10 papers
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…
Mapping Phase Diagrams of Quantum Spin Systems through Semidefinite-Programming Relaxations
David Jansen, Donato Farina, Luke Mortimer +5
Identifying quantum phase transitions poses a significant challenge in condensed matter physics, as this requires methods that both provide accurate results and scale well with sys…
Benchmarking Simulacra AI's Quantum Accurate Synthetic Data Generation for Chemical Sciences
Fabio Falcioni, Elena Orlova, Timothy Heightman +2
In this work, we benchmark \simulacra's synthetic data generation pipeline against a state-of-the-art Microsoft pipeline on a dataset of small to large systems. By analyzing the en…