Hybrid Quantum Machine learning using Quantum Integrated Cloud Architecture (QICA)

TL;DR: In this article , the authors provide an overview of the patent-pending Quantum Integrated Cloud Architecture (QICA) implemented in support of aerospace programs, fielded and deployed to accelerate the pace of research and commercial enterprise adoption of low-bandwidth satellite networks using nanosatellites in a low orbit.

Abstract: Gate-based and Annealing Quantum Computing backend systems purposely built and deployed based on Quantum Integrated Cloud Architecture (QICA) design patterns, provide enhanced quantum computational capabilities that consist of a suite of hybrid quantum-classical cloud-based software and specialized hybrid quantum-classical machine learning algorithms, tools, solvers, and simulators using physics-inspired models and High-Performance Computing circuits simulators. By harnessing the unique properties of quantum mechanics and building on decades of computer science and quantum physics research, our motivation in this paper consists of two folds: (1) to provide an overview of our PATENT-PENDING Quantum Integrated Cloud Architecture (QICA) implemented in support of aerospace programs, fielded and deployed to accelerate the pace of research and commercial enterprise adoption of low-bandwidth satellite networks using nanosatellites in a low orbit. (2) Demonstrate QICA-based computational advantages of Hybrid Quantum-Classical machine learning architecture using IBM Quantum, exploring the base generic quantum neural network (QNN) interfaces provided in Qiskit Machine Learning based on which many agnostic variations of both hybrid classical-quantum and quantum neural networks (QNN) are based on hybrid quantum machine learning.