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Engineering qubit dynamics in open systems with photonic synthetic lattices

2025· preprint· en· W4405173323 on OpenAlexafffund
Francesco Di Colandrea, Tareq Jaouni, John Grace, Dilip Paneru, Mirko Arienzo, Alessio D’Errico, Ebrahim Karimi

Bibliographic record

VenuePhysical Review Research · 2025
Typepreprint
Languageen
FieldComputer Science
TopicNeural Networks and Reservoir Computing
Canadian institutionsNational Research Council CanadaMcGill UniversityUniversity of Ottawa
FundersMinistero dell'Università e della RicercaCanada Research Chairs
KeywordsQubitPhotonicsDynamics (music)Computer sciencePhysicsQuantum mechanicsQuantumAcoustics

Abstract

fetched live from OpenAlex

The evolution of a quantum system interacting with an environment can be described as a unitary process acting on both the system and the environment. In this framework, the system's evolution can be predicted by tracing out the environmental degrees of freedom. Here, we establish a precise mapping between the global unitary dynamics and the quantum operation involving the system, wherein the system is a single qubit, and the environment is modeled as a discrete lattice space. This approach enables the implementation of arbitrary noise operations on single-polarization qubits using a minimal set of three liquid-crystal metasurfaces, whose transverse distribution of the optic axes can be patterned to reproduce the target process. We experimentally validate this method by simulating common noise processes, such as phase errors and depolarization. Besides providing a practical solution for quantum state purification, this work demonstrates a versatile approach for the simulation of open qubit dynamics, with potential implications for quantum error correction and environment-induced quantum phase transitions.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.074
GPT teacher head0.404
Teacher spread0.331 · 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 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
Published2025
Admission routes2
Has abstractyes

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