Choreography and Profiling of Quantum-Classical FaaS Workflows on Hybrid Clouds

Quantum computing is entering the mainstream as part of cloud offerings, where it serves as a special-purpose accelerator in larger applications. However, it is still challenging for developers and researchers to design, build and manage the resources for Hybrid Quantum-Classical (HQC) applications that include both classical logic (x86, ARM) and quantum circuit blocks, and run across both traditional and quantum processors. Further, quantum hardware is available on public cloud and even on-premise as private clouds, with varying capabilities, costs and queue times. Further, such quantum circuits also expose optimization methods that offer cost, time, accuracy and parallelism trade-offs. So, there is a compelling for easy composition of HQC applications that can be effortlessly and efficiently deployed on hybrid clouds. In this paper, we propose a framework to intuitively compose and deploy “zero-touch” quantum-classical Function-as-a-Service (FaaS) workflows through various workflow patterns that leverage diverse cloud system (workflow partitioning, adaptive polling) and quantum (circuit cutting, qubit reuse) optimizations. These utilize our XFaaS FaaS workflow framework for hybrid cloud deployments on AWS and Azure, and IBM Qiskit SDK for the quantum circuit toolchain. We also offer detailed experimental profiling of these optimizations for realistic and synthetic HQC applications on real clouds, and on real and simulated quantum hardware, and analyze the benefits of cloud system and quantum circuit optimizations. Our results demonstrate up to 53 % improvement in time and 80 % in cost when quantum circuit optimization on hardware is used in conjunction with dynamic fan-out.