Quantum Machine Learning Tensor Network States

Frontiers in Physics

Alexey Uvarov¹  Jacob Biamonte¹  Andrey Kardashin² 

[1] Moscow, Russia;null;

关键词: quantum computing;    quantum algorithms and circuits;    tensor network algorithms;    ground state;    properties;    machine learning;    quantum information;   

DOI  :  10.3389/fphy.2020.586374

来源: Frontiers

【 摘 要 】

Tensor network algorithms seek to minimize correlations to compress the classical data representing quantum states. Tensor network algorithms and similar tools—called tensor network methods—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.

【 授权许可】

CC BY