Near-ultrastrong nonlinear light-matter coupling in superconducting circuits
Bibliographic record
Abstract
Light-matter interaction between an atom and an electromagnetic resonator is ubiquitous in quantum technologies. Although linear light-matter coupling $$g{\hat{\sigma }}_{x}(\hat{a}+{\hat{a}}^{{{\dagger}} })$$ can reach the ultrastrong regime g/ω > 10−1, nonlinear light-matter coupling $$\frac{\chi }{2}{\hat{\sigma }}_{z}{\hat{a}}^{{{\dagger}} }\hat{a}$$ is typically perturbative and limited to χ/ω < 10−2. Nonlinear coupling has the advantage of commuting with the atomic $${\hat{\sigma }}_{z}$$ and photonic $${\hat{a}}^{{{\dagger}} }\hat{a}$$ Hamiltonian, allowing for fundamental operations such as quantum-non-demolition measurement. Here, we use a superconducting circuit to demonstrate the experimental realization of near-ultrastrong χ/ω = (4.852 ± 0.006) × 10−2. We also show signatures of light-light nonlinear coupling ( $$\chi {\hat{a}}^{{{\dagger}} }\hat{a}{\hat{b}}^{{{\dagger}} }\hat{b}$$ ) and χ/2π = 580.3 ± 0.4 MHz matter-matter nonlinear coupling ( $$\frac{\chi }{4}{\hat{\sigma }}_{z,a}{\hat{\sigma }}_{z,b}$$ ), representing the largest reported ZZ interaction between two coherent qubits. Such advances in the nonlinear coupling strength of light, matter modes enable new physical regimes and could lead to orders of magnitude faster qubit readout and gates. Nonlinear light-matter coupling has applications in quantum technologies, for instance in quantum-non-demolition measurements, but its strength is typically limited. Here the authors demonstrate near-ultrastrong nonlinear light-matter coupling in a superconducting circuit with two transmons and a quarton coupler.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".