Solving combinatorial optimization problems efficiently is a major stake in the industry. Thanks to the power of quantum computing, it is nowadays possible to prepare, with implementation on small datasets, the quantum machine learning algorithms that will solve these problems for the next generations of quantum computers.

July 22, 2021






This blog has been authored by Abhirami V S and Vijayaraghavan V from Infosys Ltd.



·         J. Preskill, "Quantum computing in the NISQ era and beyond," Quantum, vol. 2, p. 79, 2018.View at: Publisher Site | Google Scholar

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·         Maria Schuld, Alex Bocharov, Krysta M. Svore, and Nathan Wiebe. Circuit-centric quantum classifiers. Physical Review A, 101(3), mar 2020. DOI: 10.1103/physreva.101.032308

·         Andrea et al., "Transfer learning in hybrid classical-quantum neural networks", Quantum (4), ISSN (2521-327X), http://dx.doi.org/10.22331/q-2020-10-09-340, DOI 0.22331/q-2020-10-09-340.


Posted by Dr. Vijayaraghavan Varadharajan at 5:42 AM | | Comments (0)

January 20, 2021

References

1. Quantum computing for finance: overview and prospects-Roman  Or us, Samuel Mugel and Enrique Lizaso Overview and prospects (researchgate.net)

2. Quantum amplitude estimation algorithms on IBM quantum devices - Pooja Raoa , Kwangmin Yub,*, Hyunkyung Limc , Dasol Jinc , and Deokkyu Choid https://arxiv.org/pdf/2008.02102.pdf

3. Grover's Search Algorithm- Wikipedia https://en.wikipedia.org/wiki/Grover%27s_algorithm

4. Quantum Finance: Credit Risk Analysis with QAE Alice liu https://medium.com/@aliceliu2004/quantum-finance-credit-risk-analysis-with-qae-b339b585aaed

Posted by Dr. Vijayaraghavan Varadharajan at 10:09 AM | | Comments (0)

December 18, 2020