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Quantum Optimization
Quantum Machine Learning
Multithreaded parallelism for heterogeneous clusters of QPUs
arXiv
Authors: Philipp Seitz, Manuel Geiger, Christian B. Mendl
Year
2023
Paper ID
6478
Status
Preprint
Abstract Read
~2 min
Abstract Words
82
Citations
N/A
Abstract
In this work, we present MILQ, a quantum unrelated parallel machines scheduler and cutter. The setting of unrelated parallel machines considers independent hardware backends, each distinguished by differing setup and processing times. MILQ optimizes the total execution time of a batch of circuits scheduled on multiple quantum devices. It leverages state-of-the-art circuit-cutting techniques to fit circuits onto the devices and schedules them based on a mixed-integer linear program. Our results show a total improvement of up to 26 % compared to a baseline approach.
Why This Paper Matters
- This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
- It adds a 2023 reference point for readers tracking recent quantum research.
- In this work, we present MILQ, a quantum unrelated parallel machines scheduler and cutter.
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