Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.026 |
| Scholarly communication | 0.010 | 0.020 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".