11 citations · 26 across the 9 of their papers we have counts for
9 papers
Bosonic Encodings for Hermite-Galerkin Discretizations of High-Dimensional PDEs and Bayesian Inverse Problems
Alice Barthe
The Koopman-von Neumann framework has been proposed to design quantum algorithms for non-linear dynamics. It maps a non-linear ordinary differential equation to a linear partial di…
How to make quantum cheese: efficient geometry oracles for exponentially many pseudorandom microstructures
Alice Barthe
Quantum algorithms for simulating linear systems are often formulated under oracle access assumptions. A central question is when such oracles can be implemented by polynomial-size…
Quantum Advantage in Learning Quantum Dynamics via Fourier coefficient extraction
Alice Barthe, Mahtab Yaghubi Rad, Michele Grossi +1
One of the key challenges in quantum machine learning is finding relevant machine learning tasks with a provable quantum advantage. A natural candidate for this is learning unknown…
On Dequantization of Supervised Quantum Machine Learning via Random Fourier Features
Mehrad Sahebi, Alice Barthe, Yudai Suzuki +2
In the quest for quantum advantage, a central question is under what conditions can classical algorithms achieve a performance comparable to quantum algorithms--a concept known as…
Universal approximation of continuous functions with minimal quantum circuits
Adrián Pérez-Salinas, Mahtab Yaghubi Rad, Alice Barthe +1
The conventional paradigm of quantum computing is discrete: it utilizes discrete sets of gates to realize bitstring-to-bitstring mappings, some of them arguably intractable for cla…
Gate-based quantum simulation of Gaussian bosonic circuits on exponentially many modes
Alice Barthe, M. Cerezo, Andrew T. Sornborger +2
We introduce a framework for simulating, on an -qubit quantum computer, the action of a Gaussian Bosonic (GB) circuit on a state over modes. Specifically, we encode th…
Parameterized quantum circuits as universal generative models for continuous multivariate distributions
Alice Barthe, Michele Grossi, Sofia Vallecorsa +2
Parameterized quantum circuits have been extensively used as the basis for machine learning models in regression, classification, and generative tasks. For supervised learning, the…
Gradients and frequency profiles of quantum re-uploading models
Alice Barthe, Adrián Pérez-Salinas
Quantum re-uploading models have been extensively investigated as a form of machine learning within the context of variational quantum algorithms. Their trainability and expressivi…
Bloch Sphere Binary Trees: A method for the visualization of sets of multi-qubit systems pure states
Alice Barthe, Michele Grossi, Jordi Tura +1
Understanding the evolution of a multi-qubit quantum system, or elucidating what portion of the Hilbert space is occupied by a quantum dataset becomes increasingly hard with the nu…