circuits and quantum systems. - MSRudolph/PauliPropagation.jl

github.com

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Armando Angrisani @aangrisani.bsky.social · Jan 24

Our approach is highly scalable, as shown by @quantummanuel.bsky.social’s numerics for a Hamiltonian variational ansatz on a 6×6 lattice (below) and real-time dynamics on an 11×11 lattice.

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Armando Angrisani @aangrisani.bsky.social · Jan 24

We demonstrate that paths with many high-weight Paulis can be truncated due to their negligible contribution to expectation values in random circuits. Intuitively, global operators under noisy random gates effectively behave as if subjected to local depolarizing noise.

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Armando Angrisani @aangrisani.bsky.social · Jan 24

Pauli propagation algorithms work by computing an approximate Heisenberg evolution of the observable in the Pauli basis, iteratively forming a Pauli tree and pruning it with a case-dependent truncation rule.

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