Experimental Optimal Single Qubit Purification in an NMR Quantum Information Processor
Bibliographic record
Abstract
High quality single qubits are the building blocks in quantum information processing. But they are vulnerable to environmental noise. To overcome noise, purification techniques, which generate qubits with higher purities from qubits with lower purities, have been proposed. Purifications have attracted much interest and been widely studied. However, the full experimental demonstration of an optimal single qubit purification protocol proposed by Cirac, Ekert and Macchiavello [Phys. Rev. Lett. 82, 4344 (1999), the CEM protocol] more than one and half decades ago, still remains an experimental challenge, as it requires more complicated networks and a higher level of precision controls. In this work, we design an experiment scheme that realizes the CEM protocol with explicit symmetrization of the wave functions. The purification scheme was successfully implemented in a nuclear magnetic resonance quantum information processor. The experiment fully demonstrated the purification protocol, and showed that it is an effective way of protecting qubits against errors and decoherence.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".