Performance Analysis of Support Vector Machine Implementations on the D-Wave Quantum Annealer-Reference-Cited by-同舟云学术

Performance Analysis of Support Vector Machine Implementations on the D-Wave Quantum Annealer

Published:2021 Issue: Volume: Page:84-97
ISSN:0302-9743
Container-title:Computational Science – ICCS 2021
language:
Short-container-title:

Author:

Bhatia Harshil Singh,Phillipson Frank

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-77980-1_7

Reference27 articles.

1. Bhatia, H.: Support vector machine implementation on d-wave quantum annealer. https://github.com/HarshilBhatia/-Support-Vector-Machine-Implementation-on-D-Wave-Quantum-Annealer

2. Booth, M., Reinhardt, S.P., Roy, A.: Partitioning Optimization Problems for Hybrid Classical/Quantum Execution. Technical report, D-Wave Systems (2017)

3. Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27:1–27:27. software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm (2011)

4. Chatterjee, R., Yu, T.: Generalized coherent states, reproducing kernels, and quantum support vector machines. Quantum Inf. Comput. 17(15–16), 1292–1306 (2017)

5. Choi, V.: Minor-embedding in adiabatic quantum computation: I. the parameter setting problem. Quantum Inf. Process. 7(5), 193–209 (2008)

Cited by 5 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Quantum-Inspired Machine Learning Framework using a Physics-based Ising Solver Chip;2024 IEEE 7th International Conference on Industrial Cyber-Physical Systems (ICPS);2024-05-12

2. Pneumonia detection by binary classification: classical, quantum, and hybrid approaches for support vector machine (SVM);Frontiers in Computer Science;2024-01-05

3. Quantum reinforcement learning;Quantum Information Processing;2023-02-22

4. GPU-accelerated simulations of quantum annealing and the quantum approximate optimization algorithm;Computer Physics Communications;2022-09

5. Garden optimization problems for benchmarking quantum annealers;Quantum Information Processing;2021-09