Pith. sign in

REVIEW 1 cited by

Data needs and challenges for quantum dot devices automation

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2312.14322 v3 pith:T7NOMGHH submitted 2023-12-21 cond-mat.mes-hall cs.DBcs.LGquant-ph

Data needs and challenges for quantum dot devices automation

classification cond-mat.mes-hall cs.DBcs.LGquant-ph
keywords quantumtuningautomationchallengesdevicesoperationscalableaccounted
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

Gate-defined quantum dots are a promising candidate system for realizing scalable, coupled qubit systems and serving as a fundamental building block for quantum computers. However, present-day quantum dot devices suffer from imperfections that must be accounted for, which hinders the characterization, tuning, and operation process. Moreover, with an increasing number of quantum dot qubits, the relevant parameter space grows sufficiently to make heuristic control infeasible. Thus, it is imperative that reliable and scalable autonomous tuning approaches are developed. This meeting report outlines current challenges in automating quantum dot device tuning and operation with a particular focus on datasets, benchmarking, and standardization. We also present insights and ideas put forward by the quantum dot community on how to overcome them. We aim to provide guidance and inspiration to researchers invested in automation efforts.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Large Scale Optimization of Disordered Hubbard Models through Tensor and Neural Networks

    cond-mat.mes-hall 2026-04 unverdicted novelty 6.0

    Neural networks trained on local 3x3 tensor-network charge-stability data can predict on-site disorder with high accuracy (R²>0.99) for the central dot in larger 5x5 disordered Hubbard model arrays, enabling scalable ...