Logarithmic-Regret Quantum Learning Algorithms for Zero-Sum Games

Release time:2024-11-07 14:56 read:
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Speaker: Dr. Minbo Gao, Institute of Software Chinese, Academy of Sciences

Time: 2024-11-08  15:00-16:00

Venue: Lecture Hall 404, Northeast Building, College of Science


Abstract: We propose the first online quantum algorithm for zero-sum games with O ̃(1)regret under the game setting. Moreover, our quantum algorithm computes an ε-approximate Nash equilibrium of an m×nmatrix zero-sum game in quantum time O ̃(√(m+n)/ε^2.5 ), yielding a quadratic improvement over classical algorithms in terms of m,n. Our algorithm uses standard quantum inputs and generates classical outputs with succinct descriptions, facilitating end-to-end applications. Technically, our online quantum algorithm ‘quantizes’ classical algorithms based on the optimistic multiplicative weight update method. At the heart of our algorithm is a fast quantum multi-sampling procedure for the Gibbs sampling problem, which may be of independent interest.