14 papers
Q-GAIN: A Python Package for Machine Learning and Physically Informed Analysis Applications
M. Doris, S. Guo, S. M. Koh +5
Here we describe the quantum gas analysis and inference (Q-GAIN) Python package, which enables rapid deployment of machine learning (ML) and physics-informed analysis techniques fo…
Overcoming Configuration Bottleneck: Modular Pathways to Stable Semiconductor Spin-Qubit Arrays
Justyna P. Zwolak, Anthony Sigillito
Over the past decade, semiconductor spin qubits have progressed from few-qubit demonstrations towards larger-scale devices fabricated in increasingly reproducible academic and indu…
Can machine learning for quantum-gas experiments be explainable?
I. B. Spielman amd J. P. Zwolak
Virtually all aspects of many-body atomic physics are challenging: experiments are technically demanding, datasets have become enormous, and the memory and CPU requirements for cla…
Report on reproducibility in condensed matter physics
A. Akrap, D. Bordelon, S. Chatterjee +20
We present recommendations to improve reproducibility and replicability in condensed matter physics. This area of physics has consistently produced both fundamental insights into t…
FAlCon: A unified framework for algorithmic control of quantum dot devices
Tyler J. Kovach, Daniel Schug, Zach D. Merino +3
As spin-based quantum systems scale, their setup and control complexity increase sharply. In semiconductor quantum dot (QD) experiments, device-to-device variability, heterogeneous…
QDFlow: A Python package for physics simulations of quantum dot devices
Donovan L. Buterakos, Sandesh S. Kalantre, Joshua Ziegler +2
Recent advances in machine learning (ML) have accelerated progress in calibrating and operating quantum dot (QD) devices. However, most ML approaches rely on access to large, repre…