193 citations · 288 across the 15 of their papers we have counts for
16 papers
Effective-Hamiltonian Quantum Solvers for Differential Equations: Alternative Constructions and Function Encodings
Annie E. Paine
Differential equations can be encoded as ground-state problems by constructing a positive-semidefinite effective Hamiltonian whose minimum-energy state represents the solution. We…
OpenQARP: a modular framework for quantum application research
Stefano Scali, Vicente P. Soloviev, Antonio Márquez Romero +7
We introduce OpenQARP, the Open Quantum Application Research Package: an open-source Python framework for quantum application research, built on a compiled C++ core. In OpenQARP, a…
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…
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…
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…
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…
Differential equation quantum solvers: engineering measurements to reduce cost
Annie Paine, Casper Gyurik, Antonio Andrea Gentile
Quantum computers have been proposed as a solution for efficiently solving non-linear differential equations (DEs), a fundamental task across diverse technological and scientific d…
Physics-Informed Quantum Machine Learning: Solving nonlinear differential equations in latent spaces without costly grid evaluations
Annie E. Paine, Vincent E. Elfving, Oleksandr Kyriienko
We propose a physics-informed quantum algorithm to solve nonlinear and multidimensional differential equations (DEs) in a quantum latent space. We suggest a strategy for building q…
What can we learn from quantum convolutional neural networks?
Chukwudubem Umeano, Annie E. Paine, Vincent E. Elfving +1
Quantum machine learning (QML) shows promise for analyzing quantum data. A notable example is the use of quantum convolutional neural networks (QCNNs), implemented as specific type…
Quantum Chebyshev Transform: Mapping, Embedding, Learning and Sampling Distributions
Chelsea A. Williams, Annie E. Paine, Hsin-Yu Wu +2
We develop a paradigm for building quantum models in the orthonormal space of Chebyshev polynomials. We show how to encode data into quantum states with amplitudes being Chebyshev…
Quantum Kernel Methods for Solving Differential Equations
Annie E. Paine, Vincent E. Elfving, Oleksandr Kyriienko
We propose several approaches for solving differential equations (DEs) with quantum kernel methods. We compose quantum models as weighted sums of kernel functions, where variables…
Protocols for Trainable and Differentiable Quantum Generative Modelling
Oleksandr Kyriienko, Annie E. Paine, Vincent E. Elfving
We propose an approach for learning probability distributions as differentiable quantum circuits (DQC) that enable efficient quantum generative modelling (QGM) and synthetic data g…
Quantum Quantile Mechanics: Solving Stochastic Differential Equations for Generating Time-Series
Annie E. Paine, Vincent E. Elfving, Oleksandr Kyriienko
We propose a quantum algorithm for sampling from a solution of stochastic differential equations (SDEs). Using differentiable quantum circuits (DQCs) with a feature map encoding of…
Solving nonlinear differential equations with differentiable quantum circuits
Oleksandr Kyriienko, Annie E. Paine, Vincent E. Elfving
We propose a quantum algorithm to solve systems of nonlinear differential equations. Using a quantum feature map encoding, we define functions as expectation values of parametrized…