9 papers · 1 filter
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…
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…
Variational protocols for emulating digital gates using analog control with always-on interactions
Claire Chevallier, Joseph Vovrosh, Julius de Hond +3
We design variational pulse sequences tailored for neutral atom quantum simulators and show that we can engineer layers of single-qubit and multi-qubit gates. As an application, we…
Let Quantum Neural Networks Choose Their Own Frequencies
Ben Jaderberg, Antonio A. Gentile, Youssef Achari Berrada +2
Parameterized quantum circuits as machine learning models are typically well described by their representation as a partial Fourier series of the input features, with frequencies u…
Potential of quantum scientific machine learning applied to weather modelling
Ben Jaderberg, Antonio A. Gentile, Atiyo Ghosh +5
In this work we explore how quantum scientific machine learning can be used to tackle the challenge of weather modelling. Using parameterised quantum circuits as machine learning m…
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…