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Record W4401243876 · doi:10.1117/12.3027156

Towards deterministic quantum photonic information processing with quantum dot phase shifters

2024· article· en· W4401243876 on OpenAlexaff
Jacob Ewaniuk, Adam McCaw, Sofia Arranz Regidor, Stephen Hughes, Bhavin J. Shastri, Nir Rotenberg

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicNeural Networks and Reservoir Computing
Canadian institutionsQueen's University
Fundersnot available
KeywordsPhotonicsQuantum dotPhysicsQubitQuantum computerOptoelectronicsQuantum networkPhotonic integrated circuitQuantum informationQuantumElectronic engineeringQuantum mechanicsEngineering

Abstract

fetched live from OpenAlex

Semiconductor quantum dots (QDs) are a type of solid-state quantum emitter that can act as a near-ideal quantum light-matter interface when integrated with high-quality nanophotonic systems. Though QDs have typically been used to create state-of-the-art, on-demand single photon sources, here we widen the perspective on QDs, showing how to design quantum photonic integrated circuits based on both linear and nonlinear QD phase shifters. Specifically, we find that linear QD phase shifters can be used to realize cryogenically-compatible, fast, low-loss, and high-fidelity reconfigurable linear circuits. When paired with QDs that mediate interactions between photonic qubits, generating nonlinear phase shifts, deterministic quantum photonic logic gates can be achieved. Thus, our work paves the way for the realization of on-chip, cryogenically-compatible linear and nonlinear quantum photonic circuits, including quantum photonic neural networks, which can form the foundation for scalable and efficient quantum photonic technologies.

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 categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.986
Threshold uncertainty score1.000

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 communication0.0020.002
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 head0.017
GPT teacher head0.274
Teacher spread0.258 · 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.

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

Citations0
Published2024
Admission routes1
Has abstractyes

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