Efficiently testing whether a quantum state possesses a given structure is both a fundamental and a practical task in quantum information. Among the most important structured families are Gaussian states, which underpin quantum optics and many-body physics while defining paradigmatic regimes of efficient classical simu...
Decoded quantum interferometry (DQI) is a novel paradigm for tackling approximate optimization problems on quantum computers. This framework comes with strong performance guarantees and exploits a well-established duality between optimization and coding theory. A central question, however, is whether DQI can actually p...
Maximilian J Kramer, Elies Gil-Fuster, Benjamin D. M. Jones et al.· 0 citations
This work presents a simplified analytical characterization of DQI, and provides new empirical evidence that classical sampling algorithms can closely match DQI's optimization performance, offering a more nuanced perspective on the practical advantage of DQI.
Elies Gil-Fuster, Matan Ninio, Lennart Bittel et al.· 1 citation
The results demonstrate a rigorous learning separation for a natural ML task based on Hamiltonian evolution, while building connections between quantum learning theory, quantum simulation, and QML.
It is shown that gradient-based PQCs can exhibit improved performance on unseen data as model size increases, displaying the phenomenon of double descent, which contrasts with the traditional view that larger models lead to degraded generalization.
Marie C. Kempkes, Elies Gil-Fuster, Carlos Bravo-Prieto et al.· arXiv.org· 0 citations
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