GR-288 Comparative performance analysis of hybrid quantum machine learning algorithm to assess Post stroke rehabilitation exercises

GR-288 Comparative performance analysis of hybrid quantum machine learning algorithm to assess Post stroke rehabilitation exercises

Due to the advancements in technology, data is growing exponentially. With this increased dataset size, the computation to process the generated information is rising sequentially. And the currently available classical computational tools and learning algorithms will not work due to the limitations of Moore's law. To overcome the computational issues, we have to switch to Quantum Computing which works based on the laws of Quantum Mechanics. Quantum Machine Learning (QML), a subset of Quantum Computing, is faster and more capable of doing complex calculations that a classical computer can't. Classical Computers work on bits - 0 or 1, whereas a Quantum Bit (also known as a qubit) works on the superposition principle and can be 0 and 1 at the same time before it is measured. Other properties known as Quantum Entanglement, Quantum Parallelism, etc., also will help in understanding the other qubit state and parallel processing the data. In this paper, we introduce hybrid quantum and convolutional models built using PennyLane on the UI-PRMD dataset for the Kinect sensor. By involving quantum layers in a traditional network, a better performance can be achieved compared with the traditional neural network performance.