QAOA in Quantum Datacenters: Parallelization, Simulation, and Orchestration
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866913725629333504 |
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| author | Liaqat, Amana Darwish, Ahmed Roman, Adrian DiAdamo, Stephen |
| author_facet | Liaqat, Amana Darwish, Ahmed Roman, Adrian DiAdamo, Stephen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_06233 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | QAOA in Quantum Datacenters: Parallelization, Simulation, and Orchestration Liaqat, Amana Darwish, Ahmed Roman, Adrian DiAdamo, Stephen Quantum Physics Distributed, Parallel, and Cluster Computing 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. |
| title | QAOA in Quantum Datacenters: Parallelization, Simulation, and Orchestration |
| topic | Quantum Physics Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2503.06233 |