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Record W4406785398 · doi:10.48550/arxiv.2501.13315

The Economics of an Open-Source Quantum Computer

2025· preprint· en· W4406785398 on OpenAlexfundno aff
Roger G. Melko

Bibliographic record

VenueArXiv.org · 2025
Typepreprint
Languageen
FieldComputer Science
TopicDistributed and Parallel Computing Systems
Canadian institutionsnot available
FundersInstitut Périmètre de physique théoriqueNatural Sciences and Engineering Research Council of CanadaGovernment of CanadaMinistry of Colleges and UniversitiesInnovation, Science and Economic Development Canada
KeywordsOpen sourceComputer scienceQuantumQuantum computerMathematical economicsEconomicsPhysicsQuantum mechanicsProgramming languageSoftware

Abstract

fetched live from OpenAlex

Open-source projects that aim to make their offerings public have competed against for-profit, proprietary companies in a number of domains. These open-source projects often arise in response to the offerings of proprietary companies in markets where products have already been commercialized. We assess what impact an open-source project might have when it enters the market for quantum computing - a market where the core technology is still being developed. We argue that an open-source quantum computer might alleviate market frictions that have impeded the development of a fault-tolerant quantum computer by providing the market with a possible mechanism for: 1.) benchmarking, 2.) the development of hardware agnostic technology, and 3.) improved liquidity on the supply side of the market for labor. Should these outcomes be realized, they may not only benefit the open-source project, but also the for-profit, proprietary companies operating in the ecosystem. In this respect, an open-source quantum project may play a more complementary role to the proprietary firms in the ecosystem, rather than a more competitive role. Our collective insights may have implications for other settings where an open-source project enters into a market where the core technology has yet to be commercialized.

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), Open science
Consensus categoriesOpen science
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 score0.436
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0080.008
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 head0.046
GPT teacher head0.282
Teacher spread0.236 · 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; both teacher heads agree on what is shown here.

Study designSimulation or modeling
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

Citations0
Published2025
Admission routes1
Has abstractyes

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