Training Multi-layer Neural Networks on Ising Machine

• URL: http://arxiv.org/abs/2311.03408v1
• Date: Mon, 6 Nov 2023 04:09:15 GMT
• Title: Training Multi-layer Neural Networks on Ising Machine
• Authors: Xujie Song, Tong Liu, Shengbo Eben Li, Jingliang Duan, Wenxuan Wang and Keqiang Li
• Abstract summary: This paper proposes an Ising learning algorithm to train quantized neural network (QNN) As far as we know, this is the first algorithm to train multi-layer feedforward networks on Ising machines.
• Score: 41.95720316032297
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As a dedicated quantum device, Ising machines could solve large-scale binary optimization problems in milliseconds. There is emerging interest in utilizing Ising machines to train feedforward neural networks due to the prosperity of generative artificial intelligence. However, existing methods can only train single-layer feedforward networks because of the complex nonlinear network topology. This paper proposes an Ising learning algorithm to train quantized neural network (QNN), by incorporating two essential techinques, namely binary representation of topological network and order reduction of loss function. As far as we know, this is the first algorithm to train multi-layer feedforward networks on Ising machines, providing an alternative to gradient-based backpropagation. Firstly, training QNN is formulated as a quadratic constrained binary optimization (QCBO) problem by representing neuron connection and activation function as equality constraints. All quantized variables are encoded by binary bits based on binary encoding protocol. Secondly, QCBO is converted to a quadratic unconstrained binary optimization (QUBO) problem, that can be efficiently solved on Ising machines. The conversion leverages both penalty function and Rosenberg order reduction, who together eliminate equality constraints and reduce high-order loss function into a quadratic one. With some assumptions, theoretical analysis shows the space complexity of our algorithm is $\mathcal{O}(H^2L + HLN\log H)$, quantifying the required number of Ising spins. Finally, the algorithm effectiveness is validated with a simulated Ising machine on MNIST dataset. After annealing 700 ms, the classification accuracy achieves 98.3%. Among 100 runs, the success probability of finding the optimal solution is 72%. Along with the increasing number of spins on Ising machine, our algorithm has the potential to train deeper neural networks.

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