Creating lattice gauge potentials in circuit QED: The bosonic Creutz ladder
Bibliographic record
Abstract
In this work we propose two protocols to make an effective gauge potential for microwave photons in circuit QED. The first scheme is based on coupled transmons whose on-site energies are harmonically modulated in time. We investigate the effect of various types of capacitive and inductive couplings, and the role of the phase difference between adjacent sites on creating a complex hopping rate between coupled qubits. The second method relies on the parametrically coupling the modes of a SQUID in a resonator and controlling the hopping phase via a coherent pump. Both proposals can be readily realized in a superconducting circuit with the existing technology and are suitable for scalable lattices. As an example benefiting from these complex-valued hopping terms, we simulated the behavior of a plaquette of bosonic Creutz ladder as one of the important models with interdisciplinary interest in various branches of physics. Our results clearly show the emergence of chiral edge modes and directional transport between lattice sites. Combined with intrinsic nonlinearity of the transmon qubits such lattices would be an ideal platform for simulating many different Hamiltonians such as the Bose--Hubbard model with nontrivial gauge fields. Important direct applications of the presented results span a broad range from signal processing in nonreciprocal transport to quantum simulation of gauge-invariant models in fundamental physics.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".