Quantum Machine Learning Tensor Network States

Tensor network algorithms seek to minimize correlations compress the classical data representing quantum states. and similar tools—called tensor methods—form backbone of modern numerical methods used simulate many-body physics have a further range applications in machine learning. Finding contracting states is computational task, which may be accelerated by computing. We present algorithm that returns description rank- r state satisfying an area law approximating eigenvector given black-box access unitary matrix. Our work creates bridge between several contemporary approaches, including networks, variational eigensolver (VQE), approximate optimization (QAOA), computation.