Hybrid quantum-classical photonic neural networks
Bibliographic record
Abstract
Neuromorphic (brain-inspired) photonics leverages photonic chips to accelerate neural networks, offering high-speed and energy efficient solutions for use in datacom, autonomous vehicles, or other time sensitive applications. However, the limited size of photonic neural networks limits the complexity of solvable tasks. A natural candidate to provide increased complexity is quantum computing and its exponential speedup capabilities. Specifically, we explore photonic continuous variable (CV) quantum computation. Combining classical networks with trainable CV quantum circuits yields hybrid networks that provide significant trainability and accuracy improvements. On a classification task, hybrid networks achieve the same accuracy as fully classical networks that are twice the size. When noise is applied to the network parameters, the hybrid and classical networks maximize accuracy below the expected on-chip noise level. These results demonstrate that hybrid networks can achieve increased performance with smaller network sizes, providing a promising route to scalable neuromorphic photonic processing.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".