We propose several algorithms for learning unitary operators from quantum statistical
queries (QSQs) with respect to their Choi-Jamiolkowski state. Quantum statistical queries …

Learning complex quantum processes is a central challenge in many areas of quantum
computing and quantum machine learning, with applications in quantum benchmarking …

Quantum statistical queries provide a theoretical framework for investigating the
computational power of a learner with limited quantum resources. This model is particularly …

Kearns' statistical query (SQ) oracle (STOC'93) lends a unifying perspective for most
classical machine learning algorithms. This ceases to be true in quantum learning, where …

We consider the problem of testing and learning from data in the presence of resource
constraints, such as limited memory or weak data access, which place limitations on the …

This white paper, developed through close collaboration between IBM Research and UIUC
researchers within the IIDAI Institute, envisions transforming hybrid cloud systems to meet …

Topological data analysis (TDA) is a fast-growing field that utilizes advanced tools from
topology to analyze large-scale data. A central problem in topological data analysis is …

Characterizing a quantum system by learning its state or evolution is a fundamental problem
in quantum physics and learning theory with a myriad of applications. Recently, as a new …

This work studies the learnability of unknown quantum circuits in the near term. We prove
the natural robustness of quantum statistical queries for learning quantum processes and …

Quantum computing, one of the most exciting scientific journeys of our time, holds
remarkable potential by promising to rapidly solve computational problems. However, the …