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Record W4309812876 · doi:10.1149/ma2022-02321207mtgabs

(Invited) Optoelectronic Quantum Information Processing: An All-Group IV Integrated Platform

2022· article· en· W4309812876 on OpenAlexaff
Oussama Moutanabbir, Patrick Del Vecchio, Anis Attiaoui, Gabriel Fettu, Nicolas Rotaru, Simone Assali

Bibliographic record

VenueECS Meeting Abstracts · 2022
Typearticle
Languageen
FieldComputer Science
TopicNeural Networks and Reservoir Computing
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsPhotonQubitHeterojunctionQuantum entanglementSemiconductorScalabilityQuantum informationPhysicsQuantum information scienceQuantum computerOptoelectronicsQuantumComputer scienceEngineering physicsQuantum mechanics

Abstract

fetched live from OpenAlex

Achieving coherent optical photon-to-spin conversion is a long-sought-after strategy for surmounting current fundamental limits in optical schemes that hinder the long-distance distribution of entanglement. Moreover, photon-to-spin interfaces are also essential for a direct mapping of the quantum information encoded in photon flying qubits to stationary spin processors. However, the lack of scalable materials offering an efficient interaction with optical photons along with optimal spin properties remains a formidable obstacle hindering the development of these quantum technological components. With this perspective, this presentation will discuss strategies to address these challenges by leveraging the degrees of freedom offered by group IV (Si)GeSn semiconductors, namely strain and composition, to tailor the electronic structure and eventually fulfill these prerequisites. These innovative systems do not only have the potential to enable coherent photon-to-spin interfaces, but because of their compatibility with the semiconductor industry they will also offer scalability, manufacturability, and cost-effectiveness. We will show that this family of semiconductors provide an additional flexibility to control the charge carrier states and achieve a selective confinement of holes. The latter benefit from a quiet quantum environment that has been at the core of increasingly reliable quantum processors and memories. However, most if not all available experimental studies of two-dimensional gas systems have been thus far focused on heavy-hole (HH) states. This is attributed to the nature of the heterostructures currently available (e.g, Ge/SiGe, InGaAs/GaAs), where compressive strain lifts the valence band degeneracy and leaves HH states energetically well above the light-hole (LH) states. We will demonstrate that tensile strained Ge/GeSn quantum wells alleviate these limitations and allow to selectively confine LH provided the strain is higher than 1%. This requires strain relaxed, high Sn content GeSn buffer layers to be used to grow Ge quantum wells with LH ground state, high g-factor anisotropy, and a tunable splitting of the hole subbands. The optical and electronic properties of these low-dimensional systems will be described and discussed. Spin injection and coherent control will also be addressed. Additionally, qubit designs exploiting the ability to engineer LH states and the Ge large spin-orbit coupling allowing fast all-electrical spin-manipulation schemes will also be presented and discussed.

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 score0.052
Threshold uncertainty score0.860

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 communication0.0010.002
Open science0.0010.001
Research integrity0.0000.001
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 head0.020
GPT teacher head0.245
Teacher spread0.225 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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