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Quantum Machine Learning
Modeling and Simulating Rydberg Atom Quantum Computers for Hardware-Software Co-design with PachinQo
arXiv
Authors: Jason Zev Ludmir, Yuqian Huo, Nicholas S. DiBrita, Tirthak Patel
Year
2024
Paper ID
6198
Status
Preprint
Abstract Read
~2 min
Abstract Words
106
Citations
N/A
Abstract
Quantum computing has the potential to accelerate various domains: scientific computation, machine learning, and optimization. Recently, Rydberg atom quantum computing has emerged as a promising quantum computing technology, especially with the demonstration of the zonal addressing architecture. However, this demonstration is only compatible with one type of quantum algorithm, and extending it to compile and execute general quantum algorithms is a challenge. To address it, we propose PachinQo, a framework to co-design the architecture and compilation for zonal addressing systems for any given quantum algorithm. PachinQo's evaluation demonstrates its ability to improve a quantum algorithm's estimated probability of success by 45% on average in error-prone quantum environments.
Why This Paper Matters
- This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
- It adds a 2024 reference point for readers tracking recent quantum research.
- Quantum computing has the potential to accelerate various domains: scientific computation, machine learning, and optimization.
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