14 citations · 23 across the 20 of their papers we have counts for
9 papers · 1 filter
Optimised Fermion-Qubit Encodings for Quantum Simulation with Reduced Transpiled Circuit Depth
Michael Williams de la Bastida, Thomas M. Bickley, Peter V. Coveney
Simulation of fermionic Hamiltonians with gate-based quantum computers requires the selection of an encoding from fermionic operators to quantum gates, the most widely used being t…
Fast-Forward Lattice Boltzmann: Learning Kinetic Behaviour with Physics-Informed Neural Operators
Xiao Xue, Marco F. P. ten Eikelder, Mingyang Gao +7
The lattice Boltzmann equation (LBE), rooted in kinetic theory, provides a powerful framework for capturing complex flow behaviour by describing the evolution of single-particle di…
Equivariant U-Shaped Neural Operators for the Cahn-Hilliard Phase-Field Model
Xiao Xue, Marco F. P. ten Eikelder, Tianyue Yang +4
Phase separation in binary mixtures, governed by the Cahn-Hilliard equation, plays a central role in interfacial dynamics across materials science and soft matter. While numerical…
Towards Compact Wavefunctions from Quantum-Selected Configuration Interaction
Tim Weaving, Angus Mingare, Alexis Ralli +1
A recent direction in quantum computing for molecular electronic structure sees the use of quantum devices as configuration sampling machines integrated within high-performance com…
An Uncertainty Visualization Framework for Large-Scale Cardiovascular Flow Simulations: A Case Study on Aortic Stenosis
Xiao Xue, Tushar M. Athawale, Jon W. S. McCullough +5
We present a generalizable uncertainty quantification (UQ) and visualization framework for lattice Boltzmann method simulations of high Reynolds number vascular flows, demonstrated…
The wall confronting large language models
Peter V. Coveney, Sauro Succi
We show that the scaling laws which determine the performance of large language models (LLMs) severely limit their ability to improve the uncertainty of their predictions. As a res…
Quantum-Informed Machine Learning for Predicting Spatiotemporal Chaos with Practical Quantum Advantage
Maida Wang, Xiao Xue, Mingyang Gao +1
We introduce a quantum-informed machine learning (QIML) framework for modelling the long-term behaviour of high-dimensional chaotic systems. QIML combines a one-time, offline-train…
Simulating the Antiferromagnetic Heisenberg Model on a Spin-Frustrated Kagome Lattice with the Contextual Subspace Variational Quantum Eigensolver
Tim Weaving, Alexis Ralli, Vinul Wimalaweera +2
In this work we investigate the ground state properties of a candidate quantum spin liquid using a superconducting Noisy Intermediate-Scale Quantum (NISQ) device. Specifically, we…
Bridging Quantum Chemistry and MaxCut: Classical Performance Guarantees and Quantum Algorithms for the Hartree-Fock Method
Alexis Ralli, Tim Weaving, Peter V. Coveney +1
In quantum chemistry, self-consistent field (SCF) algorithms define a nonlinear optimization problem, with both continuous and discrete components. In this work, we derive Hartree-…