CTRQNets & LQNets: Continuous Time Recurrent and Liquid Quantum Neural Networks
This addresses the problem of inflexibility in quantum neural networks for researchers in quantum machine learning, though it appears incremental as it builds on existing QNN frameworks.
The authors tackled the static construction and lack of dynamic intelligence in quantum neural networks by developing Liquid Quantum Neural Networks (LQNets) and Continuous Time Recurrent Quantum Neural Networks (CTRQNets), which achieved accuracy increases of up to 40% on CIFAR-10 binary classification compared to existing QNNs.
Neural networks have continued to gain prevalence in the modern era for their ability to model complex data through pattern recognition and behavior remodeling. However, the static construction of traditional neural networks inhibits dynamic intelligence. This makes them inflexible to temporal changes in data and unfit to capture complex dependencies. With the advent of quantum technology, there has been significant progress in creating quantum algorithms. In recent years, researchers have developed quantum neural networks that leverage the capabilities of qubits to outperform classical networks. However, their current formulation exhibits a static construction limiting the system's dynamic intelligence. To address these weaknesses, we develop a Liquid Quantum Neural Network (LQNet) and a Continuous Time Recurrent Quantum Neural Network (CTRQNet). Both models demonstrate a significant improvement in accuracy compared to existing quantum neural networks (QNNs), achieving accuracy increases as high as 40\% on CIFAR 10 through binary classification. We propose LQNets and CTRQNets might shine a light on quantum machine learning's black box.