Resources
Publications
Machine learning as an enabler of qubit scalability.
N Ares
Nature Reviews Materials (2021)
Deep reinforcement learning for efficient measurement of quantum devices.
V Nguyen, SB Orbell, DT Lennon, H Moon, F Vigneau, LC Camenzind, …
npj Quantum Information 7, 100 (2021)
Quantum device fine-tuning using unsupervised embedding learning.
NM van Esbroeck, DT Lennon, H Moon, V Nguyen, F Vigneau, …
New Journal of Physics 22, 095003 (2020)
Machine learning enables completely automatic tuning of a quantum device faster than human experts.
H Moon, DT Lennon, J Kirkpatrick, NM van Esbroeck, LC Camenzind, …
Nature Communications 11, 4161 (2020)
Efficiently measuring a quantum device using machine learning.
DT Lennon, H Moon, LC Camenzind, L Yu, DM Zumbühl, GAD Briggs, …
npj Quantum Information 5, 79 (2019)
Cross-architecture Tuning of Silicon and SiGe-based Quantum Devices Using Machine Learning.
B Severin, DT Lennon, LC Camenzind, F Vigneau, F Fedele, D Jirovec, …
arXiv:2107.12975
Videos
VSF Long Range Colloquium: Natalia Ares (Oxford University)
Virtual Science Forum
03 From chips to qubits faster than human experts
MPI for the Science of Light
News
Press Release:
QuantrolOx and Zurich Instruments
team up
March 6th, 2024
Press Release:
QuantrolOx’s Quantum EDGE delivers over 100x speed
Feb 29th, 2024
Press Release:
QuantrolOx automates tune-ups of Quantware QPUs
Aug 30th, 2023
Press Release:
QuantrolOx launches automation software
Aug 10th, 2023
Sifted:
QuantrolOx raise €10.5m from EIC
Oct 14th, 2022
Press Release:
QuantrolOx partners with QuantWare
May 20th, 2022
Business Wire:
QpiAI and QuantrolOx sign a MoU
Apr 13th, 2022
Press Release:
QuantrolOx raise £1.4m in funding
Feb 24th, 2022
Blog
A Case for Open Architectures
Author: Vishal Chatrath
February 2023
Accelerating Quantum Futures
Author: Andrew Briggs
April 2022
Device Variability in Silicon
Author: Brandon Severin
March 2022