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Record W4308241581 · doi:10.21203/rs.3.rs-2236100/v1

Experimental demonstration of input-output indefiniteness in a single quantum device

2022· preprint· en· W4308241581 on OpenAlexaff
Yu Guo, Zixuan Liu, Hao Tang, Xiao‐Min Hu, Bi‐Heng Liu, Yun‐Feng Huang, Chuan‐Feng Li, Guang-Can Guo, Giulio Chiribella

Bibliographic record

VenueResearch Square · 2022
Typepreprint
Languageen
FieldComputer Science
TopicQuantum Information and Cryptography
Canadian institutionsPerimeter Institute
FundersFundamental Research Funds for the Central UniversitiesNational Key Research and Development Program of ChinaChina Postdoctoral Science FoundationUniversity of Science and Technology of ChinaNational Natural Science Foundation of China
KeywordsQuantumSuperposition principleArrow of timeComputer sciencePhysicsQuantum mechanicsTheoretical physics

Abstract

fetched live from OpenAlex

Abstract At the fundamental level, the dynamics of quantum fields is invariant under the combination of time reversal, charge conjugation, and parity inversion. This symmetry implies that a broad class of effective quantum evolutions are bidirectional, meaning that the exchange of their inputs and outputs gives rise to valid quantum evolutions. Recently, it has been observed that quantum theory is theoretically compatible with a family of operations in which the roles of the inputs and outputs is indefinite. However, such operations have not been demonstrated in the laboratory so far. Here we experimentally demonstrate input-output indefiniteness in a photonic setup, demonstrating an advantage in a quantum game and showing incompatibility with a definite input-output direction by more than 69 standard deviations. Our results establish input-output indefiniteness as a new resource for quantum information protocols, and enable the table-top simulation of hypothetical scenarios where the arrow of time could be in a quantum superposition.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.114
GPT teacher head0.384
Teacher spread0.269 · 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 designBench or experimental
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

Citations7
Published2022
Admission routes1
Has abstractyes

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