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Record W4385455407 · doi:10.1364/ol.497599

Experimental demonstration of deterministic quantum search for multiple marked states without adjusting the oracle

2023· article· en· W4385455407 on OpenAlexaff
Xin He, Wentao Zhao, Wang-Chu Lv, Chen-Hui Peng, Zhe Sun, Yong-Nan Sun, Qi-Ping Su, Chui‐Ping Yang

Bibliographic record

VenueOptics Letters · 2023
Typearticle
Languageen
FieldComputer Science
TopicQuantum Information and Cryptography
Canadian institutionsUniversity of Calgary
FundersNational Natural Science Foundation of China
KeywordsOpticsComputer scienceQuantumPhysicsOracleQuantum mechanics

Abstract

fetched live from OpenAlex

Grover's search algorithm is a well-known quantum algorithm that has been extensively studied and improved to increase its success rate and enhance its flexibility. However, most improved search algorithms require an adjustment of the oracle, which may not be feasible in practical problem-solving scenarios. In this work, we report an experimental demonstration of a deterministic quantum search for multiple marked states without adjusting the oracle. A linear optical setup is designed to search for two marked states, one in a 16-state database with an initial equal-superposition state and the other in an 8-state database with different initial nonequal-superposition states. The evolution of the probability of finding each state in the database is also measured and displayed. Our experimental results agree well with the theoretical predictions, thereby proving the feasibility of the search protocol and the implementation scheme. This work is a pioneering experimental demonstration of deterministic quantum search for multiple marked states without adjusting the oracle.

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)0.000
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 score0.930
Threshold uncertainty score0.265

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.031
GPT teacher head0.284
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 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

Citations4
Published2023
Admission routes1
Has abstractyes

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