Online Learning Quantum States with the Logarithmic Loss via VB-FTRL

• URL: http://arxiv.org/abs/2311.04237v2
• Date: Mon, 14 Oct 2024 06:44:47 GMT
• Title: Online Learning Quantum States with the Logarithmic Loss via VB-FTRL
• Authors: Wei-Fu Tseng, Kai-Chun Chen, Zi-Hong Xiao, Yen-Huan Li,
• Abstract summary: Online learning of quantum states with the logarithmic loss (LL-OLQS) is a classic open problem in online learning for over three decades. In this paper, we generalize VB-FTRL for LL-OLQS with moderate computational complexity. Each of the algorithm consists of a semidefinite program that can be implemented in time by, for example, cutting-plane methods.
• Score: 1.8856444568755568
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• Abstract: Online learning of quantum states with the logarithmic loss (LL-OLQS) is a quantum generalization of online portfolio selection (OPS), a classic open problem in online learning for over three decades. This problem also emerges in designing stochastic optimization algorithms for maximum-likelihood quantum state tomography. Recently, Jezequel et al. (arXiv:2209.13932) proposed the VB-FTRL algorithm, the first regret-optimal algorithm for OPS with moderate computational complexity. In this paper, we generalize VB-FTRL for LL-OLQS. Let $d$ denote the dimension and $T$ the number of rounds. The generalized algorithm achieves a regret rate of $O ( d^2 \log ( d + T ) )$ for LL-OLQS. Each iteration of the algorithm consists of solving a semidefinite program that can be implemented in polynomial time by, for example, cutting-plane methods. For comparison, the best-known regret rate for LL-OLQS is currently $O ( d^2 \log T )$, achieved by an exponential weight method. However, no explicit implementation is available for the exponential weight method for LL-OLQS. To facilitate the generalization, we introduce the notion of VB-convexity. VB-convexity is a sufficient condition for the volumetric barrier associated with any function to be convex and is of independent interest.

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