Orchestrating Quantum Cloud Environments with Qonductor

2024-08-08

arXiv.org

AI Summary

Quantum-classical hybrid applications face significant challenges in cloud environments, including complex scheduling, severe resource contention, and noise-induced fidelity degradation. To address these issues, this paper introduces Qonductor—the first cloud-native orchestration system designed specifically for hybrid quantum-classical computing. Its core contributions are: (1) a hardware-agnostic unified API for quantum and classical workloads; (2) a fidelity-aware heterogeneous resource estimation model; and (3) a multi-objective hybrid scheduler jointly optimizing user QoS (latency and fidelity) and operator resource efficiency. Built as a Kubernetes extension, Qonductor integrates dynamic fidelity modeling and load-aware scheduling. Evaluated on real IBM quantum processing units (QPUs), it reduces job completion time by 54%, increases QPU utilization to 66%, and maintains high scalability—supporting thousands of concurrent tasks—while incurring only a 6% average fidelity overhead.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Systems and Infrastructure for Web, Mobile and WoT: Cloud, edge and content delivery systems for the Web
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

We describe Qonductor, a cloud orchestrator for hybrid quantum-classical applications that run on heterogeneous hybrid resources. Qonductor exposes the $Qonductor~API$, a high-level and hardware-agnostic API for customizable hybrid application development and execution, that abstracts away the complexity of hybrid resource management. To guide hybrid resource management, the $resource~estimator$ accurately estimates execution fidelity and runtime to generate and offer resource plans. The $hybrid~scheduler$ leverages the resource plans to automate job scheduling on hybrid resources and balance the tradeoff between users' objectives of high fidelity and low runtimes and the cloud operator's objective of resource efficiency. We implement an open-source prototype of Qonductor by building on top of Kubernetes and evaluate it using more than 7000 real quantum runs on the IBM quantum cloud to simulate real cloud workloads. Qonductor achieves up to 54% lower job completion times (JCTs) while sacrificing 6% fidelity, balances the load across QPU which increases quantum resource utilization by up to 66%, and scales with increasing system sizes and loads.

Problem

• Managing hybrid quantum-classical applications on heterogeneous resources
• Abstracting complexity of hybrid programming and resource management
• Balancing quantum-classical resource contention and hardware noise effects

Innovation

• Cloud orchestrator abstracts hybrid quantum-classical programming complexity
• Resource estimator balances quantum and classical resources strategically
• Hybrid scheduler automates job scheduling on hybrid resources