Balanced Cross-Kerr Coupling for Superconducting Qubit Readout
Bibliographic record
Abstract
Dispersive readout, the standard method for measuring superconducting qubits, is limited by multiphoton qubit-resonator processes arising even at moderate drive powers. These processes degrade performance, causing dispersive readout to lag behind single- and two-qubit gates in both speed and fidelity. In this Letter, we propose a novel readout method, termed junction readout. Junction readout leverages the nonperturbative cross-Kerr interaction resulting from coupling a qubit and a resonator via a Josephson junction. Furthermore, by adding a capacitive coupling in parallel to the junction, Purcell decay induced by the exchange coupling can be suppressed. We also show that junction readout is more robust against deleterious multiphoton processes, and offers greater flexibility for resonator frequency allocation. Crucially, junction readout achieves superior performance compared to dispersive readout while maintaining similar hardware overhead. Numerical simulations show that junction readout can achieve fidelity exceeding 99.99% in under 30 ns of integration time, making it a promising alternative for superconducting qubit readout with current hardware.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".