Source to detector simulation of quantum photonic integrated circuits
Bibliographic record
Abstract
Quantum photonic devices are typically based on either discrete variable (DV) or continuous variable (CV) principles. In either case, their performance must meet strict “fidelity” requirements, especially when scaled using integrated photonic circuitry. DV devices must be robust to imperfections such as timing delays between photons, unwanted spectral correlations present in photons from nonideal single photon sources and variations in circuit components. Furthermore, it becomes increasingly important to have a methodology for characterizing the performance of devices in the presence of such errors. Here, we present a method for simulating quantum photonic integrated circuits (PICs) and use it to model the behavior of a circuit constructed to model the evolution of a quantum state subject to a Bose-Hubbard Hamiltonian, introducing variations to the coupling gaps in the directional couplers inside the circuit. Input states consisting of both indistinguishable and distinguishable photons are modelled. Compact models for the directional couplers and other photonic circuit elements that are parameterized for a specific fabrication process can be incorporated into the circuit using a foundry-associated compact model library, ensuring consistency between circuit design and manufactured component. In CV applications, the degree of squeezing provided by some nonlinear element is often a key metric. Here we describe how the degree of squeezing produced by spontaneous four-wave mixing (SFWM) in a microring resonator can be modelled in the low-power limit, accounting for the effects of self-phase modulation (SPM), cross-phase modulation (XPM), and component losses.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".