9 papers
Quantum Fourier Generative Models Trainable at Large Scale
Cenk Tüysüz, Oleksandr Kyriienko, Michele Grossi
We propose an algorithmic framework for building and training quantum generative models corresponding to multivariate probability distributions. Our model uses parallel Fourier fea…
Quantum Chebyshev Probabilistic Models for Fragmentation Functions
Jorge J. MartÃnez de Lejarza, Hsin-Yu Wu, Oleksandr Kyriienko +2
Quantum generative modeling is emerging as a powerful tool for advancing data analysis in high-energy physics, where complex multivariate distributions are common. However, efficie…
Advantage for Discrete Variational Quantum Algorithms in Circuit Recompilation
Oleksandr Kyriienko, Chukwudubem Umeano, Zoë Holmes
The relative power of quantum algorithms, using an adaptive access to quantum devices, versus classical post-processing methods that rely only on an initial quantum data set, remai…
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…
Quantum community detection via deterministic elimination
Chukwudubem Umeano, Stefano Scali, Oleksandr Kyriienko
We propose a quantum algorithm for calculating the structural properties of complex networks and graphs. The corresponding protocol -- deteQt -- is designed to perform large-scale…