10 papers
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…
Migrating QAOA from Qiskit 1.x to 2.x: An experience report
Julien Cardinal, Imen Benzarti, Ghizlane El boussaidi +1
Migrating quantum algorithms across evolving frameworks introduces subtle behavioral changes that affect accuracy and reproducibility. This paper reports our experience converting…
Does Data Complexity Predict Quantum Advantage? A framework, a pre-specified test, and an attribution of the measured edge
Christophe Pere
Quantum machine learning (QML) holds promise for pattern recognition, optimization, and data analysis, but the conditions under which it can outperform classical methods remain unc…
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…
EAQGA: A Quantum-Enhanced Genetic Algorithm with Novel Entanglement-Aware Crossovers
Mohammad Kashfi Haghighi, Matthieu Fortin-Deschênes, Christophe Pere +1
Genetic algorithms are highly effective optimization techniques for many computationally challenging problems, including combinatorial optimization tasks like portfolio optimizatio…
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…
XGSwap: eXtreme Gradient boosting Swap for Routing in NISQ Devices
Jean-Baptiste Waring, Christophe Pere, Sébastien Le Beux
In the current landscape of noisy intermediate-scale quantum (NISQ) computing, the inherent noise presents significant challenges to achieving high-fidelity long-range entanglement…
Noise Aware Utility Optimization of NISQ Devices
Jean-Baptiste Waring, Christophe Pere, Sebastien Le Beux
In order to enter the era of utility, noisy intermediate-scale quantum (NISQ) devices need to enable long-range entanglement of large qubit chains. However, due to the limited conn…