Noncommutative Bohnenblust–Hille inequalities

Author:

Volberg Alexander,Zhang HaonanORCID

Abstract

AbstractBohnenblust–Hille inequalities for Boolean cubes have been proven with dimension-free constants that grow subexponentially in the degree (Defant et al. in Math Ann 374(1):653–680, 2019). Such inequalities have found great applications in learning low-degree Boolean functions (Eskenazis and Ivanisvili in Proceedings of the 54th annual ACM SIGACT symposium on theory of computing, pp 203–207, 2022). Motivated by learning quantum observables, a qubit analogue of Bohnenblust–Hille inequality for Boolean cubes was recently conjectured in Rouzé et al. (Quantum Talagrand, KKL and Friedgut’s theorems and the learnability of quantum Boolean functions, 2022. arXiv preprintarXiv:2209.07279). The conjecture was resolved in Huang et al. (Learning to predict arbitrary quantum processes, 2022. arXiv preprintarXiv:2210.14894). In this paper, we give a new proof of these Bohnenblust–Hille inequalities for qubit system with constants that are dimension-free and of exponential growth in the degree. As a consequence, we obtain a junta theorem for low-degree polynomials. Using similar ideas, we also study learning problems of low degree quantum observables and Bohr’s radius phenomenon on quantum Boolean cubes.

Funder

Division of Mathematical Sciences

Division of Materials Sciences and Engineering

Hausdorff Center for Mathematics

Austrian Science Fund

Publisher

Springer Science and Business Media LLC

Subject

General Mathematics

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. On the Pauli Spectrum of QAC0;Proceedings of the 56th Annual ACM Symposium on Theory of Computing;2024-06-10

2. Asymptotic Insights for Projection, Gordon–Lewis, and Sidon Constants in Boolean Cube Function Spaces;International Mathematics Research Notices;2024-06-05

3. Quantum Talagrand, KKL and Friedgut’s Theorems and the Learnability of Quantum Boolean Functions;Communications in Mathematical Physics;2024-04

4. Sub-exponential ML Algorithm for Predicting Ground State Properties;Computational Science – ICCS 2023;2023

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