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Quantum Computing Meets Artificial Intelligence: Innovations and Challenges

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Handbook on Artificial Intelligence-Empowered Applied Software Engineering

Part of the book series: Artificial Intelligence-Enhanced Software and Systems Engineering ((AISSE,volume 2))

Abstract

With Quantum Computers being more accessible than ever, Artificial Intelligence (AI) researchers are keen to experiment with this computation power with the expectation to take the field to a new level. Exploiting the High dimensional Quantum Hilbert space to efficiently map the data features is the secret to achieve speedup. Furthermore, quantum entanglement is an exclusive feature of the Quantum platform, with a very high computation cost to be simulated classically, which has been experimentally proven to improve classification task accuracy and speed. Quantum Artificial Intelligence Software Engineering has its own challenges. Since the entire solution stack is different than its classical counterpart and the underlying hardware is vulnerable to noise, there is no methodology for producing and sustaining Quantum software as an emerging technology. Besides, an additional error mitigation process should be considered in the software life cycle to correct the defective results. Despite all the limitations of the Noisy Intermediate Scale Quantum (NISQ) hardware, many research projects are currently depending on it to solve problems like slowing down climate change, creating a sustainable business environment, and accelerating drug discovery. In this chapter, quantum-assisted artificial intelligence applications are discussed, highlighting their motivations as well as their challenges.

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Notes

  1. 1.

    Each Quantum Gate can be described by a matrix U, and the only constraint on this matrix is to be unitary : \({U}\dagger {U} = I\), where \({U}\dagger \) is the adjoint of matrix U and I is the identity matrix.

  2. 2.

    Vector notation in Quantum Mechanics that represent the state 0 mathematically.

  3. 3.

    A system’s Hamiltonian is an operator that corresponds to the overall energy of that system, and comprises both kinetic and potential energy.

  4. 4.

    A Hilbert space is a space with a finite or infinite number of dimensions that extends vector algebra and calculus methods from the two-dimensional Euclidean plan and three-dimensional space.

  5. 5.

    Hedging is a risk management approach that involves taking an opposite role in a similar asset to offset investment risks [67].

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Acknowledgements

We would like to express our gratitude to Dr. Sherif Saif and Dr. Wael S. Afifi for their insightful suggestions that helped to improve this work.

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Authors and Affiliations

  1. Computer and Systems Department, Electronics Research Institute, Cairo, Egypt

    Maha A. Metawei, Hesham Eldeeb & Salwa M. Nassar

  2. Computer and Systems Engineering Department, Faculty of Engineering, Ain Shams University, Cairo, Egypt

    Mohamed Taher

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  1. Maha A. Metawei
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  2. Hesham Eldeeb
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  3. Salwa M. Nassar
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Correspondence to Maha A. Metawei .

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Editors and Affiliations

  1. Department of Informatics, University of Piraeus, Piraeus, Greece

    Maria Virvou

  2. Department of Informatics, University of Piraeus, Piraeus, Greece

    George A. Tsihrintzis

  3. College of Engineering and Computer Science, Wright State University, Joshi Research, Dayton, OH, USA

    Nikolaos G. Bourbakis

  4. KES International, Shoreham-by-Sea, UK

    Lakhmi C. Jain

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Metawei, M.A., Eldeeb, H., Nassar, S.M., Taher, M. (2022). Quantum Computing Meets Artificial Intelligence: Innovations and Challenges. In: Virvou, M., Tsihrintzis, G.A., Bourbakis, N.G., Jain, L.C. (eds) Handbook on Artificial Intelligence-Empowered Applied Software Engineering. Artificial Intelligence-Enhanced Software and Systems Engineering, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-031-08202-3_12

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