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Record W4402408670 · doi:10.1103/physrevb.110.125414

Impact of electrostatic crosstalk on spin qubits in dense CMOS quantum dot arrays

2024· article· en· W4402408670 on OpenAlexaff
Jesús D. Cifuentes, Tuomo Tanttu, Paul Steinacker, Santiago Serrano, Ingvild Hansen, James Slack-Smith, Will Gilbert, Jonathan Y. Huang, Ensar Vahapoglu, Ross C. C. Leon, Nard Dumoulin Stuyck, Kohei M. Itoh, N. V. Abrosimov, Hans-Joachim Pohl, M. L. W. Thewalt, Arne Laucht, Chih Hwan Yang, Christopher C. Escott, Fay E. Hudson, Wee Han Lim, Rajib Rahman, Andrew S. Dzurak, André Saraiva

Bibliographic record

VenuePhysical review. B./Physical review. B · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum and electron transport phenomena
Canadian institutionsSimon Fraser University
FundersArmy Research OfficeAustralian Research Council
KeywordsQubitCrosstalkQuantum dotCMOSPhysicsSuperconducting quantum computingSpin (aerodynamics)OptoelectronicsCondensed matter physicsQuantum mechanicsQuantumOptics

Abstract

fetched live from OpenAlex

The abundance of historical investment in the integrated circuit sector is playing a crucial role in the quantum era. Progress in semiconductor spin qubits recently enabled the incorporation of foundry-level fabrication techniques into quantum processors, paving the way for scalable and commercial quantum computing. However, current CMOS quantum processors, which use dense gate arrays to define quantum dots, are prone to electrostatic crosstalk due to capacitive coupling. This crosstalk, transferred through spin-orbit interactions, impacts the Larmor frequency of the spin qubits. Utilizing advanced simulation tools and measurements in several state-of-the-art CMOS quantum devices, the authors formulate here a framework illustrating how electric fields interact with electron spins in intricate arrays, offering vital insights for the development of this quantum technology.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.373
Teacher spread0.360 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2024
Admission routes1
Has abstractyes

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