activity
20182022
most citedPennyLane: Automatic differentiation of hybrid quantum-classical computations

233 citations · 341 across the 7 of their papers we have counts for

collaborators

7 papers

quant-ph2022★ 4 cited

Predicting Properties of Quantum Systems with Conditional Generative Models

Haoxiang Wang, Maurice Weber, Josh Izaac +1

Machine learning has emerged recently as a powerful tool for predicting properties of quantum many-body systems. For many ground states of gapped Hamiltonians, generative models ca…

cs.LG2022★ 4 cited

Certifying Out-of-Domain Generalization for Blackbox Functions

Maurice Weber, Linyi Li, Boxin Wang +3

Certifying the robustness of model performance under bounded data distribution drifts has recently attracted intensive interest under the umbrella of distributional robustness. How…

quant-ph2021★ 6 cited

Toward Reliability in the NISQ Era: Robust Interval Guarantee for Quantum Measurements on Approximate States

Maurice Weber, Abhinav Anand, Alba Cervera-Lierta +6

Near-term quantum computation holds potential across multiple application domains. However, imperfect preparation and evolution of states due to algorithmic and experimental shortc…

quant-ph2020★ 41 cited

Optimal Provable Robustness of Quantum Classification via Quantum Hypothesis Testing

Maurice Weber, Nana Liu, Bo Li +2

Quantum machine learning models have the potential to offer speedups and better predictive accuracy compared to their classical counterparts. However, these quantum algorithms, lik…

cs.LG2020★ 47 cited

RAB: Provable Robustness Against Backdoor Attacks

Maurice Weber, Xiaojun Xu, Bojan Karlaš +2

Recent studies have shown that deep neural networks (DNNs) are vulnerable to adversarial attacks, including evasion and backdoor (poisoning) attacks. On the defense side, there hav…

eess.IV2019★ 6 cited

Observer Dependent Lossy Image Compression

Maurice Weber, Cedric Renggli, Helmut Grabner +1

Deep neural networks have recently advanced the state-of-the-art in image compression and surpassed many traditional compression algorithms. The training of such networks involves…

quant-ph2018★ 233 cited

PennyLane: Automatic differentiation of hybrid quantum-classical computations

Ville Bergholm, Josh Izaac, Maria Schuld +65

PennyLane is a Python 3 software framework for differentiable programming of quantum computers. The library provides a unified architecture for near-term quantum computing devices,…