Challenges and Future Prospects of Quantum Linear System Algorithm in Quantum Machine Learning

DOI

10.2991/978-94-6463-300-9_33How to use a DOI?

Keywords

Quantum Computing Algorithms; Harrow-Hassidim-Lloyd; Quantum Random Access Memory; Quantum Linear Systems Algorithms

Abstract

Algorithms designed for quantum computing, such as Harrow-Hassidim-Lloyd (HHL), have shown significant potential in solving linear equations, with the possibility of achieving exponential acceleration. However, there are still several areas that need to be further developed and improved. One of the critical challenges is converting classical data into quantum data, a process that is integral to the functioning of quantum computing algorithms. This transformation is often a computationally expensive process, which, if not addressed, could significantly limit the efficiency of quantum computing. Another important area requiring improvement involves the precision of quantum phase estimation (QPE) and amplitude amplification. These processes are vital for the successful execution of quantum algorithms, yet maintaining a high level of accuracy in a quantum environment remains a complex issue. Also, the concept of Quantum Random Access Memory (QRAM) for temporary data storage poses another challenge. While QRAM can theoretically enable efficient access to quantum data, it may lead to the failure of exponential acceleration in certain circumstances, posing a significant limitation to quantum computing algorithms. It is worth noting that the Harrow-Hassidim-Lloyd algorithm’s output does not correspond to the solutions found through classical computations. This discrepancy represents another hurdle in utilizing the HHL algorithm for practical applications. Despite these challenges, Quantum Linear Systems Algorithms (QLSA), an emerging field in quantum computing, shows promise. It’s still in the developmental stages but is already being tested and implemented in certain fields like machine learning and optimization. Despite the limitations of the current quantum computing systems, their potential to revolutionize these fields is indeed exciting.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Jingwen Yu
PY  - 2023
DA  - 2023/11/27
TI  - Challenges and Future Prospects of Quantum Linear System Algorithm in Quantum Machine Learning
BT  - Proceedings of the 2023 International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2023)
PB  - Atlantis Press
SP  - 322
EP  - 328
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-300-9_33
DO  - 10.2991/978-94-6463-300-9_33
ID  - Yu2023
ER  -