8 papers · 1 filter
Lie-Algebraic Classical Simulation of Bosonic Systems Beyond Gaussian Dynamics
Adelina Bärligea, Timothy Heightman, Jakob S. Kottmann +1
Classical simulability is ultimately determined by both the dynamics of a quantum system and the observables being evaluated. Lie-algebraic simulation exploits the latter to make e…
Hamilton-Zero: A Neural Tensor-Network Foundation Model for Ground States of Arbitrary Quadratic Qubit Hamiltonians
Timothy Heightman, Elena Orlova, Philip Mantrov +1
A central promise of useful quantum advantage is the ability to compute ground states of Hamiltonian systems beyond the reach of classical simulation methods. Here we demonstrate t…
Heisenberg-Limited Quantum Hamiltonian Learning via Randomly Spread Product-States
Bora Baran, Timothy Heightman
We show how the Heisenberg-limited quadratic Fisher-information regime of short-time quantum evolution can be made practically accessible for quantum Hamiltonian learning, using on…
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…
Deep Learning in Classical and Quantum Physics
Timothy Heightman, Marcin PÅodzieÅ
Scientific progress is tightly coupled to the emergence of new research tools. Today, machine learning (ML)-especially deep learning (DL)-has become a transformative instrument for…