Elivagar: Efficient Quantum Circuit Search for Classification-Reference-Cited by-同舟云学术

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Elivagar: Efficient Quantum Circuit Search for Classification

Published:2024-04-27 Issue: Volume:18 Page:336-353
ISSN:
Container-title:Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2
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Author:

Anagolum Sashwat¹^ORCID,Alavisamani Narges²^ORCID,Das Poulami³^ORCID,Qureshi Moinuddin⁴^ORCID,Shi Yunong⁵^ORCID

Affiliation:

1. Pennsylvania State University, State College, PA, USA

2. Georgia Institute of Technology, Atlanta, GA, USA

3. The University of Texas at Austin, Austin, TX, USA

4. Georgia Institute of Technology, Atlanta, GA, United States of America

5. Amazon Quantum Technologies, New York, New York, United States of America

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3620665.3640354

Reference96 articles.

1. The power of quantum neural networks

2. Bowen Baker, Otkrist Gupta, Nikhil Naik, and Ramesh Raskar. Designing neural network architectures using reinforcement learning. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings, 2017.

3. Time-sliced quantum circuit partitioning for modular architectures

4. Automatic differentiation in machine learning: a survey;Baydin Atilim Gunes;Journal of Machine Learning Research,2018

5. Parameterized quantum circuits as machine learning models

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