Native three-body interactions in a superconducting lattice gauge quantum simulator
Bibliographic record
Abstract
While universal quantum computers remain under development, analog quantum simulators offer a powerful alternative for understanding complex systems in condensed matter, chemistry, and high-energy physics. One compelling application is the characterization of real-time lattice gauge theories (LGTs). LGTs are nonperturbative tools, utilizing discretized space-time to describe gauge-invariant models. They hold immense potential for understanding fundamental physics but require enforcing local constraints analogous to electromagnetism's Gauss's law. These constraints, which arise from gauge symmetries and dictate the form of the interaction between matter and gauge fields, are a significant challenge for simulators to enforce. Implementing these constraints at the hardware level in analog simulations is crucial. This requires realizing multibody interactions between matter and gauge-field elements, enabling them to evolve together while suppressing unwanted two-body interactions that violate the gauge symmetry. In this paper, we propose and implement a parametrically activated three-qubit interaction within a circuit quantum electrodynamics architecture. We experimentally demonstrate a minimal $\text{U}(1)$ spin-$\frac{1}{2}$ model with a time evolution that intrinsically satisfies Gauss's law in the system. This design serves as the foundational block for simulating LGTs on a superconducting photonic lattice.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".