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Record W3214686435 · doi:10.1038/s41534-022-00590-8

Demonstration of long-range correlations via susceptibility measurements in a one-dimensional superconducting Josephson spin chain

2022· article· en· W3214686435 on OpenAlexaff
Daniel Tennant, Xi Dai, A. J. Martinez, Robbyn Trappen, Denis Melanson, Muhammet Ali Yurtalan, Yongchao Tang, Salil Bedkihal, Rui Yang, S P Novikov, Jeffrey A. Grover, Steven Disseler, James I. Basham, Rabindra Das, David Kim, A. J. Melville, Bethany M. Niedzielski, S. J. Weber, J. L. Yoder, Andrew J. Kerman, E. Mozgunov, Daniel A. Lidar, Adrian Lupaşcu

Bibliographic record

Venuenpj Quantum Information · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum and electron transport phenomena
Canadian institutionsUniversity of Waterloo
FundersLawrence Livermore National LaboratoryArmy Research OfficeNational Nuclear Security AdministrationOffice of the Director of National IntelligenceAdvanced Research Projects AgencyIntelligence Advanced Research Projects ActivityDefense Advanced Research Projects AgencyNIH Office of the DirectorU.S. Department of Energy
KeywordsQubitSuperconducting quantum computingFlux qubitQuantum entanglementJosephson effectSuperconductivityPhysicsChain (unit)Quantum mechanicsCondensed matter physicsQuantumQuantum computer

Abstract

fetched live from OpenAlex

Abstract Spin chains have long been considered an effective medium for long-range interactions, entanglement generation, and quantum state transfer. In this work, we explore the properties of a spin chain implemented with superconducting flux circuits, designed to act as a connectivity medium between two superconducting qubits. The susceptibility of the chain is probed and shown to support long-range, cross-chain correlations. In addition, interactions between the two end qubits, mediated by the coupler chain, are demonstrated. This work has direct applicability in near term quantum annealing processors as a means of generating long-range, coherent coupling between qubits.

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 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.169
Threshold uncertainty score0.904

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.031
GPT teacher head0.245
Teacher spread0.214 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations7
Published2022
Admission routes1
Has abstractyes

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