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Automatic Optimization of Variational Quantum Algorithm-Based Classifiers

Published: 16 May 2023 Publication History

Abstract

With the advent of the era of big data, how to efficiently process data has become an urgent problem. Although classical machine learning algorithms are very mature in processing data, they require a large amount of data to complete the training process. Quantum machine learning uses the superposition, entanglement, parallelism and other characteristics of quantum computing to improve the performance of classical machine learning algorithms. At the same time, circuit optimization of the quantum classifier can reduce the computational complexity. This paper proposes an automatic optimization model of the classifier based on the variational quantum algorithm, which reduces the complexity of the parameter update process through the deep optimization of the core circuit. This model greatly speeds up the training process and has lower complexity than other quantum classifiers. In addition, a unique regularization method is used to avoid overfitting. The simulation results show that the classification of some typical data sets can achieve better classification results, and the training parameters and training speed are significantly better than other quantum classifiers. Finally, the robustness of the classifier is further verified in the presence of noise.

References

[1]
Jordan M I, Mitchell T M. Machine learning: Trends, perspectives, and prospects[J]. Science, 2015, 349(6245): 255-260.
[2]
Schuld M, Sinayskiy I, Petruccione F. An introduction to quantum machine learning[J]. Contemporary Physics, 2015, 56(2): 172-185.
[3]
Biamonte J, Wittek P, Pancotti N, Quantum machine learning[J]. Nature, 2017, 549(7671): 195-202.
[4]
Lloyd S, Mohseni M, Rebentrost P. Quantum algorithms for supervised and unsupervised machine learning[J]. arXiv preprint arXiv:1307.0411, 2013.
[5]
Guţă M, Kotłowski W. Quantum learning: asymptotically optimal classification of qubit states[J]. New Journal of Physics, 2010, 12(12): 123032.
[6]
Busemeyer J R, Bruza P D. Quantum models of cognition and decision[M]. Cambridge University Press, 2012.
[7]
Schuld M, Fingerhuth M, Petruccione F. Implementing a distance-based classifier with a quantum interference circuit[J]. EPL (Europhysics Letters), 2017, 119(6): 60002.
[8]
Farhi E, Neven H. Classification with quantum neural networks on near term processors[J]. arXiv preprint arXiv:1802.06002, 2018.
[9]
Schuld M, Bocharov A, Svore K M, Circuit-centric quantum classifiers[J]. Physical Review A, 2020, 101(3): 032308.
[10]
Amin J, Sharif M, Gul N, Quantum machine learning architecture for COVID-19 classification based on synthetic data generation using conditional adversarial neural network[J]. Cognitive Computation, 2021: 1-12.
[11]
Amy M, Maslov D, Mosca M, A meet-in-the-middle algorithm for fast synthesis of depth-optimal quantum circuits[J]. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2013, 32(6): 818-830.
[12]
Nam Y, Ross N J, Su Y, Automated optimization of large quantum circuits with continuous parameters[J]. npj Quantum Information, 2018, 4(1): 1-12.
[13]
Fösel T, Niu M Y, Marquardt F, Quantum circuit optimization with deep reinforcement learning[J]. arXiv preprint arXiv:2103.07585, 2021.
[14]
Schuld M. Quantum machine learning models are kernel methods[J]. arXiv e-prints, 2021: arXiv: 2101.11020.
[15]
LaRose R, Coyle B. Robust data encodings for quantum classifiers[J]. Physical Review A, 2020, 102(3): 032420.
[16]
Huang H Y, Broughton M, Mohseni M, Power of data in quantum machine learning[J]. Nature Communications, 2021, 12(1).
[17]
Schuld M, Petruccione F. Quantum ensembles of quantum classifiers[J]. Scientific reports, 2018, 8(1): 1-12.
[18]
Debnath S, Linke N M, Figgatt C, Demonstration of a small programmable quantum computer with atomic qubits[J]. Nature, 2016, 536(7614): 63-66.
[19]
Adhikary S, Dangwal S, Bhowmik D. Supervised learning with a quantum classifier using multi-level systems[J]. Quantum Information Processing, 2020, 19(3): 1-12.

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  • (2025)QRLaXAI: quantum representation learning and explainable AIQuantum Machine Intelligence10.1007/s42484-025-00253-97:1Online publication date: 19-Feb-2025

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  1. Automatic Optimization of Variational Quantum Algorithm-Based Classifiers

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    cover image ACM Other conferences
    AIPR '22: Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition
    September 2022
    1221 pages
    ISBN:9781450396899
    DOI:10.1145/3573942
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 16 May 2023

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    Author Tags

    1. Automatic optimization
    2. Machine learning
    3. Quantum classifiers
    4. Quantum computing

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    • Natural Science Basic Research Program of Shaanxi

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    • (2025)QRLaXAI: quantum representation learning and explainable AIQuantum Machine Intelligence10.1007/s42484-025-00253-97:1Online publication date: 19-Feb-2025

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