Novel quantum inspired binary neural network algorithm

Moolchand Sharma, Nebojša Bačanin, Tarik Ahmed Rashid

Om Prakash Patel, Aruna Tiwari

In this paper a novel quantum based binary neural network learning algorithm is proposed. It forms three layer network structure. The proposed method make use of quantum concept for updating and finalizing weights of the neurons and it works for two class problem. The use of quantum concept form an optimized network structure. Also performance in terms of number of neurons and classification accuracy is improved. Same is compared with a quantum-based algorithm for optimizing artificial neural networks algorithm (QANN). It is found that there is improvement in the form of number of neurons at hidden layer, number of iterations, training accuracy and generalization accuracy.

Om Prakash Patel, Aruna Tiwari

In this paper a quantum based binary neural network algorithm is proposed, named as Advance Quantum based Binary Neural Network Learning Algorithm (AQ-BNN). It forms neural network structure constructively by adding neurons at hidden layer. The connection weights and separability parameter are decided using quantum computing concept. Constructive way of deciding network not only eliminates over-fitting and underfitting problem but also saves time. The connection weights have been decided by quantum way, it gives large space to select optimal weights. A new parameter that is quantum separability is introduced here which find optimal separability plane to classify input sample in quantum way. For each connection weights it searches for optimal separability plane. Thus the best separability plane is found out with respect to connection weights. This algorithm is tested with three benchmark data set and produces improved results than existing quantum inspired and other classification approaches.

Om Prakash Patel, Aruna Tiwari

Rahul Kumar Singh, Seyran Saeedi, Zheng Zhang

Binary neural networks (BNNs) are increasingly deployed in edge computing applications due to their low hardware complexity and high energy efficiency. However, verifying the robustness of BNNs against input perturbations, including adversarial attacks, remains computationally challenging because the underlying decision problem is inherently combinatorial. In this paper, we propose an Ising- and quantum-inspired framework for BNN robustness verification. We show that, for a broad class of BNN architectures, robustness verification can be formulated as a Quadratic Constrained Boolean Optimization (QCBO) problem and subsequently transformed into a Quadratic Unconstrained Boolean Optimization (QUBO) instance amenable to Ising and quantum-inspired solvers. We demonstrate the feasibility of this formulation on binarized MNIST by solving the resulting QUBOs with a free energy machine (FEM) solver and simulated annealing. We also show the deployment of this framework on quantum annealing and digital annealing platforms. Our results highlight the potential of quantum-inspired computing and Ising computing as a pathway toward trustworthy AI systems.

Sanjay Gupta, Royce K. P. Zia