120 citations · 187 across the 6 of their papers we have counts for
6 papers
Experimental quantum reservoir computing with a circuit quantum electrodynamics system
Baptiste Carles, Julien Dudas, Léo Balembois +2
Quantum reservoir computing is a machine learning framework that offers ease of training compared to other quantum neural networks, as it does not rely on gradient-based optimizati…
Training the parametric interactions in an analog bosonic quantum neural network with Fock basis measurement
Julien Dudas, Baptiste Carles, Elie Gouzien +2
Quantum neural networks promise to extend the power of machine learning into the quantum domain, with potential applications ranging from automatic recognition of quantum states to…
Training of Physical Neural Networks
Ali Momeni, Babak Rahmani, Benjamin Scellier +25
Physical neural networks (PNNs) are a class of neural-like networks that leverage the properties of physical systems to perform computation. While PNNs are so far a niche research…
Sequential dispersive measurement of a superconducting qubit
Théau Peronnin, Danijela Marković, Quentin Ficheux +1
We present a superconducting device that realizes the sequential measurement of a transmon qubit. The device disables common limitations of dispersive readout such as Purcell effec…
Reservoir computing with the frequency, phase and amplitude of spin-torque nano-oscillators
Danijela Marković, Nathan Leroux, Mathieu Riou +8
Spin-torque nano-oscillators can emulate neurons at the nanoscale. Recent works show that the non-linearity of their oscillation amplitude can be leveraged to achieve waveform clas…
Demonstration of an effective ultrastrong coupling between two oscillators
Danijela Marković, Sébastien Jezouin, Quentin Ficheux +6
When the coupling rate between two quantum systems becomes as large as their characteristic frequencies, it induces dramatic effects on their dynamics and even on the nature of the…