5 citations · 6 across the 4 of their papers we have counts for
3 papers · 1 filter
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…