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Record W2161027938 · doi:10.1126/science.1229957

Universal Computation by Multiparticle Quantum Walk

2013· article· en· W2161027938 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueScience · 2013
Typearticle
Languageen
FieldComputer Science
TopicQuantum Computing Algorithms and Architecture
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsQuantum walkQuantum computerQuantum algorithmQuantumPhysicsQuantum processRandom walkStatistical physicsQuantum mechanicsQuantum dynamicsMathematics

Abstract

fetched live from OpenAlex

Computing Power of Quantum Mechanics There is much interest in developing quantum computers in order to perform certain tasks much faster than, or that are intractable for, a classical computer. A general quantum computer, however, requires the fabrication and operation a number of quantum logic devices (see the Perspective by Franson ). Broome et al. (p. 794 , published online 20 December) and Spring et al. (p. 798 , published online 20 December) describe experiments in which single photons and quantum interference were used to perform a calculation (the permanent of a matrix) that is very difficult on a classical computer. Similar to random walks, quantum walks on a graph describe the movement of a walker on a set of predetermined paths; instead of flipping a coin to decide which way to go at each point, a quantum walker can take several paths at once. Childs et al. (p. 791 ) propose an architecture for a quantum computer, based on quantum walks of multiple interacting walkers. The system is capable of performing any quantum operation using a subset of its nodes, with the size of the subset scaling favorably with the complexity of the operation.

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.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.943
Threshold uncertainty score0.391

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.006
GPT teacher head0.224
Teacher spread0.218 · 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