Quantum learning theory is a new and very active area of research at the intersection of
quantum computing and machine learning. Important breakthroughs in the past two years …

Learning about physical systems from quantum-enhanced experiments can outperform
learning from experiments in which only classical memory and processing are available …

Consider an online convex optimization problem where the loss functions are self-
concordant barriers, smooth relative to a convex function $ h $, and possibly non-Lipschitz …

Shadow tomography for quantum states provides a sample efficient approach for predicting
the measurement outcomes of quantum systems. However, these shadow tomography …

Understanding the computational complexity of learning efficient classical programs in
various learning models has been a fundamental and important question in classical …

Quantum state tomography, which aims to find the best description of a quantum state—the
density matrix, is an essential building block in quantum computation and communication …

Online learning quantum states with the logarithmic loss (LL-OLQS) is a quantum
generalization of online portfolio selection, a classic open problem in the field of online …

Among recent insights into learning quantum states, online learning and shadow
tomography procedures are notable for their ability to accurately predict expectation values …

Logistic regression, the Support Vector Machine (SVM), and least squares are well-studied
methods in the statistical and computer science community, with various practical …

Measuring properties of quantum systems is a fundamental problem in quantum mechanics.
We provide a simple method for estimating the expectation value of observables with an …