160 citations · 1.3k across the 30 of their papers we have counts for
4 papers · 1 filter
Deep Reinforcement Learning for Efficient Measurement of Quantum Devices
V. Nguyen, S. B. Orbell, D. T. Lennon +9
Deep reinforcement learning is an emerging machine learning approach which can teach a computer to learn from their actions and rewards similar to the way humans learn from experie…
Measuring the thermodynamic cost of timekeeping
A. N. Pearson, Y. Guryanova, P. Erker +4
All clocks, in some form or another, use the evolution of nature towards higher entropy states to quantify the passage of time. Due to the statistical nature of the second law and…
Quantum device fine-tuning using unsupervised embedding learning
N. M. van Esbroeck, D. T. Lennon, H. Moon +8
Quantum devices with a large number of gate electrodes allow for precise control of device parameters. This capability is hard to fully exploit due to the complex dependence of the…
Machine learning enables completely automatic tuning of a quantum device faster than human experts
H. Moon, D. T. Lennon, J. Kirkpatrick +10
Device variability is a bottleneck for the scalability of semiconductor quantum devices. Increasing device control comes at the cost of a large parameter space that has to be explo…