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Quantum Encrypted Communication between Two IBMQ Systems Using Quantum Permutation Pad

2022· article· en· W4285814863 on OpenAlexaff
Maria Perepechaenko, Randy Kuang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicQuantum Computing Algorithms and Architecture
Canadian institutionsQuantropi (Canada)
Fundersnot available
KeywordsControlled NOT gateComputer scienceQuantum computerQubitEncryptionTheoretical computer scienceAlgorithmPermutation (music)Quantum algorithmQuantum cryptographyQuantum error correctionQuantumQuantum informationComputer engineeringComputer networkPhysicsQuantum mechanics

Abstract

fetched live from OpenAlex

We demonstrate an early functional implementation of the Kuang and Barbeau’s Quantum Permutation Pad (QPP) algorithm on the IBMQ physical quantum computers using the Qiskit development kit. Our implementation of the quantum encryption QPP algorithm uses 2-qubit permutation operators created from the compositions of a few CNOT and NOT gates. With inability to physically transfer qubits between two IBMQ machines we acted as two separate IBMQ computers and described how ideally two quantum systems can securely communicate using QPP. Since the physical qubits are still noisy, we use a simple error correction technique by choosing the correct state with the highest probability. Our implementation can be extended to a hybrid system that consist of a quantum computer communicating with a classical computer securely using QPP. This work can be considered as a toy example of the fully secure implementation of quantum encryption using QPP. Nevertheless, it is a promising first step towards secure quantum communication.

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 categoriesnone
Consensus categoriesnone
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 score0.473
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.000
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 head0.030
GPT teacher head0.280
Teacher spread0.250 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations11
Published2022
Admission routes1
Has abstractyes

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