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20212026
most citedMiniaturizing neural networks for charge state autotuning in quantum dots

23 citations · 24 across the 3 of their papers we have counts for

collaborators

5 papers

cond-mat.mes-hall2026

Admittance extraction of quantum dot arrays for charge sensing

Conor Power, Mathieu Moras, Andrii Sokolov +2

This work demonstrates admittance extraction of quantum dot arrays using a rate equation frame- work matching experimental reflectometry measurements. Using a semiclassical rate eq…

quant-ph2026

Quantum Simulation of Si/SiGe Devices with Experimentally Calibrated Micromagnet Effects

Andrii Sokolov, Conor Power, Mathieu Moras +7

Silicon-based spin qubits in Si/SiGe heterostructures are a leading platform for scalable quantum computing, yet bridging the gap between theoretical computer-aided design(CAD) mod…

cond-mat.mes-hall2024★ 1 cited

Commercial CMOS Process for Quantum Computing: Quantum Dots and Charge Sensing in a 22 nm Fully Depleted Silicon-on-Insulator Process

S. V. Amitonov, A. Aprà, M. Asker +16

Confining electrons or holes in quantum dots formed in the channel of industry-standard fully depleted silicon-on-insulator CMOS structures is a promising approach to scalable qubi…

quant-ph2024

Persistence of Entangled States and High Fidelity Quantum Gate Operations in Si/SiGe Spin Qubits at High Temperature

S. Amitonov, A. Aprà, M. Asker +20

We characterize single- and two-qubit operations in a SiGe quantum dot array, from the perspective of its quantum information processing capabilities. The analysis includes rigorou…

cond-mat.mes-hall2021★ 23 cited

Miniaturizing neural networks for charge state autotuning in quantum dots

Stefanie Czischek, Victor Yon, Marc-Antoine Genest +8

A key challenge in scaling quantum computers is the calibration and control of multiple qubits. In solid-state quantum dots, the gate voltages required to stabilize quantized charg…