Analysis of the Effect of Entanglement Operators and the Scalability of Players’ Payoff Computation in N‐Player Quantum Games
Bibliographic record
Abstract
Abstract Entanglement is one of the most vital resources in quantum computing. Particularly, in quantum game theory, entanglement holds significant importance since it defines the interactions and the information exchange between two or more players. In this research work, a new set of entanglement operators is introduced for N‐player quantum games. This study explores how their application affects each player's behavior throughout the game. Furthermore, the entangling capabilities are tested and evaluated for a wide range of entanglement operator angles, by exploiting the Von Neumann entropy metric, both on a simulator and on a real IBM Quantum system. Their hardware efficiency, against state‐of‐the‐art quantum games entanglement operators, is assessed in terms of circuit depths and fidelity, and showcases their great potential as multiplayer quantum games operators. Finally, three methods are compared for computing the players’ payoffs, regarding their scalability and efficiency. This study shows that in all payoff cases, the quantum game results, computed by the quantum hardware, are in accordance with the simulated ones.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".