6 papers · 1 filter
Exploring the Effects of Entanglement on Quantum Machine Learning of Pathogen Epitope-Receptor Binding
Aspen Erlandsson Brisebois, Luis Pablo Gonzalez Dominguez, Shivansi Prajapati +8
Parameterized quantum circuits (PQCs) provide a flexible substrate for hybrid quantum machine learning (QML), but their practical value on Noisy Intermediate-Scale Quantum (NISQ) d…
Breaking concentration barriers for quantum extreme learning on digital quantum processors
Timothée Dao, Ege Yilmaz, Ibrahim Shehzad +8
Reservoir computing leverages rich, non-linear dynamics to process temporal data. Quantum variants promise enhanced expressivity from high-dimensional Hilbert spaces, yet their pra…
Robust GHZ State Preparation via Majority-Voted Boundary Measurements
Jean-Baptiste Waring, Sébastien Le Beux, Christophe Pere
Preparing high-fidelity Greenberger-Horne-Zeilinger (GHZ) states on noisy quantum hardware remains challenging due to cumulative gate errors and decoherence. We introduce Group-Maj…
Data Complexity: a threshold between Classical and Quantum Machine Learning -- Part I
Christophe Pere
Quantum machine learning (QML) holds promise for accelerating pattern recognition, optimization, and data analysis, but the conditions under which it can truly outperform classical…
Mid-circuit measurement as an algorithmic primitive
Antoine Lemelin, Christophe Pere, Olivier Landon-Cardinal +1
We explore the usefulness of mid-circuit measurements to enhance quantum algorithmics. Specifically, we assess how quantum phase estimation (QPE) and mid-circuit measurements can i…
CHSH Violations using Dynamic Circuits
Jean-Baptiste Waring, Christophe Pere, Sébastien Le Beux
Scalable quantum computing relies on high-quality, long-range entanglement, a challenge on noisy, near-term devices. The need for practical insights for near-term algorithm design…