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arXiv · 1712.05304

A quantum algorithm to train neural networks using low-depth circuits

Guillaume Verdon·Michael Broughton·Jacob Biamonte
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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 $4\%$.

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Guillaume Verdon, Michael Broughton, Jacob Biamonte. 2017-12-14. A quantum algorithm to train neural networks using low-depth circuits. https://arxiv.org/abs/1712.05304

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