193 citations · 754 across the 33 of their papers we have counts for
33 papers
A QSVT-Based Quantum Jacobi Algorithm for Linear Systems with Application to the Poisson Equation
Louisa M. Piskol, Thorsten Grahs, Stefan Langer +1
Many computational fluid dynamics (CFD) algorithms solve partial differential equations by discretization, resulting in large and sparse systems of linear equations. While iterativ…
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…
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 Chebyshev Probabilistic Models for Fragmentation Functions
Jorge J. Martínez 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…
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…
Addressing the Readout Problem in Quantum Differential Equation Algorithms with Quantum Scientific Machine Learning
Chelsea A. Williams, Stefano Scali, Antonio A. Gentile +2
Quantum differential equation solvers aim to prepare solutions as -qubit quantum states over a fine grid of points, surpassing the linear scaling of classical solvers.…
Can Geometric Quantum Machine Learning Lead to Advantage in Barcode Classification?
Chukwudubem Umeano, Stefano Scali, Oleksandr Kyriienko
We consider the problem of distinguishing two vectors (visualized as images or barcodes) and learning if they are related to one another. For this, we develop a geometric quantum m…
Multidimensional Quantum Generative Modeling by Quantum Hartley Transform
Hsin-Yu Wu, Vincent E. Elfving, Oleksandr Kyriienko
We develop an approach for building quantum models based on the exponentially growing orthonormal basis of Hartley kernel functions. First, we design a differentiable Hartley featu…
Beyond the Buzz: Strategic Paths for Enabling Useful NISQ Applications
Pratibha Raghupati Hegde, Oleksandr Kyriienko, Hermanni Heimonen +6
There is much debate on whether quantum computing on current NISQ devices, consisting of noisy hundred qubits and requiring a non-negligible usage of classical computing as part of…
Quantum Iterative Methods for Solving Differential Equations with Application to Computational Fluid Dynamics
Chelsea A. Williams, Antonio A. Gentile, Vincent E. Elfving +2
We propose quantum methods for solving differential equations that are based on a gradual improvement of the solution via an iterative process, and are targeted at applications in…
Ground state-based quantum feature maps
Chukwudubem Umeano, Oleksandr Kyriienko
We introduce a quantum data embedding protocol based on the preparation of a ground state of a parameterized Hamiltonian. We analyze the corresponding quantum feature map, recastin…
Geometric quantum machine learning of BQP protocols and latent graph classifiers
Chukwudubem Umeano, Vincent E. Elfving, Oleksandr Kyriienko
Geometric quantum machine learning (GQML) aims to embed problem symmetries for learning efficient solving protocols. However, the question remains if (G)QML can be routinely used f…
The topology of data hides in quantum thermal states
Stefano Scali, Chukwudubem Umeano, Oleksandr Kyriienko
We provide a quantum protocol to perform topological data analysis (TDA) via the distillation of quantum thermal states. Recent developments of quantum thermal state preparation al…
Nonlinear Rydberg exciton-polaritons in CuO microcavities
Maxim Makhonin, Anthonin Delphan, Kok Wee Song +13
Rydberg excitons (analogues of Rydberg atoms in condensed matter systems) are highly excited bound electron-hole states with large Bohr radii. The interaction between them as well…
Quantum topological data analysis via the estimation of the density of states
Stefano Scali, Chukwudubem Umeano, Oleksandr Kyriienko
We develop a quantum topological data analysis (QTDA) protocol based on the estimation of the density of states (DOS) of the combinatorial Laplacian. Computing topological features…
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…
Protocols for classically training quantum generative models on probability distributions
Sachin Kasture, Oleksandr Kyriienko, Vincent E. Elfving
Quantum Generative Modelling (QGM) relies on preparing quantum states and generating samples from these states as hidden - or known - probability distributions. As distributions fr…
Nonlinear interactions of dipolar excitons and polaritons in MoS2 bilayers
Charalambos Louca, Armando Genco, Salvatore Chiavazzo +12
Nonlinear interactions between excitons strongly coupled to light are key for accessing quantum many-body phenomena in polariton systems. Atomically-thin two-dimensional semiconduc…
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…