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Quantum Machine Learning

Finding Optimal Training Parameters for Quantum Generative Adversarial Networks

arXiv

Authors: C. Strynar, R. M. Rajapakse

Year

2023

Paper ID

57070

Status

Preprint

Abstract Read

~2 min

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Abstract

Some of the most impressive achievements of contemporary Machine Learning systems comes from the GAN (Generative Adversarial Network) structure. DALLE-2 and GPT- 3, two of the most impressive and recognizable feats of ML in recent years, were both trained using adversarial techniques. The world of Quantum Computing is already well aware of the value of such techniques on near-term Quantum Hardware: QGANs provide a highly efficient method for loading classical data into a quantum state. We investigate the performance of these techniques in an attempt to determine some of the optimal training parameters in a Qiskit-style Parameterized Circuit QGAN framework.

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.
Some of the most impressive achievements of contemporary Machine Learning systems comes from the GAN (Generative Adversarial Network) structure.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2310.19117 [2] arXiv https://arxiv.org/abs/2310.19117 [3] Publisher https://arxiv.org/abs/2310.19117

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