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Record W2954955923 · doi:10.22215/etd/2017-11829

Negotiating a Quantum Computation Network: Mechanics, Machines, Mindsets

2017· dissertation· en· W2954955923 on OpenAlexaff
Derek Noon

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldComputer Science
TopicQuantum Computing Algorithms and Architecture
Canadian institutionsCarleton University
Fundersnot available
KeywordsMindsetQuantum computerNegotiationComputer scienceContext (archaeology)SoftwareComputationQuantumTheoretical computer scienceSoftware engineeringManagement scienceArtificial intelligenceSociologyEngineeringSocial scienceProgramming language

Abstract

fetched live from OpenAlex

This dissertation describes the origins, development, and distribution of quantum computing from a socio-technical perspective. It depicts quantum computing as a result of the negotiations of heterogeneous actors using the concepts of ANT and socio-technical analyses of computing and infrastructure more generally. It draws on two years of participant observation and interviews with the hardware and software companies that developed, sold, and distributed both machines and a mindset for a new approach to computing: adiabatic quantum computation (AQC). It illustrates how a novel form of computation and software writing was developed by challenging and recoding the usual distinctions between digital and analogue computing, and discusses how the myriad controversies and failures attending quantum computing were resolved provisionally through a series of human and non-human negotiations. These negotiations disrupted, scrambled, and reconstituted what we usually understand as hardware, software, and mindset, and permitted a disruptive' technology to gain common acceptance in several high profile scientific, governmental, and financial institutions. It is the relationalities established across these diverse processes that constitute quantum computing, and consequences of this account of computation are considered in the context of digital media theory, industrial histories of computing, and socio-technical theories of technological innovation. Many sources of support helped me through the PhD program. I'm grateful to Mitacs for its financial support of this research, and for providing me such good STEM peers/research subjects.

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 categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.753
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0020.000
Research integrity0.0000.001
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 head0.012
GPT teacher head0.270
Teacher spread0.258 · 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.

Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations11
Published2017
Admission routes1
Has abstractyes

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