Application of Quantum Annealing to Training of Deep Neural Networks
Abstract
In Deep Learning, a well-known approach for training a Deep Neural Network starts by training a generative Deep Belief Network model, typically using Contrastive Divergence (CD), then fine-tuning the weights using backpropagation or other discriminative techniques. However, the generative training can be time-consuming due to the slow mixing of Gibbs sampling. We investigated an alternative approach that estimates model expectations of Restricted Boltzmann Machines using samples from a D-Wave quantum annealing machine. We tested this method on a coarse-grained version of the MNIST data set. In our tests we found that the quantum sampling-based training approach achieves comparable or better accuracy with significantly fewer iterations of generative training than conventional CD-based training. Further investigation is needed to determine whether similar improvements can be achieved for other data sets, and to what extent these improvements can be attributed to quantum effects.
- Publication:
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arXiv e-prints
- Pub Date:
- October 2015
- DOI:
- 10.48550/arXiv.1510.06356
- arXiv:
- arXiv:1510.06356
- Bibcode:
- 2015arXiv151006356A
- Keywords:
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- Quantum Physics;
- Computer Science - Machine Learning;
- Statistics - Machine Learning
- E-Print:
- 18 pages