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Disentangling the sources of ionizing radiation in superconducting qubits

2023· article· en· W4318707249 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe European Physical Journal C · 2023
Typearticle
Languageen
FieldEngineering
TopicRadiation Effects in Electronics
Canadian institutionsQueen's University
FundersLaboratori Nazionali del Gran SassoOffice of ScienceMinistero dell’Istruzione, dell’Università e della RicercaU.S. Department of Energy
KeywordsQubitQuantum decoherencePhysicsMeasure (data warehouse)Ionizing radiationSuperconductivityQuantum computerWork (physics)QuantumNuclear physicsComputational physicsComputer scienceQuantum mechanicsIrradiationDatabase

Abstract

fetched live from OpenAlex

Abstract Radioactivity was recently discovered as a source of decoherence and correlated errors for the real-world implementation of superconducting quantum processors. In this work, we measure levels of radioactivity present in a typical laboratory environment (from muons, neutrons, and $$\gamma $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>γ</mml:mi> </mml:math> -rays emitted by naturally occurring radioactive isotopes) and in the most commonly used materials for the assembly and operation of state-of-the-art superconducting qubits. We present a GEANT-4 based simulation to predict the rate of impacts and the amount of energy released in a qubit chip from each of the mentioned sources. We finally propose mitigation strategies for the operation of next-generation qubits in a radio-pure environment.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.193

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.235
Teacher spread0.221 · 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