639 citations · 1.1k across the 18 of their papers we have counts for
19 papers
A provable quantum advantage for approximate optimization via decoded quantum interferometry
Maximilian J. Kramer, Elies Gil-Fuster, Benjamin D. M. Jones +2
Decoded quantum interferometry (DQI) is a novel paradigm for tackling approximate optimization problems on quantum computers. This framework comes with strong performance guarantee…
Cautious optimism for deep parameterized quantum circuits
Marie Kempkes, Elies Gil-Fuster, Carlos Bravo-Prieto +5
A central challenge in quantum machine learning is understanding the scaling behavior of parameterized quantum circuits (PQCs). In particular, it remains unclear how their performa…
Approximate sampling from decoded quantum interferometry via Markov chain Monte Carlo methods
Elies Gil-Fuster, Matan Ninio, Lennart Bittel +4
Optimization problems are among the leading candidates for industrially relevant quantum advantage. Decoded quantum interferometry (DQI) has been proposed to tackle approximate opt…
Optimal algorithmic complexity of inference in quantum kernel methods
Elies Gil-Fuster, Seongwook Shin, Sofiene Jerbi +2
Quantum kernel methods are among the leading candidates for achieving quantum advantage in supervised learning. A key bottleneck is the cost of inference: evaluating a trained mode…
A PAC-Bayesian approach to generalization for quantum models
Pablo Rodriguez-Grasa, Matthias C. Caro, Jens Eisert +3
Generalization is a central concept in machine learning theory, yet for quantum models, it is predominantly analyzed through uniform bounds that depend on a model's overall capacit…
Prospects for quantum advantage in machine learning from the representability of functions
Sergi Masot-Llima, Elies Gil-Fuster, Carlos Bravo-Prieto +2
Demonstrating quantum advantage in machine learning tasks requires navigating a complex landscape of proposed models and algorithms. To bring clarity to this search, we introduce a…
Quantum computing and artificial intelligence: status and perspectives
Giovanni Acampora, Andris Ambainis, Natalia Ares +36
This white paper discusses and explores the various points of intersection between quantum computing and artificial intelligence (AI). It describes how quantum computing could supp…
Kernel-based dequantization of variational QML without Random Fourier Features
Ryan Sweke, Seongwook Shin, Elies Gil-Fuster
There is currently a huge effort to understand the potential and limitations of variational quantum machine learning (QML) based on the optimization of parameterized quantum circui…
Double descent in quantum kernel methods
Marie Kempkes, Aroosa Ijaz, Elies Gil-Fuster +4
The double descent phenomenon challenges traditional statistical learning theory by revealing scenarios where larger models do not necessarily lead to reduced performance on unseen…
Opportunities and limitations of explaining quantum machine learning
Elies Gil-Fuster, Jonas R. Naujoks, Grégoire Montavon +3
A common trait of many machine learning models is that it is often difficult to understand and explain what caused the model to produce the given output. While the explainability o…
Concept learning of parameterized quantum models from limited measurements
Beng Yee Gan, Po-Wei Huang, Elies Gil-Fuster +1
Classical learning of the expectation values of observables for quantum states is a natural variant of learning quantum states or channels. While learning-theoretic frameworks esta…
On the relation between trainability and dequantization of variational quantum learning models
Elies Gil-Fuster, Casper Gyurik, Adrián Pérez-Salinas +1
The quest for successful variational quantum machine learning (QML) relies on the design of suitable parametrized quantum circuits (PQCs), as analogues to neural networks in classi…
On the expressivity of embedding quantum kernels
Elies Gil-Fuster, Jens Eisert, Vedran Dunjko
One of the most natural connections between quantum and classical machine learning has been established in the context of kernel methods. Kernel methods rely on kernels, which are…
Potential and limitations of random Fourier features for dequantizing quantum machine learning
Ryan Sweke, Erik Recio-Armengol, Sofiene Jerbi +4
Quantum machine learning is arguably one of the most explored applications of near-term quantum devices. Much focus has been put on notions of variational quantum machine learning…
Understanding quantum machine learning also requires rethinking generalization
Elies Gil-Fuster, Jens Eisert, Carlos Bravo-Prieto
Quantum machine learning models have shown successful generalization performance even when trained with few data. In this work, through systematic randomization experiments, we sho…
Exploiting symmetry in variational quantum machine learning
Johannes Jakob Meyer, Marian Mularski, Elies Gil-Fuster +4
Variational quantum machine learning is an extensively studied application of near-term quantum computers. The success of variational quantum learning models crucially depends on f…
Encoding-dependent generalization bounds for parametrized quantum circuits
Matthias C. Caro, Elies Gil-Fuster, Johannes Jakob Meyer +2
A large body of recent work has begun to explore the potential of parametrized quantum circuits (PQCs) as machine learning models, within the framework of hybrid quantum-classical…
Training Quantum Embedding Kernels on Near-Term Quantum Computers
Thomas Hubregtsen, David Wierichs, Elies Gil-Fuster +3
Kernel methods are a cornerstone of classical machine learning. The idea of using quantum computers to compute kernels has recently attracted attention. Quantum embedding kernels (…
Data re-uploading for a universal quantum classifier
Adrián Pérez-Salinas, Alba Cervera-Lierta, Elies Gil-Fuster +1
A single qubit provides sufficient computational capabilities to construct a universal quantum classifier when assisted with a classical subroutine. This fact may be surprising sin…