TY - JOUR
AU - Kardashin, Andrey
AU - Uvarov, Alexey
AU - Biamonte, Jacob
PY - 2021
M3 - Original Research
TI - Quantum Machine Learning Tensor Network States
JO - Frontiers in Physics
UR - https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2020.586374
VL - 8
SN - 2296-424X
N2 - Tensor network algorithms seek to minimize correlations to compress the classical data representing quantum states. Tensor network algorithms and similar toolsâ€”called tensor network methodsâ€”form the backbone of modern numerical methods used to simulate many-body physics and have a further range of applications in machine learning. Finding and contracting tensor network states is a computational task, which may be accelerated by quantum computing. We present a quantum algorithm that returns a classical description of a rank-r tensor network state satisfying an area law and approximating an eigenvector given black-box access to a unitary matrix. Our work creates a bridge between several contemporary approaches, including tensor networks, the variational quantum eigensolver (VQE), quantum approximate optimization algorithm (QAOA), and quantum computation.

ER -