Theory and Mitigation of Crosstalk on Quantum Information Processors
Bibliographic record
Abstract
Successfully implementing large-scale quantum computation has proven to be an exceptionally arduous task. Decoherence and imperfect control limit the coherent manipulation of large ensembles of particles. While quantum error correction provides robust schemes for executing quantum algorithms on error-prone systems, the methods usually assume that the errors are well-behaved and lie below some threshold. The burden of QEC can be substantial, and reaching error rates well below these thresholds can dramatically improve the processing capabilities of a device. \n \nA hierarchy of error processes exists with increasingly desirable properties at the cost of realism and generality. For example, quantum circuits subject to Markovian errors typically have higher error thresholds than those under general errors. We can further divide Markovian errors into coherent and incoherent processes, with the former having much lower thresholds. \n \nThis thesis examines crosstalk, a type of coherent error process, and mainly studies its role in superconducting quantum computing devices. \n \nThe first part of our work details a systematic framework for modeling crosstalk that occurs during the operation of a quantum computer, i.e., what happens on the device while performing gates. We break this crosstalk down into local and nonlocal effects. We show how to model local crosstalk on a digital computer without approximations efficiently. Unlike local crosstalk, nonlocal crosstalk cannot be modeled efficiently on a digital computer without approximations. Thus, we develop a framework for approximating the effect of nonlocal crosstalk. We observed a negligible difference between our approximation and the exact system dynamics in typical systems. \n \nThe second part of this thesis details our attempts to characterize and efficiently mitigate crosstalk on fixed-frequency superconducting qubits experimentally. The first obstacle we encountered was learning the crosstalk affecting a system. When the crosstalk is weak, existing methods prove difficult, so we developed a new approach to measure crosstalk. The second problem we needed to address was verifying that our model was correct. Using the results from our first measurements, we compare predicted evolutions with experimental data in a setting much different than the measurement procedure. We see excellent agreement between experiment and theory, indicating the model is reasonable. The last outstanding puzzle piece in this investigation is using this model to mitigate crosstalk, and our research is ongoing.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".