193 citations · 759 across the 35 of their papers we have counts for
4 papers · 2 filters
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…
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…