Signals 4 · free daily AI digest

Guiding Agents of Quantum Games to Equilibrium using Matrix Exponential Fixed-Point Iteration

Paper recorded by Signals 4 on 2026-09-18 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-18 on arXiv · recorded by Signals 4 on 2026-09-21

Category: cs.LG · 机器学习 · first seen 2026-09-21

Abstract

In recent years, quantum game theory has gained significant attention as a framework for studying decision-making in multi-agent systems using quantum principles. However, computing equilibrium strategies is challenging because the dimension of the joint Hilbert space grows as the product of the players' local dimensions. In this paper, we consider an extended Gutoski-Watrous (EGW) game in which e

Read on arXiv →

#15 most recent of 235 cs.LG papers we have recorded · ↑ newer: Learning to Move Cities: Deep Meta-Models and Reinforcement Policies f · ↓ older: End-to-End Hard-Label Cryptanalytic Model Extraction Using Efficient S
Cite this page: Guiding Agents of Quantum Games to Equilibrium using Matrix Exponential Fixed-Point Iteration: the #15 most recent of 235 cs.LG papers we have recorded (as of 2026-09-18). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/guiding-agents-of-quantum-games-to-equilibrium-using-matrix-exponential-fixed-po.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
Related: More cs.LG papers · arXiv signals · All papers · Today in AI
Get 4 AI signals a day by email — free.
Subscribe free → See all plans →
Get 4 AI signals a day by email — free
All models · All repos · By company · Daily editions