Manuel Rudolph

@quantummanuel.bsky.social

PhD Candidate in Physics @EPFL 🇨🇭 I like simulating quantum computers 💻

I recently submitted my PhD thesis 🎉 I am now planning the next step of my journey. I will stay in Berlin for personal reasons, which makes me curious which research groups and companies would hire postdocs or equivalent for remote positions. If you have any leads, please DM!

Check out our published paper and Marco's thread below! One thing to appreciate about ML as an application for quantum computers is that cheap, truncated simulations are themselves valid models. Beating them with shot noise and daunting gradient scaling is no easy feat.

Marco Cerezo@mvscerezo.bsky.social · 4mo ago

Extremely proud to see our paper "Quantum Convolutional Neural Networks are Effectively Classically Simulable" published in PRX Quantum journals.aps.org/prxquantum/a... This is an instantiation of our work, provable absence of barren plateaus implies classical simulability.

Hey - we've extended Pauli and Majorana propagation to simulating thermal states The trick is to imaginary-time evolve identity and then normalize by the trace of the Pauli (or Majorana) sum at the end The catch is that both our analytics and numerics suggest it only works at high temperatures 🧵👇

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Manuel Rudolph@quantummanuel.bsky.social · 6mo ago

Did you know you can simulate quantum states with Pauli and Majorana propagation? In short: High-temperature states are provably and practically sparse, and we can use imaginary time evolution to get there starting from the infinite-temperature state. scirate.com/arxiv/2602.0...

Yanting is keeping you updated on our efforts to continually improve propagation algorithms and our code-base PauliPropagation.jl. This time with memory savings and increased robustness to truncations for simulating quantum systems with certain symmetries ⬆️ ➡️ ⬇️ ⬅️ Expect more in the future! 🚀

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

Yanting Teng@yteng.bsky.social · 8mo ago

Happy to share our paper: Leveraging Symmetry Merging in Pauli Propagation scirate.com/arxiv/2512.1... tl;dr We improve standard Pauli propagation by merging Pauli strings related by symmetry. Shoutout to my collaborators @sueyeonchung.bsky.social @quantummanuel.bsky.social @qzoeholmes.bsky.social

Welcoming summer the best way we know how: with pasta, physics, and a phenomenal team 🍝⚛️ A warm #Google #Quantum #AI welcome to Manuel Rudolph, who’s joining us this summer! 🎉 We’re thrilled to have his sharp mind and curious spirit with us Thx Nikita+team for organizing

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Retweeting this for the folks (myself included) that weren't online over the long weekend PauliPropagation.jl is open source library that you can use to approximately simulate quantum circuits We explain the nitty gritty of how these algorithms work in practise in our latest companion paper 🧵👇

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Manuel Rudolph@quantummanuel.bsky.social · last yr.

❗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

In a new preprint arxiv.org/abs/2503.05693, led by Joseph Tindall and Antonio Mello at Flatiron CCQ, we simulate annealing of disordered quantum magnets 🧲 ⌛ and in many cases find better accuracy than recent results from D-Wave devices and leading classical methods (c.f. arxiv.org/abs/2403.00910).

Dynamics of disordered quantum systems with two- and three-dimensional tensor networks

Quantum spin glasses form a good testbed for studying the performance of various quantum annealing and optimization algorithms. In this work we show how two- and three-dimensional tensor networks can ...

arxiv.org

Here we take steps to understanding the potential of warm starts for VQAs We provide a general variance lower bound for patches of loss landscapes: - for both structured and unstructured circuits - to provide small-angle-initialization 'guarantees' - to study the scaling of regions of attraction

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Ricard Puig@pvricard.bsky.social · last yr.

New paper on arXiv 🔥 We present a bound that unifies all the previous guarantees of small regions with substantial gradients in BP landscapes. This allows us to study new architectures, parameter correlations, and points on the landscape that could not be analyzed before. scirate.com/arxiv/2502.0...

Out today, our latest paper on the theme of: "offload as much work as possible from NISQ hardware" Here we focus on classically simulating small regions ("patches") of an expectation landscape Our algs/theorems apply to dynamical simulation, VQAs and beyond ➡️ scirate.com/arxiv/2411.1... ⬅️ 🧵👇

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Manuel Rudolph@quantummanuel.bsky.social · 2y ago

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