8 citations · 10 across the 12 of their papers we have counts for
12 papers
Topology Obstructs Pure Foundation Neural Quantum States
Timothy Heightman, Elena Orlova, Philip Mantrov +1
Foundation models for ground states in spin-1/2 systems are a promising method for problems ranging from quantum chemistry to identifying new phase diagrams. Nearly all such models…
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…
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…
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…
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…
Deep Learning in Classical and Quantum Physics
Timothy Heightman, Marcin Płodzień
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…
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…
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…
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…
Spoofing of Quantum Channels Enables Low-Rank Projective Simulation
Timothy Heightman, Grzegorz Rajchel-Mieldzioć
The ability to characterise and discern quantum channels is a crucial aspect of noisy quantum technologies. In this work, we explore the problem of distinguishing quantum channels…
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…
Qudit-inspired optimization for graph coloring
David Jansen, Timothy Heightman, Luke Mortimer +2
We introduce a quantum-inspired algorithm for graph coloring problems (GCPs) that utilizes qudits in a product state, with each qudit representing a node in the graph and parameter…