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Record W2943531133 · doi:10.2172/1527395

Quantum Computing as a High School Module

2019· report· en· W2943531133 on OpenAlexfundno aff
Anastasia Perry, Ranbel F. Sun, Ciaran Hughes, Joshua Isaacson, Jessica Turner

Bibliographic record

Venuenot available
Typereport
Languageen
FieldComputer Science
TopicQuantum Computing Algorithms and Architecture
Canadian institutionsnot available
FundersUniversity of Colorado BoulderOffice of ScienceUniversity of WaterlooUniversity of St AndrewsFermilabHigh Energy PhysicsU.S. Department of Energy
KeywordsQuantum computerBridge (graph theory)Intersection (aeronautics)Computer scienceKey (lock)Field (mathematics)QuantumMathematics educationComputational sciencePhysicsQuantum mechanicsMathematicsEngineeringPure mathematics

Abstract

fetched live from OpenAlex

Quantum computing is a growing field at the intersection of physics andcomputer science. This module introduces three of the key principles thatgovern how quantum computers work: superposition, quantum measurement, andentanglement. The goal of this module is to bridge the gap between popularscience articles and advanced undergraduate texts by making some of the moretechnical aspects accessible to motivated high school students. Problem setsand simulation based labs of various levels are included to reinforce theconceptual ideas described in the text. This is intended as a one week coursefor high school students between the ages of 15-18 years. The course begins byintroducing basic concepts in quantum mechanics which are needed to understandquantum computing.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.201
Threshold uncertainty score0.673

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0050.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.2010.072

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 head0.017
GPT teacher head0.271
Teacher spread0.253 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations19
Published2019
Admission routes1
Has abstractyes

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