In Situ Noise Characterization of the D-Wave Quantum Annealer
Bibliographic record
Abstract
Various sources of noise limit the performance of quantum computers based on superconducting flux qubits by altering their states in an uncontrolled manner throughout computations and reducing their coherence time. In quantum annealers, this introduces fluctuations to the linear constants of the original problem Hamiltonian, such that they find the ground states of problems perturbed from those programmed. Here we describe how to turn this drawback into a method to probe the frequency dependence of the noise in situ of the D-Wave 2000Q quantum annealer. The method relates the autocorrelation of the readout-state of a qubit repeatedly collapsed from uniform superposition to that of the noise impingent on the qubit. We show that this leads to an estimate for the noise spectral density affecting D-Wave qubits under normal operating conditions. The method is general and can be used to characterize noise in all architectures for quantum annealing.
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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 | 0.000 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".