A walk through quantum noise: a study of error signatures and characterization methods
Bibliographic record
Abstract
The construction of large scale quantum computing devices might be one of the most exciting \nand promising endeavors of the 21st century, but it also comes with many challenges. \nAs quantum computers are supplemented with more registers, their error profile generally \ngrows in complexity, rendering the enterprise of quantifying the reliability of quantum computations \nincreasingly difficult through naive characterization techniques. In the last decade, \na lot of efforts has been directed toward developing highly scalable benchmarking schemes. \nA leading family of characterization methods built upon scalable principles is known as randomized \nbenchmarking (RB). \nIn this thesis, many tools are presented with the objective of improving the scalability, \nand versatility of RB techniques, as well as demonstrating their reliability under various \nerror models. \nThe first part of this work investigates the connection between the error of individual \ncircuit components and the error of their composition. Before reasoning about intricate circuit \nconstructions, it is shown that there exists a well-motivated way to define decoherent \nquantum channels, and that every channel can be factorized into a unitary-decoherent composition. \nThis dichotomy carries to the circuit evolution of important error parameters by \nassuming realistic error scenarios. Those results are used to improve the confidence interval \nof RB diagnoses and to reconcile experimentally estimated parameters with physically and \noperationally meaningful quantities. \nIn the second part of this thesis, various RB schemes are either developed or more rigorously \nanalyzed. A first result consists of the introduction of “dihedral benchmarking”, a \ntechnique which, if performed in conjunction with standard RB protocols, enables the characterization \nof operations that form a universal gate-set. Finally, rigorous analysis tools are \nprovided to demonstrate the reliability of a highly scalable family of generator-based RB \nprotocols known as direct RB.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".