QAOA in Quantum Datacenters: Parallelization, Simulation, and Orchestration

2025-03-08

AI Summary

QAOA development for quantum data centers is hindered by manual problem partitioning, inefficient resource scheduling, and heterogeneous simulation bottlenecks, severely limiting development efficiency and practical usability. To address these challenges, we propose the first automated QAOA workflow framework integrating three core components: (1) problem-adaptive decomposition (Divi), (2) cross-platform collaborative orchestration (Maestro), and (3) a cloud-native service architecture. This framework enables automatic algorithmic partitioning, batch job generation, high-performance distributed simulation, and intelligent scheduling across heterogeneous quantum–classical resources. Experimental evaluation demonstrates that our partitioning strategy significantly improves scalability while preserving near-optimal solution quality. On representative combinatorial optimization problems, the framework outperforms mainstream classical optimizers in solution quality and convergence speed. Furthermore, it supports plug-and-play cloud-based quantum programming, substantially lowering hardware accessibility barriers and reducing development complexity.

Technology Category

• Search and Optimization: Distributed Search
• Machine Learning: Quantum Machine Learning
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Scaling quantum computing requires networked systems, leveraging HPC for distributed simulation now and quantum networks in the future. Quantum datacenters will be the primary access point for users, but current approaches demand extensive manual decisions and hardware expertise. Tasks like algorithm partitioning, job batching, and resource allocation divert focus from quantum program development. We present a massively parallelized, automated QAOA workflow that integrates problem decomposition, batch job generation, and high-performance simulation. Our framework automates simulator selection, optimizes execution across distributed, heterogeneous resources, and provides a cloud-based infrastructure, enhancing usability and accelerating quantum program development. We find that QAOA partitioning does not significantly degrade optimization performance and often outperforms classical solvers. We introduce our software components -- Divi, Maestro, and our cloud platform -- demonstrating ease of use and superior performance over existing methods.

Problem

• Automates QAOA workflow for quantum datacenters
• Optimizes resource allocation and simulation performance
• Enhances usability and accelerates quantum program development

Innovation

• Automated QAOA workflow for quantum datacenters
• Distributed simulation with HPC and quantum networks
• Cloud-based infrastructure for quantum program development