233 citations · 239 across the 5 of their papers we have counts for
5 papers
Lindbladian Learning with Neural Differential Equations
Timothy Heightman, Roman Aseguinolaza Gallo, Edward Jiang +3
Inferring the dynamical generator of a many-body quantum system from measurement data is essential for the verification, calibration, and control of quantum processors. When the sy…
Quantum Machine Learning in Multi-Qubit Phase-Space Part I: Foundations
Timothy Heightman, Edward Jiang, Ruth Mora-Soto +2
Quantum machine learning (QML) seeks to exploit the intrinsic properties of quantum mechanical systems, including superposition, coherence, and quantum entanglement for classical d…
Solving The Quantum Many-Body Hamiltonian Learning Problem with Neural Differential Equations
Timothy Heightman, Edward Jiang, Antonio Acín
Understanding and characterising quantum many-body dynamics remains a significant challenge due to both the exponential complexity required to represent quantum many-body Hamiltoni…
SoK: How Robust is Image Classification Deep Neural Network Watermarking? (Extended Version)
Nils Lukas, Edward Jiang, Xinda Li +1
Deep Neural Network (DNN) watermarking is a method for provenance verification of DNN models. Watermarking should be robust against watermark removal attacks that derive a surrogat…
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,…