8 papers
Benchmarking a machine-learning differential equations solver on a neutral-atom logical processor
Pauline Mathiot, Elio Garnaoui, Axel-Ugo Leriche +37
We report on a performance comparison between physical and logical computations on a prototypical machine-learning application: solving differential equations using quantum kernel…
Conservative quantum offline model-based optimization
Kristian Sotirov, Annie E. Paine, Savvas Varsamopoulos +2
Offline model-based optimization (MBO) refers to the task of optimizing a black-box objective function using only a fixed set of prior input-output data, without any active experim…
A unified quantum computing quantum Monte Carlo framework through structured state preparation
Giuseppe Buonaiuto, Antonio Marquez Romero, Brian Coyle +4
We extend Quantum Computing Quantum Monte Carlo (QCQMC) beyond ground-state energy estimation by systematically constructing the quantum circuits used for state preparation. Replac…
Weak forms offer strong regularisations: how to make physics-informed (quantum) machine learning more robust
Annie E. Paine, Smit Chaudhary, Antonio A. Gentile
Physics-informed (PI) methodologies have surged to become a pillar route to solve Differential Equations (DEs), sustained by the growth of machine learning methods in scientific co…
Vortex Detection from Quantum Data
Chelsea A. Williams, Annie E. Paine, Antonio A. Gentile +2
Quantum solutions to differential equations represent quantum data -- states that contain relevant information about the system's behavior, yet are difficult to analyze. We propose…
Quantum algorithm for solving nonlinear differential equations based on physics-informed effective Hamiltonians
Hsin-Yu Wu, Annie E. Paine, Evan Philip +2
We propose a distinct approach to solving linear and nonlinear differential equations (DEs) on quantum computers by encoding the problem into ground states of effective Hamiltonian…