6 papers
Automatic Charge State Tuning of 300 mm FDSOI Quantum Dots Using Neural Network Segmentation of Charge Stability Diagram
Peter Samaha, Amine Torki, Ysaline Renaud +4
Tuning of gate-defined semiconductor quantum dots (QDs) is a major bottleneck for scaling spin qubit technologies. We present a deep learning (DL) driven, semantic-segmentation pip…
Unsupervised local learning based on voltage-dependent synaptic plasticity for resistive and ferroelectric synapses
Nikhil Garg, Ismael Balafrej, Joao Henrique Quintino Palhares +11
The deployment of AI on edge computing devices faces significant challenges related to energy consumption and functionality. These devices could greatly benefit from brain-inspired…
Unified Memcapacitor-Memristor Memory for Synaptic Weights and Neuron Temporal Dynamics
Simone D'Agostino, Marco Massarotto, Tristan Torchet +7
We present a fabricated and experimentally characterized memory stack that unifies memristive and memcapacitive behavior. Exploiting this dual functionality, we design a circuit en…
Experimental online quantum dots charge autotuning using neural networks
Victor Yon, Bastien Galaup, Claude Rohrbacher +11
Spin-based semiconductor qubits hold promise for scalable quantum computing, yet they require reliable autonomous calibration procedures. This study presents an experimental demons…
Towards a Cryogenic CMOS-Memristor Neural Decoder for Quantum Error Correction
Pierre-Antoine Mouny, Maher Benhouria, Victor Yon +6
This paper presents a novel approach utilizing a scalable neural decoder application-specific integrated circuit (ASIC) based on metal oxide memristors in a 180nm CMOS technology.…
A Cryogenic Memristive Neural Decoder for Fault-tolerant Quantum Error Correction
Victor Yon, Frédéric Marcotte, Pierre-Antoine Mouny +6
Neural decoders for quantum error correction (QEC) rely on neural networks to classify syndromes extracted from error correction codes and find appropriate recovery operators to pr…