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Record W2978124974 · doi:10.1109/icdis.2019.00039

A Survey on the Impacts of Quantum Computers on Information Security

2019· article· en· W2978124974 on OpenAlexaff
Njorbuenwu Maryann, Bobby Swar, Pavol Zavarsky

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicQuantum Computing Algorithms and Architecture
Canadian institutionsConcordia University of Edmonton
Fundersnot available
KeywordsComputer scienceQuantum computerQuantum informationQuantum entanglementInformation securityQubitNISTQuantumTheoretical computer scienceComputer securityQuantum cryptographyQuantum mechanicsPhysics

Abstract

fetched live from OpenAlex

Quantum computers differ from traditional computers because unlike traditional computers that use and process information in bits (0 or 1), the unit of information in quantum computers is the quantum bit, or qubit, that can represent additional states beyond ones and zeros at the same time. The additional states called superimposition and entanglement facilitate phenomenal processing speed of quantum computers. The emergence of quantum computers has raised many concerns in various areas of information security. This paper employs a literature survey methodology to elucidate the positive and negative impacts of quantum computers on information security. This paper further addresses the concern that quantum computers will negatively impact information security by examining all recommended information security and privacy controls of the NIST SP 800-53 Rev. 5 catalog of controls. Furthermore, the paper briefly outlines the recent work towards quantum-resistant standards.

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)0.007
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.011
Science and technology studies0.0010.002
Scholarly communication0.0020.005
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.010
GPT teacher head0.227
Teacher spread0.217 · 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
GenreReview

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

Citations21
Published2019
Admission routes1
Has abstractyes

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