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You're reading from  A Practical Guide to Quantum Machine Learning and Quantum Optimization

Product typeBook
Published inMar 2023
PublisherPackt
ISBN-139781804613832
Edition1st Edition
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Authors (2):
Elías F. Combarro
Elías F. Combarro
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Elías F. Combarro

Elías F. Combarro holds degrees from the University of Oviedo (Spain) in both Mathematics (1997, award for second highest grades in the country) and Computer Science (2002, award for highest grades in the country). After some research stays at the Novosibirsk State University (Russia), he obtained a Ph.D. in Mathematics (Oviedo, 2001) with a dissertation on the properties of some computable predicates under the supervision of Prof. Andrey Morozov and Prof. Consuelo Martínez. Since 2009, Elías F. Combarro has been an associate professor at the Computer Science Department of the University of Oviedo. He has published more than 50 research papers in international journals on topics such as Computability Theory, Machine Learning, Fuzzy Measures and Computational Algebra. His current research focuses on the application Quantum Computing to algebraic, optimisation and machine learning problems. From July 2020 to January 2021, he was a Cooperation Associate at CERN openlab. Currently, he is the Spain representative in the Advisory Board of CERN Quantum Technology Initiative, a member of the Advisory Board of SheQuantum and one of the founders of the QSpain, a quantum computing think tank based in Spain.
Read more about Elías F. Combarro

Samuel González-Castillo
Samuel González-Castillo
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Samuel González-Castillo

Samuel González-Castillo holds degrees from the University of Oviedo (Spain) in both Mathematics and Physics (2021). He is currently a mathematics research student at the National University of Ireland, Maynooth, where he works as a graduate teaching assistant. He completed his physics bachelor thesis under the supervision of Prof. Elías F. Combarro and Prof. Ignacio F. Rúa (University of Oviedo), and Dr. Sofia Vallecorsa (CERN). In it, he worked alongside other researchers from ETH Zürich on the application of Quantum Machine Learning to classification problems in High Energy Physis. In 2021, he was a summer student at CERN developing a benchmarking framework for quantum simulators. He has contributed to several conferences on quantum computing.
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Summary

In this chapter, you have learned about QAOA, one of the most popular quantum algorithms used to solve optimization problems with gate-based quantum computers. You now know that QAOA is derived as a discretization of quantum annealing and that it is implemented as a hybrid method that uses both a classical and a quantum computer to achieve its goal.

You also understand how to construct circuits for all the operations needed in the quantum part of the algorithm. In particular, you know how to use these circuits to estimate expectation values in an efficient way.

You have also mastered the tools that Qiskit provides in order to implement QAOA instances and to run them on both quantum simulators and quantum computers. You even know how to accelerate the process of running your code on quantum devices by using Qiskit Runtime. And, should you need to use QAOA with PennyLane, you also know how to do it with the help of some predefined utilities and PennyLane capabilities for automatic...

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A Practical Guide to Quantum Machine Learning and Quantum Optimization
Published in: Mar 2023Publisher: PacktISBN-13: 9781804613832

Authors (2)

author image
Elías F. Combarro

Elías F. Combarro holds degrees from the University of Oviedo (Spain) in both Mathematics (1997, award for second highest grades in the country) and Computer Science (2002, award for highest grades in the country). After some research stays at the Novosibirsk State University (Russia), he obtained a Ph.D. in Mathematics (Oviedo, 2001) with a dissertation on the properties of some computable predicates under the supervision of Prof. Andrey Morozov and Prof. Consuelo Martínez. Since 2009, Elías F. Combarro has been an associate professor at the Computer Science Department of the University of Oviedo. He has published more than 50 research papers in international journals on topics such as Computability Theory, Machine Learning, Fuzzy Measures and Computational Algebra. His current research focuses on the application Quantum Computing to algebraic, optimisation and machine learning problems. From July 2020 to January 2021, he was a Cooperation Associate at CERN openlab. Currently, he is the Spain representative in the Advisory Board of CERN Quantum Technology Initiative, a member of the Advisory Board of SheQuantum and one of the founders of the QSpain, a quantum computing think tank based in Spain.
Read more about Elías F. Combarro

author image
Samuel González-Castillo

Samuel González-Castillo holds degrees from the University of Oviedo (Spain) in both Mathematics and Physics (2021). He is currently a mathematics research student at the National University of Ireland, Maynooth, where he works as a graduate teaching assistant. He completed his physics bachelor thesis under the supervision of Prof. Elías F. Combarro and Prof. Ignacio F. Rúa (University of Oviedo), and Dr. Sofia Vallecorsa (CERN). In it, he worked alongside other researchers from ETH Zürich on the application of Quantum Machine Learning to classification problems in High Energy Physis. In 2021, he was a summer student at CERN developing a benchmarking framework for quantum simulators. He has contributed to several conferences on quantum computing.
Read more about Samuel González-Castillo