arXiv:1712.05304quant-ph
A quantum algorithm to train neural networks using low-depth circuits
Early discovery. Linked by one identifiable commenter. This paper is outside the main ranking; a mention is not an endorsement.
Abstract
Can near-term gate model based quantum processors offer quantum advantage for practical applications in the pre-fault tolerance noise regime? A class of algorithms which have shown some promise in this regard are the so-called classical-quantum hybrid variational algorithms. Here we develop a low-depth quantum algorithm to generative neural networks using variational quantum circuits. We introduce a method which employs the quantum approximate optimization algorithm as a subroutine in order produce then sample low-energy distributions of Ising Hamiltonians. We sample these states to train neural networks and demonstrate training convergence for numerically simulated noisy circuits with depolarizing errors of rates of up to $4\%$.
There has been some effort to use quantum computers to solve machine learning problems. One for training neural networks [0]; and another for unsupervised machine learning [1]. [0]: https://arxiv.org/pdf/1712.05304.pdf [1]: https://arxiv.org/pdf/1712.05771.pdf
vtomoleView on HNParent context