arXiv:2606.02655v1 Announce Type: cross Abstract: External regret certifies stability only against replacing one's behavior by a fixed alternative. In a quantum game, this misses a natural physical move: a player can apply a local completely positive trace-preserving (CPTP) map to the state it actually received or prepared. We introduce coherent swap regret as the regret benchmark against all such local CPTP deviations, and give an algorithm achieving $O(\sqrt{dT\log d})$ coherent swap regret via entropic mirror ascent on the CPTP Choi slice with a fixed-point play rule. The main result is a t
This research introduces a new regret benchmark for stability in quantum game theory, addressing a gap in previous models by accounting for quantum physical moves.
It provides a robust theoretical framework for analyzing and building stable quantum AI systems, critical for advancing quantum machine learning and its applications.
The proposed 'coherent swap regret' offers a more comprehensive measure of stability for quantum algorithms, potentially leading to more resilient quantum AI.
- · Quantum AI researchers
- · Quantum computing hardware developers
- · AI ethicists
- · Developers of unstable quantum algorithms
Improved stability measures for quantum machine learning lead to more reliable quantum AI systems.
Enhanced quantum AI accelerates breakthroughs in complex optimization and simulation tasks.
The development of truly 'channel-proof' quantum AI could establish new benchmarks for secure and robust AI across various domains.
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Read at arXiv cs.LG