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Quantum Machine Learning
Quantum Machine Learning for Radio Astronomy
arXiv
Authors: Mohammad Kordzanganeh, Aydin Utting, Anna Scaife
Year
2021
Paper ID
40960
Status
Preprint
Abstract Read
~2 min
Abstract Words
83
Citations
N/A
Abstract
In this work we introduce a novel approach to the pulsar classification problem in time-domain radio astronomy using a Born machine, often referred to as a quantum neural network. Using a single-qubit architecture, we show that the pulsar classification problem maps well to the Bloch sphere and that comparable accuracies to more classical machine learning approaches are achievable. We introduce a novel single-qubit encoding for the pulsar data used in this work and show that this performs comparably to a multi-qubit QAOA encoding.
Why This Paper Matters
- This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
- It adds a 2021 reference point for readers tracking recent quantum research.
- In this work we introduce a novel approach to the pulsar classification problem in time-domain radio astronomy using a Born machine, often referred to as a quantum neural network.
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