AI Theory for Quantum Information and Computing

Quantum machine learning (QML) is a rapidly evolving discipline which combines quantum computing and machine learning and applies data-driven strategies to the quantum realm. QML models have the potential to offer speedups and better predictive accuracy compared to their classical counterparts. Sample complexity, even of elementary problems such as classification and hypothesis testing, has not been studied for QML.

Preliminary work has shown that when the same data are embedded into a classical probability distribution, or into probability amplitudes by the Grover-Rudolph embedding, quantum classification performs better. Mathematical quantities which arise in the study of quantum state discrimination are certain families of quantum divergences, which are of fundamental importance in quantum information theory.

We will use tools from quantum information theory to study the sample complexity of quantum binary classification and to evaluate any advantage gained over the classical case. We will subsequently extend this to study the sample complexity of quantum versions of standard ML problems.

Scaling up QML to distributed systems needs the development of quantum communication networks. To this end, we will collaborate with industry partners actively working on quantum AI algorithms.

Linkages between information theory and AI, specifically reinforcement learning, can be used to improve resource allocation in quantum networks, with applications to fusion-based quantum computing.

Quantum communication requires entangled qubits, whose creation is noisy and unreliable. There are a variety of challenges that involve information-theoretic and Markov decision process frameworks to design optimal algorithms.

In this WP, we will investigate the role of information and reinforcement learning in scheduling and optimization of quantum computing systems.