Bibliographic record
Abstract
Quantum games offer situations where quantum information theory may help in solving or improving games with lack of information. For example, one may rely upon properties of entanglement for showing the superiority of a quantum player who is allowed to use quantum operations against a classical player (penny flipover [5]). Quantum communication properties enter into play in cooperative games (prisoners game, [3], guessing number [10]). An introduction to this field and an overview of important results are proposed in [7, 8]. But games may also help to make notions easier to understand, and new ways of reasoning to apprehend. The objective of this paper is to formalize and study a simple game with qubits using quantum notions of measurement and superposition, while keeping a simple formalism so that knowing quantum mechanics is not necessary to play the game. We generalize Conway's classical octal games to quantum octal games, and we solve the quantum combinatorial game QO.07 by giving a winning strategy for it. A playable version of the studied qubit game is available at the address .
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".