78 citations · 294 across the 17 of their papers we have counts for
18 papers
Quantum error mitigation from information dynamics
Philippe Suchsland, Thomas Schuster, Manuel S. Rudolph +3
Overcoming experimental errors is a central challenge in quantum science. This challenge is particularly acute in large and complex quantum circuits, where quantum information spre…
Observable Estimation in the Absence of Classical Verification
Samantha V. Barron, Bradley Mitchell, Vinay Tripathi +45
The predictive success of quantum mechanics underpins many areas of modern science, even as the exact simulation of large, interacting quantum systems remains beyond the reach of c…
Precision quantum simulation of magnon spectra and interactions
Trond I. Andersen, Nikita Astrakhantsev, Jeronimo Martinez +329
Quantum simulation promises to advance materials discovery by accurately simulating complex states of matter, their microscopic excitations, and macroscopic response functions. The…
Classically estimating observables of noiseless quantum circuits
Armando Angrisani, Alexander Schmidhuber, Manuel S. Rudolph +3
We present a classical algorithm based on Pauli propagation for estimating expectation values of arbitrary observables on random unstructured quantum circuits across all circuit ar…
Quantum Convolutional Neural Networks are Effectively Classically Simulable
Pablo Bermejo, Paolo Braccia, Manuel S. Rudolph +3
Quantum Convolutional Neural Networks (QCNNs) are widely regarded as a promising model for Quantum Machine Learning (QML). In this work we tie their heuristic success to two facts.…
A Search for Classical Subsystems in Quantum Worlds
Arsalan Adil, Manuel S. Rudolph, Andrew Arrasmith +3
Decoherence and einselection have been effective in explaining several features of an emergent classical world from an underlying quantum theory. However, the theory assumes a part…
Does provable absence of barren plateaus imply classical simulability?
M. Cerezo, Martin Larocca, Diego García-Martín +9
A large amount of effort has recently been put into understanding the barren plateau phenomenon. In this perspective article, we face the increasingly loud elephant in the room and…
Classical surrogate simulation of quantum systems with LOWESA
Manuel S. Rudolph, Enrico Fontana, Zoë Holmes +1
We introduce LOWESA as a classical algorithm for faithfully simulating quantum systems via a classically constructed surrogate expectation landscape. After an initial overhead to b…
Classical simulations of noisy variational quantum circuits
Enrico Fontana, Manuel S. Rudolph, Ross Duncan +2
Noise detrimentally affects quantum computations so that they not only become less accurate but also easier to simulate classically as systems scale up. We construct a classical si…
Connecting the Hamiltonian structure to the QAOA energy and Fourier landscape structure
Michał Stęchły, Lanruo Gao, Boniface Yogendran +2
In this paper, we aim to expand the understanding of the relationship between the composition of the Hamiltonian in the Quantum Approximate Optimization Algorithm (QAOA) and the co…
Trainability barriers and opportunities in quantum generative modeling
Manuel S. Rudolph, Sacha Lerch, Supanut Thanasilp +5
Quantum generative models provide inherently efficient sampling strategies and thus show promise for achieving an advantage using quantum hardware. In this work, we investigate the…
The power and limitations of learning quantum dynamics incoherently
Sofiene Jerbi, Joe Gibbs, Manuel S. Rudolph +4
Quantum process learning is emerging as an important tool to study quantum systems. While studied extensively in coherent frameworks, where the target and model system can share qu…
Data re-uploading with a single qudit
Noah L. Wach, Manuel S. Rudolph, Fred Jendrzejewski +1
Quantum two-level systems, i.e. qubits, form the basis for most quantum machine learning approaches that have been proposed throughout the years. However, higher dimensional quantu…
Qubit seriation: Improving data-model alignment using spectral ordering
Atithi Acharya, Manuel Rudolph, Jing Chen +2
With the advent of quantum and quantum-inspired machine learning, adapting the structure of learning models to match the structure of target datasets has been shown to be crucial f…
Decomposition of Matrix Product States into Shallow Quantum Circuits
Manuel S. Rudolph, Jing Chen, Jacob Miller +2
The rapid pace of recent advancements in numerical computation, notably the rise of GPU and TPU hardware accelerators, have allowed tensor network (TN) algorithms to scale to even…
Synergy Between Quantum Circuits and Tensor Networks: Short-cutting the Race to Practical Quantum Advantage
Manuel S. Rudolph, Jacob Miller, Danial Motlagh +3
While recent breakthroughs have proven the ability of noisy intermediate-scale quantum (NISQ) devices to achieve quantum advantage in classically-intractable sampling tasks, the us…
ORQVIZ: Visualizing High-Dimensional Landscapes in Variational Quantum Algorithms
Manuel S. Rudolph, Sukin Sim, Asad Raza +5
Variational Quantum Algorithms (VQAs) are promising candidates for finding practical applications of near to mid-term quantum computers. There has been an increasing effort to stud…
Generation of High-Resolution Handwritten Digits with an Ion-Trap Quantum Computer
Manuel S. Rudolph, Ntwali Bashige Toussaint, Amara Katabarwa +3
Generating high-quality data (e.g. images or video) is one of the most exciting and challenging frontiers in unsupervised machine learning. Utilizing quantum computers in such task…