2023/06/07 by Alexander Nietner, Nietner, Alexander · 1 citation
Computer Science · Materials Science · Mathematics · #Computational Complexity (cs.CC) #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning and Algorithms #Machine Learning in Materials Science #Quantum Physics (quant-ph) #Statistical Methods and Inference
paper · pdf · doi:10.48550/arxiv.2306.04731
openalex publication_date 2023/06/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
Free fermions are some of the best studied quantum systems. However, little is known about the complexity of learning free-fermion distributions. In this work we establish the hardness of this task in the particle number non-preserving case. In particular, we give an information theoretical hardness result for the general task of learning from expectation values and, in the more general case when the algorithm is given access to samples, we give a computational hardness result based on the LPN assumption for learning the probability density function.