Bibliographic record
Abstract
Magic state distillation (MSD) is a quantum algorithm that enables performing logical non-Clifford gates with, in principle, arbitrarily low noise levels. It is typically assumed herein that logical Clifford gates can be executed without noise. Therefore, MSD is a standard subroutine used to obtain a fault-tolerant universal set of quantum gate operations on error-corrected logical qubits. Well-known schemes conventionally rely on performing operator measurements and post-selection based on the measurement result, which makes distillation protocols non-deterministic in the presence of noise. In this work, we adapt the 15-to-1 MSD protocol such that it deterministically suppresses noise by using a coherent feedback network on the output states without the need to perform individual-qubit measurements. These advantages over textbook MSD come at the price of reducing the noise suppression per round from O(p3) to O(p2). Our technique can be applied to any MSD protocol with an acceptance rate of 1 in the absence of noise. It may be desirable to use our scheme if the coherent feedback network can be executed faster and more reliably than the measurements, and/or if logical clock cycles in the quantum processor should be kept synchronous at all times. Our result broadens the path of potential experimental realizations of MSD in near-term devices and advances the development of fault-tolerant quantum computers with practical use.
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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 | 0.000 | 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".