Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Quantum Speedups for Zero-Sum Games via Improved Dynamic Gibbs Sampling
Authors: Adam Bouland, Yosheb M Getachew, Yujia Jin, Aaron Sidford, Kevin Tian
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We give a quantum algorithm for computing an ϵ-approximate Nash equilibrium of a zero-sum game in an m n payoff matrix with bounded entries. Given a standard quantum oracle for accessing the payoff matrix our algorithm runs in time e O( m + n ϵ 2.5 +ϵ 3) and outputs a classical representation of the ϵ-approximate Nash equilibrium. This improves upon the best prior quantum runtime of e O( m + n ϵ 3) obtained by (van Apeldoorn & Gilyén, 2019) and the classical e O((m + n) ϵ 2) runtime due to (Grigoriadis & Khachiyan, 1995) whenever ϵ = Ω((m + n) 1). We obtain this result by designing new quantum data structures for efficiently sampling from a slowly-changing Gibbs distribution. |
| Researcher Affiliation | Collaboration | 1Stanford University, Stanford, CA, USA. 2Microsoft Research, Redmond, WA, USA. |
| Pseudocode | Yes | Algorithm 1: Matrix Game Solver(δ, η, T) |
| Open Source Code | No | The paper does not contain any statement or link indicating that the code for the described methodology is open-source or publicly available. |
| Open Datasets | No | The paper is theoretical and focuses on algorithm design and proofs; it does not describe empirical evaluation on datasets or mention the use of publicly available datasets for training or evaluation. |
| Dataset Splits | No | As a theoretical paper focused on algorithm design and complexity, it does not discuss training/test/validation dataset splits. |
| Hardware Specification | No | The paper is theoretical and focuses on algorithm design and complexity analysis, not on practical implementation details or experimental results. Therefore, it does not specify any hardware used. |
| Software Dependencies | No | The paper is theoretical and does not describe an implementation or empirical experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments. Therefore, no experimental setup details, such as hyperparameters or training settings, are provided. |