Comparative analysis of projector‐based compression and quantum autoencoders
Bibliographic record
Abstract
Quantum compression is a cornerstone of quantum information science, promising efficient encoding of large quantum states into fewer qubits while retaining high fidelity. In this work, we compare two pivotal strategies for compressing an i.i.d. quantum source under small-scale (few-qubit) regimes. The first strategy relies on a projector-based compression method that discards low-probability components in the state’s spectral decomposition, achieving near-optimal asymptotic compression in principle. The second strategy employs a quantum autoencoder (QAE), a variational circuit that learns to embed essential information into fewer qubits. We implement both approaches in simulation, gauge their performance, and explore how many qubits can be ”saved” in practice. Our results highlight how projector-based compression provides a near-optimal yet analytically prescriptive solution, whereas autoencoders offer a machine-learning–style route that can adapt to varied input states.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".