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Building a spin quantum bit register using semiconductor nanowires

2010· article· en· W1983767049 on OpenAlexaff
Jonathan Baugh, J. Fung, James Mracek, Ray LaPierre

Bibliographic record

VenueNanotechnology · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum and electron transport phenomena
Canadian institutionsMcMaster UniversityUniversity of Waterloo
Fundersnot available
KeywordsQubitSpin (aerodynamics)Quantum computerQuantum decoherenceNanowireQuantum technologySemiconductorQuantum dotQuantum gateSpin transistorOptoelectronicsSpin engineeringPhysicsMaterials scienceQuantum sensorQuantumQuantum simulatorCondensed matter physicsQuantum mechanicsOpen quantum systemSpin polarization

Abstract

fetched live from OpenAlex

This paper reviews recent advances in engineering spin quantum bits (qubits) in semiconductor quantum dots and describes an approach based on top-gated semiconductor nanowire devices. Fast electrical single-spin manipulation is achievable, in principle, using the spin-orbit interaction intrinsic to III-V materials, such as InAs, in concert with AC electric fields. Combined with sub-nanosecond gate control of the nearest-neighbor exchange interaction and spin readout by spin-to-charge conversion, a fully electrical solid-state quantum processor is within reach. We outline strategies for spin manipulation, robust readout and mitigation of decoherence due to nuclear fields that, when combined in a single device, should give a viable multi-qubit testbed and a building block for larger scale quantum devices.

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)0.000
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
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.014
GPT teacher head0.266
Teacher spread0.252 · 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

Citations12
Published2010
Admission routes1
Has abstractyes

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