6 citations · 9 across the 3 of their papers we have counts for
4 papers · 1 filter
Classically Approximating Variational Quantum Machine Learning with Random Fourier Features
Jonas Landman, Slimane Thabet, Constantin Dalyac +2
Many applications of quantum computing in the near term rely on variational quantum circuits (VQCs). They have been showcased as a promising model for reaching a quantum advantage…
Embedding the MIS problem for non-local graphs with bounded degree using 3D arrays of atoms
Constantin Dalyac, Loic Henriet
In the past years, many quantum algorithms have been proposed to tackle hard combinatorial problems. These algorithms, which have been studied in depth in complexity theory, are at…
Quantum evolution kernel : Machine learning on graphs with programmable arrays of qubits
Louis-Paul Henry, Slimane Thabet, Constantin Dalyac +1
The rapid development of reliable Quantum Processing Units (QPU) opens up novel computational opportunities for machine learning. Here, we introduce a procedure for measuring the s…
Qualifying quantum approaches for hard industrial optimization problems. A case study in the field of smart-charging of electric vehicles
Constantin Dalyac, Loïc Henriet, Emmanuel Jeandel +4
In order to qualify quantum algorithms for industrial NP-Hard problems, comparing them to available polynomial approximate classical algorithms and not only to exact ones -- expone…