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Maximum-entropy inference with a programmable annealer

Chancellor, N.; Szoke, S.; Vinci, W.; Aeppli, G.; Warburton, P.A.


S. Szoke

W. Vinci

G. Aeppli

P.A. Warburton


Optimisation problems typically involve finding the ground state (i.e. the minimum energy configuration) of a cost function with respect to many variables. If the variables are corrupted by noise then this maximises the likelihood that the solution is correct. The maximum entropy solution on the other hand takes the form of a Boltzmann distribution over the ground and excited states of the cost function to correct for noise. Here we use a programmable annealer for the information decoding problem which we simulate as a random Ising model in a field. We show experimentally that finite temperature maximum entropy decoding can give slightly better bit-error-rates than the maximum likelihood approach, confirming that useful information can be extracted from the excited states of the annealer. Furthermore we introduce a bit-by-bit analytical method which is agnostic to the specific application and use it to show that the annealer samples from a highly Boltzmann-like distribution. Machines of this kind are therefore candidates for use in a variety of machine learning applications which exploit maximum entropy inference, including language processing and image recognition.


Chancellor, N., Szoke, S., Vinci, W., Aeppli, G., & Warburton, P. (2016). Maximum-entropy inference with a programmable annealer. Scientific Reports, 6(1), Article 22318.

Journal Article Type Article
Acceptance Date Feb 11, 2016
Online Publication Date Mar 3, 2016
Publication Date 2016-03
Deposit Date Mar 6, 2019
Journal Scientific Reports
Publisher Nature Research
Peer Reviewed Peer Reviewed
Volume 6
Issue 1
Article Number 22318