Tensor networks for quantum machine learning
This work addresses the challenge of leveraging tensor networks for quantum machine learning, which is incremental as it builds on existing paradigms to enhance quantum computing applications.
The paper reviews how tensor networks, originally from quantum theory and later adapted for machine learning, can be applied to quantum machine learning to tackle problems that classical computers cannot solve efficiently, focusing on mapping architectures like MPS, PEPS, TTNs, and MERA to quantum computers for tasks such as data encoding and performance improvement.
Once developed for quantum theory, tensor networks have been established as a successful machine learning paradigm. Now, they have been ported back to the quantum realm in the emerging field of quantum machine learning to assess problems that classical computers are unable to solve efficiently. Their nature at the interface between physics and machine learning makes tensor networks easily deployable on quantum computers. In this review article, we shed light on one of the major architectures considered to be predestined for variational quantum machine learning. In particular, we discuss how layouts like MPS, PEPS, TTNs and MERA can be mapped to a quantum computer, how they can be used for machine learning and data encoding and which implementation techniques improve their performance.