Tensor Ring Optimized Quantum-Enhanced Tensor Neural Networks
Debanjan Konar Dheeraj Peddireddy Vaneet Aggarwal Bijaya K. Panigrahi
Quantum machine learning researchers often rely on incorporating Tensor
Networks (TN) into Deep Neural Networks (DNN) and variational optimization.
However, the standard optimization techniques used for training the contracted
trainable weights of each model layer suffer from the correlations and
entanglement structure between the model parameters on classical
implementations. To address this issue, a multi-layer design of a Tensor Ring
optimized variational Quantum learning classifier (Quan-TR) comprising
cascading entangling gates replacing the fully connected (dense) layers of a TN
is proposed, and it is referred to as Tensor Ring optimized Quantum-enhanced
tensor neural Networks (TR-QNet). TR-QNet parameters are optimized through the
stochastic gradient descent algorithm on qubit measurements. The proposed
TR-QNet is assessed on three distinct datasets, namely Iris, MNIST, and
CIFAR-10, to demonstrate the enhanced precision achieved for binary
classification. On quantum simulations, the proposed TR-QNet achieves promising
accuracy of $94.5\%$, $86.16\%$, and $83.54\%$ on the Iris, MNIST, and CIFAR-10
datasets, respectively. Benchmark studies have been conducted on
state-of-the-art quantum and classical implementations of TN models to show the
efficacy of the proposed TR-QNet. Moreover, the scalability of TR-QNet
highlights its potential for exhibiting in deep learning applications on a
large scale. The PyTorch implementation of TR-QNet is available on
Github:https://github.com/konar1987/TR-QNet/