Imperfect Quantum Photonic Neural Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Quantum photonic neural networks are variational photonic circuits that can be trained to implement high‐fidelity quantum operations. However, work‐to‐date has assumed idealized components, including a perfect π Kerr nonlinearity. This work investigates the limitations of non‐ideal quantum photonic neural networks that suffer from fabrication imperfections leading to unbalanced photon loss and imperfect routing, and weak nonlinearities, showing that they can learn to overcome most of these errors. Using the example of a Bell‐state analyzer, the results demonstrate that there is an optimal network size, which balances imperfections versus the ability to compensate for lacking nonlinearities. With a sub‐optimal effective Kerr nonlinearity, it is shown that a network fabricated with current state‐of‐the‐art processes can achieve an unconditional fidelity of 0.905 that increases to 0.999999 if it is possible to precondition success on the detection of a photon in each logical photonic qubit. These results provide a guide to the construction of viable, brain‐inspired quantum photonic devices for emerging quantum technologies.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it